One-touch fingertip sweat sensor and personalized data processing for reliable prediction of blood biomarker concentrations

Abstract

Disclosed are methods, systems, and devices for collecting and transferring naturally produced sweat containing an analyte to a biosensor and / or a biofuel cell to estimate a concentration of the analyte corresponding to a concentration of the analyte in blood and / or to generate electricity. In some embodiments, the device includes a substrate, a plurality of electrodes disposed on the substrate and operable to detect an analyte in an individual's naturally produced sweat, and a sweat-permeable layer including a hydrogel, the sweat-permeable layer being configured to contact the plurality of electrodes and to transfer the sweat containing the analyte through the sweat-permeable layer to reach the plurality of electrodes for detection and / or energy collection.

Description

[Technical field]

[0001] <Cross-reference to priority and related applications> This patent document claims priority to and the benefit of U.S. Provisional Application No. 63 / 146,359, filed February 5, 2021, and U.S. Provisional Application No. 63 / 182,579, filed April 30, 2021. The disclosures of the above patent applications are incorporated by reference as part of the disclosure of this document.

[0002] This patent document relates to electrochemical sensors. [Background technology]

[0003] The prevalence of diabetes is rising exponentially, increasing the need for reliable non-invasive approaches to glucose monitoring. Various biological fluids have been explored in recent years to replace the current blood fingertip glucose strips with non-invasive painless sensing devices. Sweat has received considerable attention, and there are various reports on correlating the results of sweat-based analysis with blood glucose levels. Thus, there remains a need to provide simple, inexpensive, and reliable devices and methods for reliable non-invasive measurement of blood glucose as well as other biomarkers based on sweat analysis. Summary of the Invention

[0004] The technology disclosed in this patent document relates to methods and devices for collecting analytes in sweat to estimate the concentration of the analytes in blood, or for generating electricity using the redox reactions of analytes in sweat.

[0005] In some embodiments, the disclosed technology can be implemented to provide a device having: a substrate; a plurality of electrodes disposed on the substrate and operable to detect an analyte in the individual's sweat; a sweat-permeable layer including a hydrogel and having a first side and a second side opposite the first side. The first side of the sweat-permeable layer is in contact with the plurality of electrodes, such that the plurality of electrodes are disposed between the substrate and the first side of the sweat-permeable layer. The sweat-permeable layer is configured to transfer naturally produced sweat, including the analyte, from the individual's fingertip by transmitting the naturally produced sweat through the sweat-permeable layer from the second side to the first side to reach the plurality of electrodes.

[0006] In some embodiments, the disclosed technology can be implemented to provide a device having: a piezoelectric chip; two or more electrodes having an anode electrode and a cathode electrode formed on the piezoelectric chip, the two or more electrodes operable to detect an electrical signal associated with a chemical reaction associated with an analyte contained in the individual's sweat entering a surface area of ​​the anode electrode and the cathode electrode; a current collector having two or more conductive material structures, the conductive material structures being disposed between the piezoelectric chip and the two or more electrodes, electrically coupling at least one of the conductive material structures to the anode electrode and electrically coupling at least another one of the conductive material structures to the cathode electrode; and a sweat-permeable layer including a hydrogel and having a first side and a second side opposite the first side. The first side of the sweat-permeable layer is in contact with the two or more electrodes and is configured to transport sweat naturally generated from the individual's fingertip by being pushed by the individual's fingertip from the second side through the sweat-permeable layer toward the first side and to reach a surface area of ​​the two or more electrodes. The piezoelectric chip undergoes a non-destructive mechanical deformation when the second side of the sweat-permeable layer is pressed with the individual's fingertip, and generates electrical energy from the non-destructive mechanical deformation of the piezoelectric chip.

[0007] In some embodiments, the disclosed technology can be implemented to provide a method for determining a concentration of an analyte present in at least one of an individual's blood, sweat, or interstitial fluid (ISF), comprising: acquiring with the device a sweat sample deposited on a sweat-permeable layer of the device from the individual's finger or other sweat gland-covered skin surface; acquiring a plurality of measurements of the level of the analyte using a signal from the device; acquiring a measurement of the level of the analyte for each of the plurality of measurements of the level of the analyte; acquiring linear slope and intercept parameters for the dependence between the acquired measurements of the concentration of the analyte in the individual's blood and the acquired measurements of the analyte level in the individual's sweat; and converting the new measurements of the analyte level in the individual's sweat into an estimate of the analyte concentration in the individual's blood using the linear slope and intercept parameters.

[0008] In some embodiments, the disclosed technology can be implemented to provide a method for determining a concentration of an analyte present in at least one of an individual's blood, sweat, or interstitial fluid (ISF), the method comprising: obtaining, by the device, a sweat sample deposited from the individual's finger onto a sweat-permeable layer of the device; acquiring a plurality of measurements of the level of the analyte using a signal from the device; acquiring, for each of the plurality of measurements of the level of the analyte, a measurement of the level of the analyte in the individual's blood; acquiring an exponent, a multiplier, and an intercept parameter for the dependence between the acquired measurements of the analyte concentration in the individual's blood and the acquired measurements of the analyte level in the individual's sweat; converting the new measurements of the analyte level in the individual's sweat into an estimate of the analyte concentration in the individual's blood using the exponent, multiplier, and intercept parameters.

[0009] In some embodiments, the disclosed technology can be implemented to provide a method for determining the concentration of an analyte in an individual's sweat, comprising: acquiring with the device a sweat sample deposited from the individual's finger onto a sweat-permeable layer of the device; acquiring a plurality of groups of measurements of the level of the analyte in the individual's sweat using a signal from the device; acquiring, for each group of measurements of the level of the analyte in the individual's sweat, a corresponding group of measurements of the analyte concentration in the individual's blood; acquiring, for each group of measurements of the level of the analyte in the individual's sweat, respective values ​​of a linear slope parameter and an intercept parameter relating to the dependence between the measurements in the group and the measurements in the corresponding group of measurements of the analyte concentration in the individual's blood; determining an average value of the linear slope parameter and an average value of the intercept parameter for the group of measurements of the analyte level in the individual's sweat; determining the concentration of the analyte in the individual's blood based on the determined average value of the linear slope parameter and the determined average value of the intercept parameter.

[0010] In some embodiments, the disclosed technology can be implemented to provide a method for generating electrical power using sweat analytes, the method comprising: placing a device on a skin surface having sweat glands to collect sweat analytes for biocatalytic reactions in a plurality of electrodes and generating electrical current from the plurality of electrodes of the device, where sweat is collected by the device from a finger of skin covered with sweat glands through a sweat-permeable layer of the device; applying pressure to the device against the skin via finger pressure sporadically to generate electrical current from the plurality of electrodes; collecting electrical power in a storage unit, either directly in the porous electrodes of the device or through a voltage regulation circuit.

[0011] In some embodiments, the disclosed technology can be implemented to provide a method for determining a concentration of a biofluid analyte in an individual, comprising: obtaining, by the device, a sweat sample deposited from the individual's finger onto a sweat-permeable layer of the device; obtaining a plurality of measured levels of the biofluid analyte in the individual's sweat using a self-generated signal or open circuit voltage from the device; obtaining, for each of the plurality of measured levels of the biofluid analyte in the individual's sweat, a voltage signal without the external action of a constant voltage or current by discharging through a resistive load between the anode and cathode of the plurality of electrodes; discharging, from a biofuel cell of the device, a regulated power or power stored in the power electronics that obtains a signal from the plurality of electrodes, for each of the plurality of measured levels of the biofluid analyte in the individual's sweat.

[0012] These and other aspects and implementations of the disclosed technology are described in more detail in the drawings, description, and claims. [Brief description of the drawings]

[0013] [Figure 1A] 1 shows an example of a blood-free fingertip sweat glucose sensor according to some embodiments of the disclosed technology. [Figure 1B] 1 shows an example of a blood-free fingertip sweat glucose sensor according to some embodiments of the disclosed technology. [Figure 1C] 1 shows an example of a blood-free fingertip sweat glucose sensor according to some embodiments of the disclosed technology. [Figure 1D] 1 shows an example of a blood-free fingertip sweat glucose sensor according to some embodiments of the disclosed technology. [Figure 1E] 1 shows an example of a blood-free fingertip sweat glucose sensor according to some embodiments of the disclosed technology. [Figure 1F] 1 shows an example of a blood-free fingertip sweat glucose sensor according to some embodiments of the disclosed technology.

[0014] [Figure 2A] 1 shows an example of a data processing protocol for personalized transformation equations. [Figure 2B] 1 shows an example of a data processing protocol for personalized transformation equations. [Figure 2C] 1 shows an example of a data processing protocol for personalized transformation equations. [Figure 2D] 1 shows an example of a data processing protocol for personalized transformation equations.

[0015] [Figure 3A] 13 shows an example of the effect of different data conversion steps in converting the current response of a sweat glucose sensor to a sweat-based blood glucose concentration. [Figure 3B] 13 shows an example of the effect of different data conversion steps in converting the current response of a sweat glucose sensor to a sweat-based blood glucose concentration. [Figure 3C] 13 shows an example of the effect of different data conversion steps in converting the current response of a sweat glucose sensor to a sweat-based blood glucose concentration. [Figure 3E] 13 shows an example of the effect of different data conversion steps in converting the current response of a sweat glucose sensor to a sweat-based blood glucose concentration. [Figure 3F] 13 shows an example of the effect of different data conversion steps in converting the current response of a sweat glucose sensor to a sweat-based blood glucose concentration.

[0016] [Figure 4A] Examples of measurement results obtained for all-day sweat glucose measurements are shown together with the corresponding blood measurements. [Figure 4B] Examples of measurement results obtained for all-day sweat glucose measurements are shown together with the corresponding blood measurements. [Figure 4C] Examples of measurement results obtained for all-day sweat glucose measurements are shown together with the corresponding blood measurements.

[0017] [Figure 5A]1 illustrates an exemplary principle of operation of a fingertip levodopa biosensor according to some embodiments of the disclosed technology. [Figure 5B] 1 illustrates an exemplary principle of operation of a fingertip levodopa biosensor according to some embodiments of the disclosed technology.

[0018] [Figure 6A] 1 shows an exemplary timeline tracked during levodopa monitoring in sweat. [Figure 6B] 1 shows an exemplary timeline tracked during levodopa monitoring in sweat. [Figure 6C] 1 shows an exemplary timeline tracked during levodopa monitoring in sweat.

[0019] [Figure 7] 1 shows exemplary data collected during an exemplary on-body demonstration of a levodopa biosensor.

[0020] [Figure 8] 1 shows an exemplary comparison of levodopa profiles obtained by levodopa measurements in blood and sweat.

[0021] [Figure 9A] 1 shows an in vitro calibration curve for a glucose sensor. [Figure 9B] 1 shows an in vitro calibration curve for a glucose sensor. [Figure 9C] 1 shows an in vitro calibration curve for a glucose sensor.

[0022] [Figure 10A] Optimization of the hand-washing process is shown through three replicate experiments using different glucose sensors. [Figure 10B] Optimization of the hand-washing process is shown through three replicate experiments using different glucose sensors. [Figure 10C] Optimization of the hand-washing process is shown through three replicate experiments using different glucose sensors. [Figure 10D]Optimization of the hand-washing process is shown through three replicate experiments using different glucose sensors. [Figure 10E] Optimization of the hand-washing process is shown through three replicate experiments using different glucose sensors.

[0023] [Figure 11A] Optimization of contact time is shown. [Figure 11B] Optimization of contact time is shown. [Figure 11C] Optimization of contact time is shown. [Figure 11D] Optimization of contact time is shown. [Figure 11E] Optimization of contact time is shown. [Figure 11F] Optimization of contact time is shown.

[0024] [Figure 12A] Indicates the stability of personal factors. [Figure 12B] Indicates the stability of personal factors.

[0025] [Figure 13] 1 shows a flow chart for calibration and analysis of sweat glucose signals versus blood glucose concentration using a fingertip touch-based sensor.

[0026] [Figure 14] Box plots of mean absolute relative differences (MARDs) for consecutive measurements during the day for three subjects are shown.

[0027] [Figure 15A] 1 shows an exemplary data processing protocol for personalized transformation equations. [Figure 15B] 1 shows an exemplary data processing protocol for personalized transformation equations.

[0028] [Figure 16A] Shown is a full-day sweat glucose measurement. [Figure 16B]Shown is a full-day sweat glucose measurement. [Figure 16C] Shown is a full-day sweat glucose measurement.

[0029] [Figure 17] The applications of the fingertip sweat sensor are shown below.

[0030] [Figure 18A] An example of a molecularly imprinted polymer (MIP)-based sensor for rapid, stress-free cortisol sensing is presented. [Figure 18B] An example of a molecularly imprinted polymer (MIP)-based sensor for rapid, stress-free cortisol sensing is presented. [Figure 18C] An example of a molecularly imprinted polymer (MIP)-based sensor for rapid, stress-free cortisol sensing is presented. [Figure 18D] An example of a molecularly imprinted polymer (MIP)-based sensor for rapid, stress-free cortisol sensing is presented. [Figure 18E] An example of a molecularly imprinted polymer (MIP)-based sensor for rapid, stress-free cortisol sensing is presented. [Figure 18F] An example of a molecularly imprinted polymer (MIP)-based sensor for rapid, stress-free cortisol sensing is presented.

[0031] [Figure 19A] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19B] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19C] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19D] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19E] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19F]Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19G] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19H] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19I] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19J] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19K] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19L] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19M] Optimization and calibration of MIP cortisol sensing in various media is shown. [Figure 19N] Optimization and calibration of MIP cortisol sensing in various media is shown.

[0032] [Figure 20A] 1 shows an example of endogenous cortisol monitoring. [Figure 20B] 1 shows an example of endogenous cortisol monitoring. [Figure 20C] 1 shows an example of endogenous cortisol monitoring. [Figure 20D] 1 shows an example of endogenous cortisol monitoring. [Figure 20E] 1 shows an example of endogenous cortisol monitoring. [Figure 20F] 1 shows an example of endogenous cortisol monitoring.

[0033] [Figure 21A] 13 shows an example of cortisol sensing during acute stimulation with CPT. [Figure 21B] 13 shows an example of cortisol sensing during acute stimulation with CPT. [Figure 21C]13 shows an example of cortisol sensing during acute stimulation with CPT. [Figure 21D] 13 shows an example of cortisol sensing during acute stimulation with CPT. [Figure 21E] 13 shows an example of cortisol sensing during acute stimulation with CPT. [Figure 21F] 13 shows an example of cortisol sensing during acute stimulation with CPT.

[0034] [Figure 22A] 1 shows an example of in vivo cortisol detection using a wearable sensor patch. [Figure 22B] 1 shows an example of in vivo cortisol detection using a wearable sensor patch. [Figure 22C] 1 shows an example of in vivo cortisol detection using a wearable sensor patch. [Figure 22D] 1 shows an example of in vivo cortisol detection using a wearable sensor patch. [Figure 22E] 1 shows an example of in vivo cortisol detection using a wearable sensor patch.

[0035] [Figure 23A] 1A-1D present diagrams and data plots illustrating exemplary embodiments and implementations of the operation of a touch-based biofuel cell (BFC) and bioenergy harvesting system in accordance with the present technology. [Figure 23B] 1A-1D present diagrams and data plots illustrating exemplary embodiments and implementations of the operation of a touch-based biofuel cell (BFC) and bioenergy harvesting system in accordance with the present technology. [Figure 23C] 1A-1D present diagrams and data plots illustrating exemplary embodiments and implementations of the operation of a touch-based biofuel cell (BFC) and bioenergy harvesting system in accordance with the present technology. [Figure 23D] 1A-1D present diagrams and data plots illustrating exemplary embodiments and implementations of the operation of a touch-based biofuel cell (BFC) and bioenergy harvesting system in accordance with the present technology. [Figure 23E]1A-1D present diagrams and data plots illustrating exemplary embodiments and implementations of the operation of a touch-based biofuel cell (BFC) and bioenergy harvesting system in accordance with the present technology.

[0036] [Figure 24] 13 shows data from exemplary in vitro and in vivo characterization embodiments of an exemplary touch-based BFC and bioenergy harvesting system.

[0037] [Diagram 25] 1 shows data from an exemplary optimization implementation for an exemplary touch-based BFC and BFC usage patterns for a bioenergy harvesting system.

[0038] [Figure 26] 1 shows data from an exemplary performance implementation of a touch-based BFC and integrated harvesting system.

[0039] [Figure 27A] 1A-1D provide diagrams and data plots illustrating example embodiments and implementations of the operation of a self-powered sensor display system in accordance with the present technology. [Figure 27B] 1A-1D provide diagrams and data plots illustrating example embodiments and implementations of the operation of a self-powered sensor display system in accordance with the present technology. [Figure 27C] 1A-1D provide diagrams and data plots illustrating example embodiments and implementations of the operation of a self-powered sensor display system in accordance with the present technology. [Figure 27D] 1A-1D provide diagrams and data plots illustrating example embodiments and implementations of the operation of a self-powered sensor display system in accordance with the present technology. [Figure 27E] 1A-1D provide diagrams and data plots illustrating example embodiments and implementations of the operation of a self-powered sensor display system in accordance with the present technology. [Figure 27F]1A-1D provide diagrams and data plots illustrating example embodiments and implementations of the operation of a self-powered sensor display system in accordance with the present technology. [Figure 27G] 1A-1D provide diagrams and data plots illustrating example embodiments and implementations of the operation of a self-powered sensor display system in accordance with the present technology.

[0040] [Figure 28] The synthesis of carbon nanotube (CNT) foam is shown.

[0041] [Figure 29] A photographic image of a 1x3 cm2 strip of CNT foam being bent is shown.

[0042] [Diagram 30] The water absorption performance of CNT foam is shown.

[0043] [Diagram 31] Assembly of CNT foam for BFC and lead zirconate titanate (PZT) chips is shown.

[0044] [Diagram 32] 1 shows a scanning electron microscope (SEM) image and corresponding electron dispersive X-ray spectroscopy (EDS) mapping of a CNT foam cathode.

[0045] [Diagram 33] Cryogenic scanning electron microscope (cryo-SEM) images of cross sections of porous and non-porous polyvinyl alcohol (PVA) hydrogels are shown.

[0046] [Diagram 34] Optimization of the BFC anode to cathode area ratio is shown.

[0047] [Diagram 35] Linear scan voltammetry (LSV) characterization of cathodes with different electrode materials is shown.

[0048] [Diagram 36] 4 shows the LSV characteristics of the anode without and with 15 mM lactate.

[0049] [Figure 37] The LSV response of the BFC after area ratio optimization is shown.

[0050] [Figure 38] Figure 1 shows electrochemical impedance spectroscopy (EIS) Nyquist plots of a two-electrode biofuel cell (BFC) covered by porous PVA hydrogel with different applied pressures.

[0051] [Figure 39] 1 shows a light microscope image of a finger with a bromophenol dye applied.

[0052] [Diagram 40] We show the BFC performance using subjects with different natural fingertip sweat rates.

[0053] [Diagram 41] 1 shows the stability of the hydrogel in a long-term harvest study.

[0054] [Diagram 42] Repeated compression of the BFC is shown.

[0055] [Diagram 43] Demonstrating energy harvesting from low intensity desk work.

[0056] [Diagram 44] 1 shows energy harvesting from inactivity during a night of sleep.

[0057] [Diagram 45] 1 shows the power harvested from a BFC pressed by a finger at different sweat durations.

[0058] [Diagram 46]4 shows the power of a BFC pressed at different frequencies.

[0059] [Figure 47] 1 shows the OCV of a PZT chip pressed at the center with different pressures.

[0060] [Figure 48] 13 illustrates energy harvesting using a PZT chip with different operating conditions.

[0061] [Figure 49] Charging a capacitor using an integrated device is shown for subjects with different sweat rates.

[0062] [Figure 50] 1 shows a system flow chart of the integrated system and corresponding voltage values.

[0063] [Figure 51A] 1 shows a schematic diagram of an exemplary embodiment of an integrated circuit substrate for a voltage regulator circuit. [Figure 51B] 1 shows a schematic diagram of an exemplary embodiment of an integrated circuit substrate for a voltage regulator circuit.

[0064] [Figure 52] 1 shows the power consumption of a microcontroller unit (MCU) at different operating voltages.

[0065] [Diagram 53] This shows the capacitor charging flow to the MCU.

[0066] [Figure 54] Shows the MCU output voltage and the charge to the electrochromic display (ECD).

[0067] [Figure 55] 1 shows an example of layer-by-layer printing and assembly of an ECD panel.

[0068] [Figure 56] 1 shows photographic images of printed ECDs displaying different content.

[0069] [Figure 57] 1 shows the current and charge consumption of the printed ECD.

[0070] [Figure 58] 13 shows examples of layer-by-layer printing and drop casting of sensors.

[0071] [Figure 59] 1 shows vitamin C sensor calibration.

[0072] [Figure 60] Optimization of the vitamin C sensor is shown.

[0073] [Figure 61] Sweat vitamin C determinations from fingertips for two subjects are shown.

[0074] [Figure 62] Pharmacokinetic correlates of response to L-Dopa using natural sweat and capillary blood samples.

[0075] [Figure 63] An exemplary method for determining a concentration of an analyte in at least one of an individual's blood, sweat, or interstitial fluid (ISF) in accordance with some embodiments of the disclosed technology is presented.

[0076] [Figure 64] An exemplary method for determining a concentration of an analyte in at least one of an individual's blood, sweat, or interstitial fluid (ISF) in accordance with some embodiments of the disclosed technology is presented.

[0077] [Figure 65]1 illustrates an exemplary method for determining a concentration of an analyte in the blood of an individual in accordance with some embodiments of the disclosed technology.

[0078] [Figure 66] 1 illustrates an exemplary method for generating electrical power using sweat analytes in accordance with some embodiments of the disclosed technology.

[0079] [Figure 67] 1 illustrates an exemplary method for determining a concentration of an analyte in a biological fluid of an individual in accordance with some embodiments of the disclosed technology.

[0080] [Figure 68] An exemplary device is shown that collects sweat for estimation of blood analyte concentrations or utilizes analyte redox reactions for energy generation in accordance with some embodiments of the disclosed technology. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0081] As the number of diabetic patients has increased exponentially, self-monitoring of blood glucose (SMBG) based on fingerstick blood samples has become an important component of diabetes management. However, self-monitoring or self-testing of blood glucose is limited by the number of allowable monitoring or testing per day. In addition, the inconvenience and pain associated with standard fingerstick blood sampling discourage patients from testing frequently. Thus, extensive efforts have been made to replace these blood fingerstick measurements and improve glucose management protocols.

[0082] Continuous blood glucose monitoring has been successfully achieved by using intradermal needles. However, a completely noninvasive, simple, and reliable approach for glucose detection has yet to be developed and validated. Thus, electrochemical biosensors for monitoring glucose in noninvasive biofluids such as saliva, tears, sweat, or interstitial fluid, which could potentially replace blood, have attracted quite recent attention. Although saliva is a readily available biofluid rich in several biomarkers, its complexity, including high viscosity and food and bacterial contamination, poses significant challenges with regard to reproducible glucose analysis. Although tears are composed mainly of water with low levels of external contaminants, and good glucose tear / blood correlations have been demonstrated, the inherent difficulty of collecting tears has hindered the development of user-friendly glucose tear sensors. Interstitial fluid (ISF) is currently an almost acceptable biofluid for glucose detection due to the dynamic equilibrium of that fluid with the bloodstream, which enhances its diagnostic relevance. However, it cannot be sampled easily, plus requires microneedles or reverse iontophoresis devices, which have issues with biofouling and skin irritation, respectively. Finally, sweat analysis has emerged quite recently among these biofluids as an attractive diagnostic biofluid due to its favorable chemical properties and non-invasive nature. Hence, the majority of non-invasive electrochemical biosensors rely on sweat analysis.

[0083] Thus, considerable efforts have been made to use sweat biofluids for non-invasive glucose monitoring. However, efforts to develop a fast and easy-to-use sweat-based glucose self-test have been largely hampered by the inherent inaccessibility of natural sweat and by the varying reports on the correlation between sweat and blood glucose concentrations. Sweat sampling is generally performed by rigorous exercise, iontophoresis, or heat-based sweat stimulation protocols. Easier and faster approaches to access this biofluid and a better understanding of the distribution of glucose molecules from blood to sweat are urgently needed for routine sweat-based user-friendly glucose self-testing.

[0084] Such limited understanding gives rise to various reports on sweat and blood glucose correlation. The reports include discussions on sweat collection methodologies from different body locations. Several studies have demonstrated good sweat / blood correlation in relation to sweat stimulation with agonist agents or sweat induced by physical activity. However, such correlation is only achieved by performing a simultaneous blood calibration for each test analysis, and the correlation is lost when attempting to use a calibration curve to convert the sweat signal. Current approaches to verify the correlation involve constructing a calibration curve using standard glucose concentrations in either an artificial or real sweat matrix. This correlation can be further obtained by using additional sensors that monitor and correct for possible variations in sweat pH, temperature, and salt concentration. The results are usually not satisfactory, especially when comparing readings from different subjects. Various reports on the reliability of sweat-based glucose assays reflect personal differences between individuals, including release rates and skin phenotypic characteristics related to age, sex, or race. Despite extensive research efforts, researchers still do not understand such a large variability in sweat gland function and skin physiology and structure from various populations. To properly use sweat as an attractive substitute for blood, these individual differences must be taken into account.

[0085] Disclosed herein are methods, materials, and devices for a new, rapid and reliable approach to the measurement of biomarker concentrations that combines a simple touch-based fingertip sweat sensor (e.g., electrochemical sensor) with a new computer-implemented algorithm that addresses individual differences toward accurate estimation of blood glucose concentrations. The new painless and simple glucose self-testing protocol leverages the fast sweat rate on the fingertip for rapid assay of natural sweating without any sweat stimulation, along with personalized sweat response-to-blood concentration conversion. Thus, reliable estimation of blood glucose sensing concentration can be achieved by a simple one-time personal pre-calibration. Such system training results in substantially improved accuracy with a Pearson correlation coefficient (Pr) higher than 0.95, with an overall mean absolute relative difference (MARD) of 7.79%, with 100% pair points residing in the A+B region of the Clark Error Grid (CEG). The speed and simplicity of the touch-based bloodless fingertip sweat assay, as well as the elimination of regular blood calibration, should result in frequent glucose self-testing and improved patient compliance toward improved diabetes management. The technology disclosed in this patent document also provides a reliable, non-invasive option for frequent monitoring of analytes other than glucose, such as levodopa, ketone bodies, lactate, alcohol, illicit drugs, tetrahydrocannabinol (THC), and cortisol, among others.

[0086] The disclosed technology can be implemented in some embodiments to provide, among other features and advantages, several significant improvements over existing technologies used to determine blood concentrations of biomarkers based on the sweat analyte responses of the biomarkers. In particular, the disclosed technology can be implemented in some embodiments to address inter-individual variability to accurately convert the sweat analyte responses of the biomarkers to concentration values ​​of those biomarkers in blood. Such novel personalized data processing methods provided by the disclosed technology are combined with touch-based fingertip sweat analysis. In some embodiments of the disclosed technology, sweat is collected upon skin contact with a collection hydrogel and then diffuses through the gel to a sensor where the analytes present in the sweat are measured.

[0087] In some embodiments of the disclosed technology, the personalized data processing method includes using a sensor to determine the concentration of an analyte in sweat (e.g., a sweat sample). For example, a glucose oxidase-based biosensor can be used to measure the glucose concentration in sweat, and a molecularly imprinted polymer (MIP)-based sensor device can be used to measure the cortisol concentration in sweat. In some embodiments, the sensor can include a sweat collection device having a sweat collection layer comprising a hydrogel, such as, for example, polyvinyl alcohol (PVA), agarose, or glycerol. The sweat collection layer can be placed adjacent to or on top of a biosensor constructed using screen printing, sputtering, inkjet, or any other suitable sensor fabrication technique. Passive sweat can be collected from the skin upon direct contact with the sweat collection layer. After contacting the skin for a predetermined time, the collected sweat diffuses through the hydrogel layer and reaches the recognition element or layer of the sensor, where the analyte concentration is measured. Several sensing technologies can be used for analyte concentration measurement, including but not limited to electrochemical, affinity, and optical-based.

[0088] In some embodiments of the disclosed technology, the personalized data processing method may further include determining a personalized (i.e., for a given individual) correlation equation using the determined concentration of the analyte (e.g., glucose) in sweat. To this end, analyte concentration measurements are, for example, performed periodically, e.g., over several days, using a sensor, and verified using a suitable approach. For example, the glucose concentration in sweat determined using the sensor (e.g., related to the output signal strength of the sensor) can be verified using a commercially available blood glucose meter. For example, blood samples can be collected and analyzed before (or simultaneously with, or immediately after) each corresponding measurement of glucose in sweat using a sensor constructed according to some embodiments of the disclosed technology. The measurement of glucose concentration in sweat performed using the sensor and the corresponding measurement of glucose concentration in blood performed using, for example, a commercially available blood glucose meter provide data points for the dependency between the glucose concentration in blood measured by the commercially available blood glucose meter and the glucose concentration or level in sweat measured using the sensor. Using the data points collected during the day, the linear slope and intercept of the dependency are obtained for each day of measurement. After several days of data collection, the linear slope and intercept values ​​are averaged to derive a personalized universal equation for directly converting sweat sensor signal strength to blood glucose concentration.

[0089] The disclosed technology can be implemented in some embodiments to provide new approaches to sweat-to-blood signal conversion, for example, new methodologies for converting sweat biomarker measurements into reliable estimates of blood concentrations of biomarkers based on personalized data processing that takes into account inter-individual variability. Current sweat sensors rely on extensive motion, heat or chemical stimulation to sample sweat, and therefore require time, energy and power consumption. In some embodiments of the disclosed technology, personalized data processing methods can include processing of signals obtained using passive natural sweat collection without the need to perform physical motion or any additional sweat stimulation steps or activities. The disclosed technology can be implemented in some embodiments to ensure that individual differences in sweat rate or skin characteristics between individuals are taken into account. Some sweat-to-blood conversion methods can produce conflicting results related to the correlation of concentrations of analytes (e.g., glucose, cortisol, lactate, etc.) in sweat with concentrations of those analytes in blood. The discrepancies in results are primarily related to the sweat collection and data processing steps. However, the disclosed technology can be implemented in some embodiments to provide a novel and accurate methodology for sweat analysis, including sweat collection, sensing and data processing steps.

[0090] The disclosed technology may be implemented in some embodiments to provide a reliable non-invasive option for frequent monitoring of analytes such as glucose, levodopa, ketone bodies, lactate, alcohol, illicit drugs, tetrahydrocannabinol (THC), and cortisol. Existing commercially available glucose meters (glucometers) require invasive, inconvenient, and painful finger-prick blood testing protocols for repeated frequent testing. Touch-based glucose testing implemented according to some embodiments of the disclosed technology allows for such frequent glucose measurements and eliminates the need for regular blood-based measurement and verification. The simplicity and speed of the touch-based blood-free finger-prick assay according to the disclosed technology has considerable potential for reliable and frequent self-testing of glucose towards improved diabetes management. Also, there is currently no commercially available test for cortisol detection. The method according to the technology disclosed herein can easily convert glucose and cortisol levels detected in sweat into blood glucose and cortisol concentration values, requiring simply touching the sensor with a fingertip and not requiring any invasive and sweat-inducing protocols.

[0091] In some embodiments of the disclosed technology, data is acquired using a sweat touch-based sensor, e.g., daily, e.g., over a week, and validated using an appropriate approach. For example, sweat glucose concentration determinations provided by the sensor can be validated using a commercially available blood glucometer, and cortisol levels can be validated using affinity testing (e.g., using an immunosensor). Initial data collection is used to estimate personalized slopes and intercepts of the dependence relating analyte concentrations measured by the sweat sensor to those measured by a reference device (e.g., a commercially available blood glucometer), and these personalized factors or parameters can be used over a period of weeks without the need for parallel blood testing. Therefore, a personalized universal equation is used to directly convert sweat analyte signal intensity to blood analyte concentration.

[0092] Data collection and processing according to some embodiments of the disclosed technology can be performed by measuring glucose levels in sweat collected from a fingertip. In some embodiments, the working electrode of a screen-printed three-electrode electrochemical sensor system is modified with the enzyme glucose oxidase, and a polyvinyl alcohol (PVA) hydrogel can be placed on top of the modified sensor to act as a sweat collector layer. Sweat is collected from the fingertip after proper washing of the hands, for example during one minute of contact. After collection, sweat glucose signals are obtained by chronoamperometry. Signals are obtained twice a day for one week and verified against a commercially available blood glucometer. A linear correlation between the two points (sweat and blood glucose) is obtained for each day of analysis, and the average slope and intercept of the dependence are calculated for the user. These personalized values ​​account for individual sweat parameters such as sweat rate and composition. In some embodiments of the disclosed technology, a personalized general equation is generated based on the personalized values ​​and then used to directly convert the sensor signal into a blood glucose concentration value. The disclosed technology can be implemented in some embodiments to use other analytes other than fingertip sweat, such as, for example, levodopa, lactate, alcohol, illicit drugs, tetrahydrocannabinol (THC), and ketone bodies, simply by modifying the electrode surface sufficient for the analyte.

[0093] In some embodiments of the disclosed technology, sweat cortisol levels can also be measured by touching the PVA gel with a fingertip, for example, for 30 seconds after washing hands for 2 minutes. The cortisol sensor has a molecular imprinted polymer (MIP) layer containing a signal indicator and cavities for cortisol detection, providing a label-free MIP sensor that does not require an additional external signal indicator for measurement with high selectivity. The signal indicator can be any material with redox properties, such as Prussian blue, ferrocene, methylene blue, or others. The current response using chronoamperometry is measured after a 2-minute incubation time to have a binding process between the MIP layer and cortisol. To verify the sensor performance, a competitive immunosensor for cortisol was introduced using iontophoresis-induced sweat.

[0094] 1A-1F show an example of a bloodless finger prick sweat analyte sensor 100 according to some embodiments of the disclosed technology. Specifically, FIG. 1A shows a portable sensor data processing device 150 (e.g., a handheld potentiostat, etc.) coupled with a bloodless fingerprint sweat analyte sensor 100 (also referred to as a "touch sweat sensor") according to some embodiments of the disclosed technology for electrochemical determination of analytes in sweat. In this example, the target analyte is glucose, and chronoamperometry is used with a fixed applied potential (e.g., -0.2V) to perform electrochemical detection of target blood biomarkers present in sweat deposited on electrodes of sensor 100.

[0095] FIG. 1B shows an image of an exemplary implementation of touch sensor device 100, further showing a user's fingertip in contact with electrode assembly 120 of the electrochemical sensing electrodes of sensor 100, and showing the sweat glands, sweat collection protocol, and sweat collection layer (e.g., a polyvinyl alcohol (PVA) layer) of sensor 100.

[0096] 1C shows a diagram illustrating an exemplary embodiment of a touch sweat sensor 100. The touch sweat sensor 100 has: a substrate 110 (e.g., comprising polyethylene terephthalate); an exemplary embodiment of an electrode assembly 120 embodied as a three-electrode configuration (e.g., working electrode (WE), counter electrode (CE), and reference electrode (RE)) that may be formed as a screen-printed sensor; an insulating layer 113 disposed over an electrical interconnect 117 for coupling the electrode assembly 120 to an interface region (e.g., contact pad) of the sensor 100; and a sweat-permeable layer (also referred to as a sweat-collecting layer) 115. Some exemplary embodiments include one or more PVA layers.

[0097] FIG. 1D illustrates an exemplary implementation of sweat collection and target blood biomarker detection, e.g., of the biomarker glucose, from a subject's fingertip, passing through an exemplary PVA gel (i.e., an embodiment of the sweat collection layer 115) and reaching the electrode assembly 120 for electrochemical detection. In some embodiments, for example, at least one of the electrodes (e.g., the working electrode (WE)) includes a chemical recognition layer 121 that includes one or more chemical reaction enhancers to catalyze or promote a chemical reaction involving the target biomarker that causes the generation of an electrical signal detectable by the electrode assembly 120. In the example shown in FIG. 1D (right panel), the chemical recognition layer 121 includes a Prussian Blue (PB) layer modified with the enzyme glucose oxidase (GOx) formed on an exemplary screen-printed working electrode to provide an electrochemical sensor transducer for selective detection of the hydrogen peroxide product (H2O2) of the glucose / GOx enzymatic reaction, which generates an electrical signal at the working electrode that the electrode assembly 120 can use to indicate a parameter (e.g., concentration) of glucose in the sweat fluid. For example, as shown in the schematic diagram of FIG. 1D, i.e., showing the enzymatic reaction at the GOx working electrode, glucose is converted into gluconic acid and hydrogen peroxide. The hydrogen peroxide molecules are then detected by the PB-modified working electrode. The electrical signal detectable by the electrode assembly 120 is processed by a data processing device, such as the portable sensor data processing device 150 shown in FIG. 1A, to determine the parameters of glucose.

[0098] Figure 1E shows the workflow for sweat glucose detection using a touch-based sweat sensor. After 20 min of food ingestion, sweat is collected after 1 min of contact with the sensor, and amperometric detection is immediately performed measuring sweat glucose. Using the personalized conversion equation shown in Figure 1F, the sweat signal is converted to blood glucose level.

[0099] Figure 1F shows data processing for sweat and blood correlation. Current signals collected from three subjects are directly correlated with blood values, showing Pearson's r (Pr) values ​​of 0.77. Thus, sweat-based blood glucose concentrations (SG) are estimated using individualized parameters K and Io. After applying the personal equation for each data set, the sweat-blood correlation increases to 0.95.

[0100] As mentioned above, the disclosed technology can be implemented in some embodiments to combine novel personalized algorithms for detecting sweat and blood concentrations of analytes of interest with simple and effective touch-based fingertip sweat collection and electrochemical detection to obtain a rapid, reliable and user-friendly self-test for glucose (FIGS. 1A and 1B). The fingertip has a high density of sweat glands (~400 sweat glands cm). -2 ), 50-500nLcm -2 min -1 Fingertip sweating produces sweat at a relatively high rate at low temperatures. Such natural fingertip sweating has recently been used for the optical detection of illicit drugs, the electrochemical detection of sweat lactate and caffeine, and the LC-MS / MS measurement of tryptophan and dopamine.

[0101] Methods and devices based on some embodiments of the disclosed technology can exploit the rapid sweat rate on the fingertip for rapid glucose measurement in natural sweating without the need for strong sweat-inducing exercise activity or iontophoretic sweat stimulation. Sweat collection from the fingertip is based on touching the surface of a sweat-absorbing polyvinyl alcohol (PVA) porous hydrogel thin film that can draw sweat droplets from the fingertip by capillary pressure for a controlled time (Fig. 1A-1D). The porous PVA thin film is placed on an electrochemical biosensor for subsequent glucose detection upon transporting sweat toward an enzyme layer covering a Prussian blue (PB) transducer. Glucose detection is made by selectively reducing the enzymatically liberated hydrogen peroxide with the PB transducer (Fig. 1C-1D). Such a rapid and simple contact-based bloodless fingertip sweat glucose assay holds considerable promise for improved patient compliance and enhanced diabetes management.

[0102] However, while natural sweating of the attractive fingertip can greatly simplify glucose sweat measurement, such direct measurements fail to address inter-individual variability and generally correlate poorly with blood glucose assays. To address these issues, the technology disclosed in this patent document uses a new "personalized" mathematical approach that substantially improves sweat-blood glucose correlation and the overall accuracy of such diabetes testing. Such a simple one-time personal calibration addresses differences in sweat rate and skin characteristics between individuals through a separate sweat-to-blood conversion algorithm after one-time training of the system. A short individual system training involves blood-verified sweat signals estimating the average individual slope (K) and intercept (Io) for each person to obtain an individualized sweat-to-blood conversion factor (Figure 1F). Such initial training and treatment allows for a substantially higher Pearson correlation coefficient (Pr) of 0.95, resulting in significantly higher accuracy, reflected in an overall mean absolute relative difference (MARD) of 7.79%, with 100% of the paired points in the A+B region of the Clarke Error Grid (CEG). These substantial improvements are realized without the need for additional sensors and complex microfluidic networks to correct and normalize the results. Following a one-time personal training of such a system, accurate blood glucose values ​​can be directly estimated from the individual's sweat glucose response over an extended period of several weeks, based only on his / her sweat signal, without the need for blood sampling (Figure 1E). A single blood calibration is recommended once or twice a month. Such single periodic measurements are analyzed by the software, which screens for outliers and updates existing personal parameters. When using the new algorithm among multiple subjects, the Pr value increases from 0.77 (raw sweat signal vs. blood glucose) to over 0.95 (calculated sweat glucose vs. blood glucose), as shown in Figure 1F for three subjects. Detailed studies show substantially higher accuracy when using both personal intercept and slope, compared to using only the slope. Such greatly improved correlation is achieved despite the values ​​of slope and intercept values ​​differing substantially between subjects.In some implementations, the slope value corresponds to the fingertip sweat velocity, while the intercept reflects multiple factors based on individual skin characteristics and sweat composition. However, it is noted that negligible electroactive interference is expected for PB-based electrode transducers using a detection potential of -0.20 V. This simple mathematical treatment can be easily integrated into software (e.g., handheld meter or smartphone app) to provide a built-in personal calibration towards autonomous estimation of sweat-based blood glucose concentration (SG). Our extensive data strongly supports the subject's personal equation based on the initial blood effective sweat response. Once such an individualized conversion is obtained, blood glucose levels can be directly and reliably estimated from sweat measurements without the need for blood guideline validation. A single blood calibration is recommended once or twice a month. Such a single periodic measurement is analyzed by the software, which screens for outliers and updates existing personal parameters. By considering inter-individual variability, the new approach provides an effective normalization of sweat glucose response, resulting in a significant improvement of correlation parameters between human sweat and blood. This allows potential applications for monitoring other sweat biomarkers.

[0103] An embodiment of the sweat-permeable layer 115 includes a hydrogel that can be made from an aqueous precursor containing a solution of monomers or polymers that can be subsequently crosslinked and solidified, either chemically or physically. The precursor can optionally contain a template material that can be removed from the solidified hydrogel forming pores within the gel structure. The formation of these porous structures within the hydrogel can aid in the material transfer of analytes from the skin surface to the electrode surface. The size of the pores can be adjusted by varying the type, amount, and removal method of the template material, and are generally macroporous with sizes of 50 nm or greater, including pore sizes ranging from 1 μm to 1 mm, and the pores can be configured to be of substantially the same or similar size regimes, or of various size regimes. For example, in some embodiments, when the aforementioned crosslinking is done in situ on the electrode, the gel can provide better bonding to the electrode surface. The gel-on-electrode combination can be made convenient for disposable use. One such example includes a porous PVA hydrogel.

[0104] Exemplary materials used in implementations to produce and test exemplary embodiments of the bloodless fingerprint sweat analyte sensor 100 include an exemplary PVA hydrogel for the sweat-permeable layer 115 of the sensor 100. Polyvinyl alcohol (PVA) (MW ∼89,000), phosphate buffer solution (PBS) (1M, pH = 7.4), potassium hydroxide (KOH), sucrose, sodium chloride, potassium chloride, glutaraldehyde, glucose oxidase (GOx), glucose, silver / silver chloride ink, and Prussian Blue (PB) carbon ink, dielectric ink, and Ecoflex 00-30 were used to evaluate methods and devices implemented according to some embodiments of the disclosed technology. Chronoamperometric measurements can be performed using a potentiostat.

[0105] Electrodes for the finger-based glucose sensor are fabricated by screen printing using a semi-automated MMP-SPM printer and a developed custom stainless steel stencil with dimensions of 12 in. × 12 in. × 75 μm thick. The electrodes are printed layer by layer. First, a silver / silver chloride ink is printed on a polyethylene terephthalate (PET) substrate as the interconnect and reference electrode, followed by a layer of PB carbon ink as the working and counter electrodes. Each layer is cured in an oven at 80 °C for 10 min. The working electrode is modified with 2 μL of GOx 40 mg / ml in 0.1 M PBS pH 7 containing 10 mg / ml BSA. After drying at room temperature, 0.5 μL of a 0.5% aqueous solution of glutaraldehyde is added to the GOx-modified working electrode and dried at 4 °C overnight.

[0106] For the fabrication of porous PVA hydrogels, stock solutions of PVA (MW ~89,000) and KOH dissolved in water are prepared in weight ratios of 1:10 and 1:5, respectively. 10 g of the PVA solution is then transferred to a beaker, followed by dropwise addition of 2 ml of water containing 14 g of KOH solution and 2.6 g of sugar under gentle stirring conditions to form a hydrogel precursor. 15 g of the precursor is then poured into a Petri dish (diameter ~9 cm) and left in a vacuum desiccator to remove excess water and crosslink until only 2 / 3 of the precursor's weight remains. The crosslinked PVA gel is then immersed in 0.1 M PBS buffer to remove the sugar template and excess KOH until the gel reaches a neutral pH. The gel (1 mm thick when immersed) is then cut into pieces of the desired size (1 × 1 cm). 2 ) and stored in PBS for future use.

[0107] In some implementations, a physical assessment on a human subject may be performed as follows: The glucose response is recorded by measuring the current difference between the background signal (PVA gel before contact) and the sweat glucose signal for 1 min at an applied potential of -0.2 V (vs. Ag / AgCl). The patient is asked to wash their index finger with wet (DI water). After washing, the fingertip is allowed to collect sweat for 3 min and then touched to the PVA sweat collection gel for 1 min. Immediately after contact, the sweat glucose signal is recorded.

[0108] Touch-based non-invasive sweat fingertip glucose detection involves two steps: sweat collection by touching a sweat-absorbing porous hydrogel membrane (covering an enzymatic biosensor) and amperometric detection of the product of the biocatalytic reaction using the biosensor (FIG. 1B). The high density of sweat glands in the fingertip ensures sufficient biological fluid volume for reliable and reproducible glucose measurement. Sweat collection from the fingertip is performed for a minimal time, e.g., about 1 minute, when the fingertip is in direct contact with the sweat-permeable layer 115. In some exemplary embodiments, the sweat collection layer 115 has a porous polyvinyl alcohol (PVA) hydrogel material disposed on the sensor surface, which facilitates the collection and transport (i.e., permeation) of sweat fluid containing its components, including the target analyte, across the opposing sides of the layer. In some embodiments, for example, the PVA hydrogel has pores with pore sizes larger than 50 nm, which may include up to 1 μm or up to 1 mm. From direct contact of the fingertip with the sweat-permeable layer 115, a small amount of sweat fluid is captured (collected) within the layer 115 and migrates through the layer 115, where the collected sweat diffuses to the recognition layer (i.e., the modified and / or unmodified electrodes of the electrochemical sensor), where an enzymatic reaction occurs to detect parameters for analytes in sweat, and is processed to determine parameters for analytes in blood (discussed later in this disclosure). Flexible polyethylene terephthalate (PET) is used as a substrate for screen printing a three-electrode (120) system electrochemical sensor (FIG. 1C). The sensor 100 is designed to fit a handheld potentiostat for dispersion analysis (FIG. 1A). As shown in FIG. 1D, the sensor 100 includes a substrate 110, electrodes (e.g., working electrode WE, counter electrode CE, reference electrode RE) 120, and a porous sweat-permeable layer 115, such as the exemplary PVA layer described above. A screen-printed Prussian blue working electrode transducer was modified with the enzyme glucose oxidase (GOx) and used for selective detection of the hydrogen peroxide product of the glucose / GOx enzymatic reaction (Figure 1D), which resulted in a sensitivity of 2.89 nA.µM, as shown in Figures 9A-9C. -1 It has a sensitivity of

[0109] Such painless touch-based glucose sensors represent a promising non-invasive approach to improve diabetes monitoring by increasing the frequency of glucose testing. However, analyzing glucose from sweat is a challenging task. Sweat glucose levels can vary depending on the method used for sweat collection. For example, sweat obtained during exercise may underestimate glucose levels, while iontophoresis may overestimate glucose levels due to the accumulation of glucose on the iontophoresis gel. In addition, contamination from skin components such as bacteria, body creams, and even glucose itself may also have an effect on the measured glucose values. Glucose concentrations in sweat range from 0.01 to 1.11 mM, which is significantly lower than blood concentrations (2 to 40 mM). Thus, fingertip touch glucose sensors ensure user-friendly sweat collection since they do not involve exercise or chemical stimulation of sweat glands. The electrochemical signal is then converted to blood glucose levels using a novel personalized algorithm to address individual skin characteristics and sweat rates. Pr values ​​for different subjects increase from 0.77 to over 0.95 when using such an individualized approach (Figure 1F).

[0110] 2A-2D show an example of a data processing protocol for personalized conversion equations. Specifically, Fig. 2A and Fig. 2C show correlating the individual values ​​of the signal from sweat for each day with the blood values ​​generating a linear plot with specific slope and intercept values ​​(i-iii). The resulting slopes and intercepts for each day are then averaged to generate a personalized equation for each user (b). Having obtained such a personalized conversion equation, the current signal from glucose in sweat is used to directly convert the signal to blood glucose levels. Fig. 2B and 2D show data from sweat glucose monitored twice a day for four days for two subjects. The upper curves (e.g., 210, 230) correspond to the signal before sweat collection (PVA gel only), while the lower curves (e.g., 220, 240) correspond to the sweat glucose response. Prior to each analysis, the user's blood glucose level is measured using a commercially available blood glucose meter.

[0111] Following the successful implementation of the touch-based sweat collection / electrochemical detection protocol sensor, the disclosed technology may be implemented in some embodiments to provide a new mathematical approach to correlate sweat glucose response with blood glucose concentration. Such a personalized sweat-to-blood conversion algorithm involves measuring fingertip sweat glucose response and calibrating these current values ​​using blood glucose levels from a commercially available glucometer. Measurements are performed at the same time each day (Fig. 2B,D). Sweat and blood glucose levels are measured before and 20 min after ingesting a meal. The optimized protocol for finger sweat analysis is strictly implemented. First, the patient is asked to clean the index finger using a wet tissue and wait for 3 minutes; then, the patient is asked to touch the sensor for 1 minute. The sweat signal is then measured using chronoamperometry at a fixed potential of -0.2 V for 60 seconds. It is noted that the use of soap to wash the finger reduced the measured signal due to potential interactions of surfactant residues with either the PVA gel or the enzyme layer. Thus, in Figures 10A-10E, mechanical washing with water is used. This washing protocol is followed by an optimal contact time of 1 min (Figures 11A-11F). The calibration plots for each day are analyzed and the average slope and intercept are calculated (Figures 2A and 2C(i-iii)). To convert the sweat glucose response to blood glucose concentration, the following equation is used: K = Δi SG / ΔBG (Equation 1) I O =i SG -(K×BG) (Formula 2) SG={i SG -(I O )} / K (Equation 3)

[0112] Slope (K) and intercept (I o ) are calculated using Equation 1 and Equation 2, respectively. As shown in Figures 2A and 2C, the slope corresponds to the change in current (Δi) obtained from the glucose sweat sensor, which correlates with the change in blood glucose concentration (ΔBG) obtained from the glucometer. i SGrepresents the current response of the sweat sensor, and BG is the blood glucose concentration (Figure 2A and Figure 2C). Finally, as shown in equation (3), the concentration of glucose based on sweat is calculated using the current response of the glucose sweat sensor, as well as the average K and I o can be estimated using equation (3) from (Figures 2A and 2Cb).

[0113] As shown in Figure 2A and 2C(b)(iii), the obtained sweat-based estimated blood glucose concentrations correlate closely with the reference fingerstick blood glucose concentrations (Pr = 0.98, for both subjects), despite large differences in the individual slope and intercept values. Also, from Figure 2A and 2C(a)(ii and iii), note that these individual K and Io parameters are stable throughout the month (Figure 12A-12B). The built-in software is used to assess the stability of these personal parameters along with a single biweekly or monthly blood calibration point. This safety calibration point is analyzed by the custom software, which identifies potential changes and updates the existing personal parameters (Figure 13). The normalization of sweat parameters represents the main advantage of this new approach, as once the matrix (sweat) variables are removed, correlations increase when data from multiple subjects are analyzed (Figure 3).

[0114] Figures 3A-3F show examples of the effect of different data conversion steps converting the current response of a sweat glucose sensor to sweat-based blood glucose concentration. Specifically, Figure 3A shows the data conversion steps from (a) the current response to sweat glucose concentration, (b) the personalized slope only, and (c) the personalized slope and intercept from different subjects (n=18). (Figures 3B-3D) Bar graphs show the correlation between measured blood glucose levels and sweat-based blood glucose concentration calculated using: (a) the personalized slope only, (b) the slope along the intercept from different subjects. (Figures 3E-3F) Clarke Error Grid (CEG) analysis results using only the personalized slope (E) and both the personalized slope and intercept (F).

[0115] The personalized parameters are obtained for the three subjects and applied to a new set of six measurements obtained for each user. The sweat sensor current signal is plotted against the reference blood values ​​measured with a glucometer (Figure 3A(a)). A Pr value of 0.77 is observed for the correlation between the sweat current response and blood glucose values ​​for the three subjects, indicating a limited correlation between the fingertip sweat glucose response and the blood glucose values. A mathematical personalized treatment is then applied to the results shown in Figure 3A(a). To demonstrate the importance of each personalized parameter, the personalized gradient (K) is first used alone to convert the sweat response to a blood glucose value (Figure 3A(b)). Such a transformation is commonly used in the literature for signal transformation. As shown in Figure 3A(b), the use of the personalized gradient increases the Pearson correlation from 0.77 to 0.90. This improvement reflects the personalized character of the protocol associated with the use of individual K values ​​for each data set. However, as shown in Figures 3B-3D(a), such a treatment based only on the individual slope results in insufficient accuracy, and the predicted blood values ​​differ significantly from the reference blood concentrations. In contrast, significantly improved accuracy is achieved by including the individualized intercept value Io in the mathematical treatment (along with the slope value). This brings the sweat-based predicted blood glucose values ​​significantly closer to the reference values ​​(Figures 3B-3D(b)), and further improves the Pr value to 0.95 (Figure 3A(c)).

[0116] The importance of using the personalized full equation can be clearly demonstrated by analyzing and comparing the CEG plots for both situations, the case with the use of the slope only (Figure 3E) and the case when the full equation is applied (Figure 3F). CEG analysis is commonly used to evaluate deviations of clinical significance between predicted BG concentrations and reference concentrations (e.g., glucometer). Such analysis uses a Cartesian diagram in which target and predicted BG values ​​are paired. Each pair is in one of five regions of the CEG diagram, with zone A showing values ​​within 20% of the reference concentration. Thus, pairs located in region A represent clinically correct predictions. It is therefore highly desirable to obtain all results within this zone. Pairs of points in region B are clinically acceptable (but not for treatment decisions), whereas pairs in regions C, D, and E are considered significant clinical errors. The CEG diagram of the touch-based sweat assay using only the slope shows that the majority of points (85%) are present in region B, and only two points (12.5%) are located in region A (Figure 3E), corresponding to a considerable correlation with the reference concentration. In contrast, when using a personalization step including both slope and intercept, the CEG analysis finds that the majority of points (81.2%) are present in region A, while only three points (18.8%) are located in region B. Overall, the CEG analysis in Figures 3E and 3F clearly demonstrates that personalization calculations based on both slope and intercept strongly influence the correlation between sweat glucose measurements and blood reference methods towards a reliable prediction of blood glucose concentrations. In addition, the measurements shown in Figure 3A(c) are also used to calculate the mean absolute relative difference (MARD). The total MARD for the touch glucose sensor is 7.79% (range 3.5-15.0% in Figure 14), based on all individual paired data points from 18 recordings of three subjects. Such a value (less than 10%) reflects the high accuracy of the methodology.

[0117] The performance of the touch-based sweat glucose sensor and the corresponding mathematical personalization procedure are evaluated in a "blind" study. Glucose levels from three patients for whom the personalized equations were previously established are monitored during a day of prolonged operation, including six measurements obtained before and after the corresponding meal intake. The same protocol is used for each sweat measurement (index finger wash, 3 min wait, 1 min contact) with a new sensor and gel each time. The sweat current signal is converted to a predicted blood glucose concentration using each subject's personal equation. The calculated blood values ​​from these "blind" studies are shown in the blue (circle) plots in Figures 4A-C. Before each sweat measurement, blood values ​​are measured and stored for comparison. The person responsible for calculating the predicted blood concentrations had no access to the reference blood values. Figures 4A-4C(b) show the correlation between sweat-based calculated blood concentrations and the corresponding blood reference levels. These data clearly show that the dynamics of such sweat-based predicted blood glucose concentrations throughout the day closely match the actual temporal blood glucose profile. Pearson's values ​​are higher than 0.95 in three subjects (ranging from 0.95 to 0.99). It is important to note that these "blind" tests are conducted one week after the initial personal system training, reflecting the robustness of the method (as well as the stability of the slope and intercept values). As shown in Figures 12A-B, the personal formula is stable for at least one month, eliminating the need for intermediate blood fingerpicking. However, regular blood calibration (once or twice a month) is recommended to ensure conversion accuracy.

[0118] Figures 4A-4C show examples of measurement results obtained for all-day sweat glucose measurements (circle markers) along with the corresponding blood measurements (square markers). Figures 4A(a), 4B(a), and 4C(a) show glucose levels in sweat collected from the fingertip (indicated by arrows) at three meals over one day, immediately before and 20 minutes after completing the three meals, using a touch sensor device. The signal obtained from the sweat sensor is directly converted to blood glucose levels using a conversion equation personalized for each user. Figures 4A(b), 4B(b), and 4C(b) show the resulting correlation plots and the corresponding Pr values.

[0119] 5A-5B show an exemplary principle of operation of a fingertip levodopa biosensor according to some embodiments of the disclosed technology. FIG. 5A shows the general procedure performed during levodopa monitoring, including: (a) a 100:25 levodopa:carbidopapyr intake is performed, followed by sweat collection using a fingertip biosensor according to the disclosed technology, (b) the same measurement procedure is repeated every 10 minutes for one hour. FIG. 5B shows: (a) a depiction of a finger placed on top of the biosensor, (b) a zoomed image of the finger area shows sweat secretion from the sweat glands, followed by sweat collection on the hydrogel membrane. The high porosity of the membrane and the incubation time allow diffusion of sweat to the tyrosinase modified transducer. When a negative potential is applied to the biosensor, an electrochemical reaction occurs, resulting in the donation of two electrons from the electrode surface to the analyte collected from the sweat, allowing the reduction of L-dopa to L-dopaquinone.

[0120] 6A-6C show an exemplary timeline performed during levodopa monitoring in sweat. Specifically, FIG. 6A shows a physical examination to begin monitoring the signal output without sweat. A levodopa pill is then orally administered. After tablet ingestion, the subject is asked to place the tip of a finger within the hydrogel membrane for approximately 2 minutes. After the sweat collection step, two potential step measurements are performed. To monitor every 10 minutes, the subject is asked to wait 6 minutes after this step until they have completed a 10-minute cycle. The subject then repeats the contact and measurement steps. The multilayer composition of the biosensor includes a carbon ink electrode modified with tyrosinase enzyme. The glutaraldehyde layer allows for the capture of the enzyme and provides stability for extended operation of the sensor. A negative potential applied to the biosensor allows for the reduction of levodopa in sweat to L-dopaquinone. FIG. 6B shows that a current signal is obtained before and after contacting the sensor, which allows the current difference (ΔI) to be obtained continuously every 10 minutes. The example shown shows an exemplary profile obtained by monitoring ΔI over a period of 1 hour after tablet ingestion, with an increase in current being observed several minutes after tablet ingestion. Figure 6C shows a chronoamperogram obtained at 10 minute steps, showing the current obtained before (upper black line, e.g., line 610) and after taking the drug (lower light black line, e.g., line 620).

[0121] FIG. 7 shows exemplary data collected during an exemplary physical demonstration of a levodopa biosensor. The performance of the fingertip sensor is tested on three different subjects at 10 minute intervals over an extended period of 1 hour. On the left side of the image, chronoamperograms obtained at each time interval are displayed. The upper black lines of each set (e.g., line 710) show the current output before any sweat collection. The lighter black lines (e.g., line 720) correspond to the current signal obtained after sweat collection and 100:25 pill ingestion. On the right side, current differences (ΔI) obtained at 10 minute intervals are displayed. The dotted vertical line labeled "Pill" sets the time at which the oral ingestion of a pill is made.

[0122] 8 shows an exemplary comparison of levodopa profiles obtained by levodopa measurements in blood and sweat. The current profiles of levodopa in blood (profiles 810 and 830 in FIG. 8) and sweat (profiles 820 and 840 in FIG. 8) are monitored at 10 minute intervals for a period of 1 hour. The arrow "P" indicates the time instance of tablet ingestion.

[0123] The disclosed technology may be implemented in some embodiments to provide a new non-invasive approach for rapid, simple, and accurate sweat glucose testing, and a new algorithm to address inter-individual variability and obtain significantly improved accuracy. Thus, natural sweat from the fingertip is used for electrochemical determination of glucose using a highly selective glucose oxidase Prussian blue sensor in conjunction with a sweat-collecting hydrogel. The resulting sweat glucose current value is converted to a predicted glucose blood concentration by applying a personalization equation to account for personal differences between test subjects. In some implementations, using personal parameters, the Pearson (Pr) correlation value increases from a Pr value of 0.77 to 0.95, resulting in a MARD of 7.79% with 100% of paired points within the A+B region of the Clark error grid. Such a significantly improved correlation is achieved despite a large variation in slope and intercept values ​​between subjects. The simplicity, speed, and accuracy of the new touch-based fingertip assay have considerable potential for reliable and frequent self-testing of glucose towards improved diabetes management. Such bloodless assays therefore represent an attractive non-invasive alternative to finger-prick blood glucose measurements (especially when frequent blood glucose measurements are desired). The new personalized data processing approach can be applied to a wide range of electrochemical sweat assays for important analytes such as levodopa, alcohol or cortisol.

[0124] In some embodiments of the disclosed technology, a device for collecting sweat to estimate the concentration of blood analytes or to utilize redox reactions of such analytes for energy generation includes: a substrate; an electrode disposed on the substrate and operable to detect and / or perform energy collection from analytes in sweat; a sweat-permeable layer having a first side and a second side opposite the first side. The first side of the sweat-permeable layer contacts the electrode such that the electrode is disposed between the substrate and the first side of the sweat-permeable layer. The sweat-permeable layer is configured to allow sweat applied to the second side permeate through the sweat-permeable layer to reach the electrode through the first side of the sweat-permeable layer.

[0125] In some exemplary embodiments, the electrode is part of one of an electrochemical sensor, an affinity-based sensor, and an optical sensor, a catalytic / biocatalytic fuel cell. In some exemplary embodiments, the sweat-permeable layer includes at least one layer of hydrogel. In some exemplary embodiments, the hydrogel includes at least one of polyvinyl alcohol (PVA), polyacrylic acid (PAA), polyethylene oxide (PEO), polyacrylamide (PAM), a cellulose material (e.g., cellulose, methylcellulose, ethylcellulose, hydroxyethylcellulose), agar, gelatin, agarose, alginate, glycerol, ethylene carbonate, propylene carbonate, and the hydrogel is disposable after each use or reusable with a corresponding container for storage and placement. In some exemplary embodiments, the analyte is glucose, and the electrode includes an electrochemical sensor including a reference electrode, a working electrode, and a counter electrode, the reference electrode includes silver, and the working electrode includes Prussian blue and glucose oxidase. In some exemplary embodiments, the analyte or fuel is lactate, the electrodes include an electrocatalytic anode and a cathode, the cathode includes a catalyst capable of promoting an oxygen reduction reaction or an oxidizable material that can itself be reduced, including silver oxide, nickel oxide, manganese oxide, and the anode electrode includes lactate oxidase and a reaction mediator, such as tetrathiafulvalene (TTF), naphthoquinone (NQ), ferrocene and its derivatives (e.g., methylferrocene, dimethylferrocene), or complexes thereof (e.g., tetrathiafulvalenetetracyanoquinodimethane).In some exemplary embodiments, the electrodes are configured to be thick and porous, the electrodes are comprised of carbonaceous materials including graphite, carbon black, carbon nanotubes, or graphene, the electrodes include elastomeric binders including styrenic triblock copolymers (e.g., polystyrene-polyisoprene-polystyrene, polystyrene-polybutylene-polyethylene-polystyrene), fluorinated rubbers (e.g., poly(vinyl fluoride-tetrafluoropropylene)), polyethylene vinyl acetate, polyurethane, ecoflex, or polydimethylsiloxane, the electrodes include template particles that are removed by dissolution or etching, including salts (e.g., sodium chloride, sodium bicarbonate), sucrose, metals (e.g., Mg, Zn), or polymers (e.g., styrene), and the electrodes include redox active materials such as conductive polymers (e.g., poly(3,4-ethylenedioxythiophene) polystyrene sulfonate), two-dimensional materials (e.g., molybdenum disulfide), two-dimensional inorganic compounds with layers a few atoms thick (e.g., MXenes (e.g., Ti2C3)). In some exemplary embodiments, electrodes are constructed with electrodeposition of conductive polymers including polypyrrole, polyethyleneimine, and polyaniline by application of a constant voltage or a voltage range that is repeatedly scanned over a controlled amount of time; electrodes are constructed with redox active materials including mediators or organic dyes that are co-deposited onto the electrode during electrodeposition of the conductive polymer; electrodes are constructed with sensor target analyte molecules including cortisol, insulin, levodopa, and proteins that are then eluted from the sensor electrode by application of a constant voltage, a voltage range that is repeatedly scanned, an aqueous solution, or an organic solution for a controlled amount of time, thereby creating a molecularly imprinted polymer electrode that contains recognition cavities that can selectively bind sweat analytes from a finger.In some exemplary embodiments, a device that collects sweat to estimate the concentration of a blood analyte or to utilize the redox reaction of that analyte for energy generation includes a voltage regulation circuit that increases a voltage when connected to an electrocatalytic electrode, which voltage increases an input signal from the electrode and can be stored in an energy storage device, such as a capacitor, a supercapacitor, a battery, or a combination thereof. According to some exemplary embodiments, the device has a voltage regulation circuit coupled to at least the electrodes of the device and configured to collect electrical energy generated by the device and store the energy in the energy storage device.

[0126] In some embodiments of the disclosed technology, a method for generating power using collected sweat analytes includes: placing a device according to the disclosed technology on a skin surface having sweat glands and collecting analytes of biocatalytic reactions in electrodes of the device to generate a current from the electrodes of the device, where sweat from a finger on skin covered with other sweat glands is collected by the device through a sweat-permeable layer of the device; applying pressure to the device via finger pressure sporadically or frequently to generate a current from the electrodes and collecting the energy directly or through a voltage regulating circuit into a storage unit and then discharging it into the storage unit; collecting the energy directly into a highly porous electrode of the device and then discharging it.

[0127] In some embodiments of the disclosed technology, a method for determining a concentration of a biofluid analyte includes: obtaining, for an individual, several measurements of the level of the analyte in the individual's sweat using a self-generated signal or open circuit voltage from a device according to the disclosed technology, where sweat is collected by the device from the individual's finger contacting a sweat-permeable layer of the device; for each of the several measurements of the level of the analyte in the individual's sweat, a voltage signal without external action of a constant voltage or current can be obtained by discharging between the anode and cathode through a load, typically a resistor with known resistance; for each of the several measurements of the level of the analyte in the individual's sweat, discharging the device according to the disclosed technology (BFC) releases energy that is conditioned, stored, and / or directed to direct power electronics that obtain a signal from the electrodes.

[0128] In some embodiments of the disclosed technology, a method for determining a blood / sweat / ISF analyte concentration includes: obtaining, for an individual, several measurements of an analyte level in the individual's sweat using a signal from a sensor of a device according to the disclosed technology, where the sweat is collected by the device from the individual's finger contacting a sweat-permeable layer of the device; obtaining a measurement of the analyte concentration in the individual's biological fluid for each of the several measurements of the analyte level in the individual's sweat; obtaining an exponent parameter, a multiplier parameter, and an intercept parameter for the dependence between the obtained measurements of the analyte concentration in the individual's blood and the obtained measurements of the analyte level in the individual's sweat; converting the new measurements of the analyte level in the individual's sweat into an estimate of the individual's blood biological fluid concentration using the exponent parameter, multiplier parameter, and intercept parameter.

[0129] Figures 9A-9C show the in vitro calibration curves of the glucose sensor. Figure 9A shows the chronoamperograms of successive additions of 50 μM glucose in 0.1 M PBS pH 7 for 60 seconds at -0.2 V. Figure 9B shows the calibration plots obtained from these chronoamperograms. Figure 9C shows the calibration plot of current signal versus blood glucose in mg / dl.

[0130] Figures 10A-10E show the optimization of the hand-washing process through three repeated experiments using different glucose sensors. Specifically, Figure 10A shows contacting the sensor without washing the hands, Figure 10B shows contacting the sensor after washing with soap for 10 seconds, Figure 10C shows contacting the sensor after washing without soap for 20 seconds, and Figure 10D shows contacting the sensor after continuously swiping with a wet tissue for 20 seconds. Figure 10E shows a bar graph of the optimization of the hand-washing process.

[0131] Figures 11A-11F show optimization of contact time. Specifically, Figure 11A shows contacting the sensor for 10 seconds, Figure 11B shows contacting the sensor for 30 seconds, Figure 11C shows contacting the sensor for 1 minute, Figure 11D shows contacting the sensor for 3 minutes, and Figure 11E shows contacting the sensor for 5 minutes. Figure 11F shows a response vs. contact time plot, with the optimal contact time being 1 minute.

[0132] 12A-12B show the stability of the individual factors, slopes (a), and intercepts (b) for two subjects (FIGS. 12A, 12B) over a four-week period with 95% confidence bands.

[0133] Customized calibration software assignment

[0134] An initial calibration is obtained using equations 1-3 and the individual values ​​of sweat are calculated in the software where the corresponding blood glucose concentration is calculated. Once or twice a month, new verified data is inserted into the software for the moving average calculation. This new inserted value is first validated (e.g., outlier detection) and, if the value is within the expected range, it can be included in the initial calculated calibration curve to obtain new averaged parameters. If the inserted blood value is outside the confidence interval, it is rejected and a new input is requested. If the software rejects three values ​​consecutively, the software indicates the need for a completely new calibration plot (Figure 13). This protocol ensures the automation of the individual mathematical processing and the high quality of the output values. The friendly software has been well accepted by patients and has shown good performance and readout values.

[0135] FIG. 13 shows a flow chart for calibrating and analyzing sweat glucose signals for blood glucose concentration using a fingertip touch-based sensor. First, an initial calibration is obtained. Then, the sweat values ​​are calculated in the software and the corresponding blood glucose concentration is calculated. Once a month, new verified data is inserted into the software for the implementation of the moving average technique. In the moving average method, new averaged parameters are calculated using the new data and the previously loaded data. For that, the user must input the blood values ​​corresponding to the sweat measurements. The blood values ​​are first verified by the software (e.g., outlier detection) and if the value is not in the expected range, it will be included in the initially calculated calibration curve, which results in a new averaged parameter. If the inserted blood value is not in the expected range, another value is requested and if the input is rejected by the software three times, a completely new calibration must be realized. This protocol ensures the automation of the mathematical processing and the quality of the output values.

[0136] Figure 14 shows box plots of the mean absolute relative difference (MARD) for consecutive measurements during the day for three subjects. Shown are the mean (diamonds), median (horizontal line within the box), 25th and 75th percentiles (bottom and top of the box), and minimum and maximum values ​​(whiskers).

[0137] 15A-15B show an exemplary data processing protocol for personalized conversion equations. Sweat glucose is monitored twice a day for four days for two subjects. Before each analysis, the user's blood glucose level is measured using a commercially available blood glucose meter. The individual values ​​of the signal from sweat for each day are correlated with the blood values ​​generating a linear plot with specific slope and intercept values ​​(a-d). The slopes and intercepts obtained for each day are then averaged to generate a personalized equation for each user (ei-iii). Once such a personalized conversion equation is obtained, the current signal from the glucose in sweat is used for the direct conversion of the signal to blood glucose levels.

[0138] Figures 16A-16C show all-day sweat glucose measurements. After three meals during the entire day, glucose levels in sweat are collected from the fingertip using a touch sensor device. The signal obtained from the sweat sensor is directly converted to blood glucose levels using a personalized conversion equation from each user. Correlation and Pearson's r values ​​are shown in (ii).

[0139] 17 shows the application of a fingertip sweat sensor. In some implementations, the application of the data processing methodology can be combined with several biosensors, including but not limited to: a levodopa biosensor modified with tyrosinase enzyme, or a non-enzymatic sensor modified via voltammetry techniques, a lactate biosensor modified via lactate oxidase enzyme (or other recognition element), a cortisol biosensor modified via molecular imprint polymerization (MIP) (or other recognition element), a ketone body biosensor using a β-hydroxybutyrate dehydrogenase enzyme modified sensor (or other recognition element), a glucose biosensor using a glucose oxidase enzyme (or other recognition element), a THC sensor using either nanoparticles, CNTs, or MIP modified sensors (or other recognition elements), illicit drugs such as cocaine using bare carbon electrodes (or other recognition elements), and alcohol using the enzyme alcohol oxidase (or other recognition element).

[0140] The disclosed technology may be implemented in some embodiments to provide a data processing approach to correlate sweat analyte responses of biomarkers in natural passive sweating with their blood concentrations. New algorithms address inter-individual variability for accurate conversion of these biomarkers to blood values. Such new personalized data processing is combined with touch-based fingertip sweat analysis. A glucose oxidase-based biosensor is used to measure sweat glucose, and a molecularly imprinted polymer (MIP)-based sweat sensor device is used for cortisol monitoring. The sweat collection device includes a biosensor realized by screen printing, sputtering, inkjet, or any other suitable sensor fabrication technology, and is covered by a sweat collection layer including, but not limited to, hydrogels such as PVA, agarose, or glycerol. Passive sweat is collected from the skin upon direct contact with the sweat collection layer. After contacting the skin for a period of time, the collected sweat diffuses through the hydrogel layer and reaches the recognition layer where the analyte is measured. Several sensing techniques can be used for analyte determination, including, but not limited to, electrochemical sensors, affinity sensors, and optical sensors. After data acquisition, a personalized correlation equation can be determined. For this, data are acquired over several days and validated with an appropriate approach. For example, sweat glucose measurements can be validated using a commercially available blood glucose meter. Blood samples are taken and analyzed before each measurement for a validation step. After data collection, the linear slopes and intercepts obtained each day are averaged and a personalized universal equation is derived to directly convert the signal intensity to blood concentration. Demonstration of such a device and data processing is realized by measuring the glucose level in sweat collected from a fingertip. As mentioned above, the working electrode of a screen-printed three-electrode system is modified with the enzyme glucose oxidase, and a polyvinyl alcohol (PVA) hydrogel is placed on top of the modified sensor to act as a sweat collection layer. Sweat is collected from the fingertip after proper washing of the hands, for example, during a one-minute contact. After collection, the sweat glucose signal is obtained by chronoamperometry.Signals are obtained twice daily for a week and validated against a commercially available blood glucometer. As described above, a linear correlation between the two points (sweat and blood glucose) is obtained for each day of analysis and an averaged slope and intercept are calculated for the user. These personalized values ​​represent individual sweat parameters such as sweat rate and composition. A personalized general formula is then used to directly convert the sensor signal to a blood glucose level. Furthermore, the advantages of this methodology can be extended to access analytes from fingertip sweat such as levodopa, lactate, alcohol, illicit drugs, tetrahydrocannabinol (THC), and ketone bodies by simply modifying the electrode surface sufficient for the analytes.

[0141] As mentioned above, sweat cortisol levels are also measured by touching the PVA gel with a fingertip for 30 seconds after washing hands for 2 minutes. The cortisol sensor includes a molecularly imprinted polymer (MIP) layer containing a signal indicator (e.g., any material with redox properties such as Prussian blue, ferrocene, methylene blue, or others) and a cavity for cortisol detection, facilitating a label-free MIP sensor that does not require additional external signal indicators for measurement with high selectivity. The current response can be measured using chronoamperometry after a 2-minute incubation time to obtain the binding process between the MIP layer and cortisol.

[0142] The disclosed technology may be implemented in some embodiments to provide a new procedure for the conversion of sweat to blood signals. In some implementations, the application of the new method uses a fingertip sweat sensor for glucose or cortisol monitoring. Current sweat sensors rely on strong motion, heat or chemical stimulation to sample sweat, and these current protocols require time, energy and power consumption. The disclosed technology relies on the processing of signals obtained by passive natural sweat collection without the need to perform motion or any additional sweat stimulation steps. Sweat is collected when a collection hydrogel located on the sensing area contacts the skin, and the collected sweat diffuses through the gel to the sensor, where the sweat analytes are measured. In some implementations, there is a possibility of mathematical applications by collecting sweat from the fingertip upon touch. Sweat glucose and cortisol are measured by chronoamperometry, and the total time for analysis is 2 minutes, including 1 minute of sweat sampling and 1 minute of sweat detection. The new data processing ensures that individual differences in sweat rate or skin characteristics are taken into account. Previous studies have yielded conflicting discussions on the correlation of sweat analytes (such as glucose, cortisol, lactate, etc.) with blood concentrations. The differences in previous results were mostly correlated with the sweat collection step and the resulting data processing. In some implementations, a method for sweat analysis includes a collection step, a sensing step, and a processing step. The disclosed technology can be implemented in some embodiments to provide a reliable non-invasive option for frequent monitoring of analytes such as levodopa, glucose, ketone bodies, lactate, alcohol, illicit drugs, tetrahydrocannabinol (THC), and cortisol. Existing commercially available glucose meters are invasive and require finger-prick blood testing protocols that are inconvenient and painful for repeated frequent testing. The new touch-based glucose test enables such frequent glucose measurements, eliminating the need for regular blood measurements and verification. The simplicity and speed of the new touch-based bloodless finger-prick assay has considerable potential for reliable and frequent self-testing of glucose towards improved diabetes management.On the other hand, there are no commercially available tests for cortisol detection. Our method can easily convert glucose and cortisol levels in sweat to blood glucose values ​​by a simple touch with the fingertip without the need for any invasive and sweat induction protocols. For this, data are acquired daily and validated with an appropriate approach. For example, sweat glucose determination can be validated using a commercially available blood glucometer, and cortisol can be validated using an affinity test (immunosensor). Initial data collection is used to estimate the individual slope and intercept, and these personal factors can be used over a period of weeks without the need for parallel blood tests. Thus, a personalized universal equation is used to directly convert sweat signal intensity to blood concentrations (Figures 15A-15B).

[0143] The disclosed technology may be implemented in some embodiments to provide a new methodology that can be used to convert sweat biomarker measurements into reliable estimates of blood concentrations based on personalized data processing that takes into account inter-individual variability. For this, a non-invasive touch-based sweat sensor is used to measure sweat analytes. A biosensor covered by a sweat collection layer is used to determine sweat analytes in natural sweat (Figures 1A-1F). A screen-printed electrochemical sensor is modified with an enzyme glucose oxidase or cortisol imprint layer and covered with a layer of PVA hydrogel. The hydrogel layer is capable of collecting natural sweat upon contact with the body. Sweat from the fingertip is used for the analysis. Upon touch, sweat from the finger accumulates in the hydrogel over a period of time, diffuses through the PVA gel, and reaches the sensing layer of the electrode where it is measured. Chronoamperometry is used to monitor glucose and cortisol concentrations in freshly collected sweat. The signals obtained for each user are then processed to build an individualized calibration to recover blood glucose and cortisol values. For personalized calibration, before the test, a commercially available glucometer or immunoassay test is used to measure the user's blood glucose and cortisol levels, respectively. Then, each user uses the device to test their sweat glucose or cortisol levels. This procedure is repeated twice a day for several days, correlating the individual values ​​of the signal from the sweat for each day with the blood values ​​generating a linear plot with specific slope and intercept values. The slopes and intercepts obtained for each day are then averaged, generating a personalized equation for each user (Figures 15A-15B). Once such a personal conversion equation is obtained, the current signal from the analytes in the sweat can be directly converted to blood glucose values ​​(Figures 16A-16C). Simple software using moving average calculations can be implemented on the electronics for autonomous data processing (Figure 13). First, an initial calibration is obtained. Then, the sweat values ​​are calculated in the software and the corresponding blood glucose concentration is calculated. Once a month, new verified data is inserted against the software for the implementation of the moving average technique.In the moving average method, new averaged parameters are calculated using new and previously loaded data. For that, the user must input the blood values ​​corresponding to the sweat measurements. The blood values ​​are first verified by the software (e.g., outlier detection), and if the value is not in the expected range, it is included in the initially calculated calibration curve, thereby resulting in a new averaged parameter. If the inserted blood value is not in the expected range, another value is requested, and if the input is rejected three times by the software, a completely new calibration must be realized. This protocol ensures the automation of the mathematical processing and the quality of the output values. This new sweat platform and correlation methodology can be converted for the analysis of any sweat biomarker (e.g., but not limited to, levodopa, lactate, alcohol, illicit drugs, tetrahydrocannabinol (THC), ketone bodies, and cortisol) (Figure 17). More complex analyses can be performed by loading different reagents into the sweat collection gel itself. Sweat from different parts of the body can also be collected using wearable epidermal platforms for devices such as tattoos, textiles, or accessories (watches, headbands, glasses, etc.) Different hydrophilic hydrogels can be used as sweat collecting layers as long as their morphology allows for rapid diffusion and stability.

[0144] The disclosed technology may be implemented in some embodiments to provide diabetes management methods and devices. Diabetes prevalence has exponentially increased the need for extensive research on non-invasive approaches for glucose monitoring. Candidates to replace current blood fingertip glucose sensors include biosensors based on saliva, tears, sweat, and ISF as substitutes for blood. Among these biofluids, sweat has attracted greater attention due to its favorable composition and ease of access. However, despite the publication of several sweat glucose sensors, there are various reports on the correlation of sweat and blood glucose levels. The disclosed technology may be implemented in some embodiments to provide a novel combination of finger sweat sampling sensor and simple algorithm for sweat-to-blood glucose conversion and normalization. The non-invasive nature of finger sweat analysis eliminates the variation of sweat characteristics due to different sweat collection methods in addition to increasing patient compliance and facilitating better glucose management. Thus, a finger sweat touch-based glucose sensor can be used to measure sweat glucose from diabetic patients, and blood validation values ​​can be used to generate individualized equations for signal conversion, with widely different slope and intercept values ​​obtained for different subjects to reflect their different sweat rates, compositions, and skin characteristics. Such variations between individuals may be related to age, sex, or race. Once the personalized transformation is established, it is used to train the system and process future results. Such system training resulted in substantially improved accuracy with a Pearson correlation coefficient (Pr) higher than 0.95 and an overall mean absolute relative difference (MARD) of 7.79%, with 100% of paired points lying within the A+B region of the Clark Error Grid (CEG). The glucose detection protocol utilizes fast sweat rate on the fingertip for a rapid glucose assay of natural sweat without the need for physical activity or iontophoresis or thermal sweat stimulation protocols, and the new personalized sweat-to-blood transformation allows different sweat components to be correlated to eliminate variables such as sweat rate, composition, and skin type.

[0145] The disclosed technology may be implemented in some embodiments to provide a drug detection method and device. Driving under the influence of illegal or illicit drugs, such as cannabis and alcohol, has become one of the major safety concerns due to the strong synergistic effects of these substances. Therefore, rapid on-site testing of such substances is necessary to reduce the risk of road accidents. Thus, the disclosed technology can contribute to accurate and rapid distributed detection of drugs using finger sweat sensors combined with mathematical approaches. The disclosed technology can be used as a personal safety system for car ignition, where the finger sweat sensor is directly integrated to the car ignition, including but not limited to the on / off button, car key, etc. Multiple sweat drug molecules can be detected simultaneously for drug screening and identification. The software used for such personalized quantification of drugs can include a drug database for identifying substances in sweat. In addition to facilitating such important and necessary applications for self-monitoring for safety, the disclosed technology allows police officers to screen drivers during traffic stops, addressing the growing concern of drug-impaired driving.

[0146] The disclosed technology may be implemented in some embodiments to provide sweat biomarker monitoring methods and devices. Individualized processing of touch-based fingertip sweat assays provides simplified and accurate tracking of key sweat biomarkers such as levodopa, cortisol, alcohol, lactate, ketone bodies, or uric acid, as well as illicit drugs or tetrahydrocannabinol (THC). Tracking fluctuations in cortisol levels is important in understanding the body's endocrine response to stress stimuli. Traditional cortisol sensing relies on centralized laboratory settings, while wearable cortisol sensors are limited to slow and complex assays. The disclosed technology may be implemented in some embodiments to provide a simple touch-based molecularly imprinted polymer (MIP) sensor for rapid cortisol detection. The sensor easily samples natural sweat from the fingertip onto a cortisol-imprinted polypyrrole with embedded Prussian blue redox probe, eliminating the need for stressful and lengthy sweat extraction procedures. By eliminating time lag, such a rapid (3.5 min) fingertip assay allows for the capture of rapid fluctuations in cortisol levels compared to previous methods. Such advantages are demonstrated by tracking cortisol responses throughout the diurnal circadian rhythm along with gold standard immunoassay validation. Rapid contact-based cortisol sensors offer an attractive, accessible, and less stressful means for quantitative stress management.

[0147] While current methodologies for sweat glucose and cortisol analysis involve either artificial sweat stimulation or exercise, the disclosed technology provides a fast, secure, and reliable methodology for sweat collection, measurement (MIP), and personalized data processing. Correlating sweat biomarker values ​​with corresponding blood values ​​is a current challenge for the sweat sensor industry, and the new methodology disclosed herein makes it possible to account for inter-individual variability for accurate estimation of blood concentrations.

[0148] Figures 18A-18F show an example of a molecularly imprinted polymer (MIP)-based sensor for rapid, stress-free cortisol sensing. Figure 18A shows the synthesis of a MIP layer for cortisol sensing: (a) PB, cortisol, and pyrrole are co-electrodeposited onto a printed carbon electrode; (b) the captured cortisol template is eluted from the polymerized PPy; (c) the corresponding MIP recognition layer after cortisol elution, cortisol-specific cavities are formed in the electrode. Figure 18B shows the sensing mechanism of the MIP. The anodic current from the oxidation of embedded PB decreases after binding of cortisol to the MIP. Figure 18C shows a touch-based fingertip cortisol sensor: (a) a photograph demonstrating a single-touch sensor application; (b) a diagram of the sensing mechanism in which cortisol from accumulated finger sweat diffuses through the hydrogel onto the MIP electrode; (c) a structural diagram of the fingertip cortisol sensor (inset) along with a cryogenic scanning electron microscope (cryo-SEM) image of the porous PVA hydrogel. FIG. 18D shows a stretchable epidermal cortisol patch: (a) the structure of the stretchable sensor, where the sensor accommodates stretchable interconnects and substrate, a shape-defining scaffolding layer, and a stretchable polymer insulator; (b) a photograph demonstrating the use of the patch on the skin after sweat generation from an exercise session; (c) the sensing mechanism of the epidermal cortisol patch, where sweat directly interacts with the MIP electrodes. FIG. 18E shows the fluctuation of cortisol through the circadian cycle. FIG. 18F shows the induction of cortisol secretion by acute physical stimulation.

[0149] Figures 19A-19N show the optimization and calibration of MIP cortisol sensing in various media. Figure 19A shows the interaction of cortisol at the MIP electrode compared to the lack of interaction at the NIP electrode. Figure 19B shows the optimization of the incubation time before sensing in PBS media. A 2 minute incubation is determined to be the most efficient and accurate incubation time for cortisol to interact with the MIP electrode. Figure 19C shows the electrochemical response of the MIP sensor to different cortisol concentrations in PBS. Figure 19D shows the corresponding calibration curve showing the logarithmic response of polar current to cortisol within the detection limit. Figure 19E ... -9Figure 19F shows the response of the MIP cortisol sensor in PBS to the addition of lactate, glucose, ascorbic acid, uric acid, acetaminophen, and urea, showing no change in response, followed by 1 × 10 -6 100 μg of cortisol, which shows a clear response. Figure 19G shows the response of the NIP-based sensor to different concentrations of cortisol in PBS. Figure 19H shows the one-touch cortisol sensing procedure. A finger is placed on the AS-based PVA hydrogel-covered MIP sensor for 30 seconds, followed by a 2-minute incubation time and measurement. Figure 19I shows the optimization of the incubation time for cortisol to interact with the PVA hydrogel-coated MIP sensor in AS. Figure 19J shows the optimization of the contact time of the finger on the gel in the one-touch cortisol sensing procedure. Figure 19K shows the response of the touched NIP electrode, showing no response to finger sweat. Figure 19L shows the CA of the MIP electrode touched by the covered finger, showing no sensor response to a simple pressing action. Figure 19M shows the CA response of the MIP cortisol sensor using the AS-based PVA hydrogel with different cortisol concentrations. FIG. 19N shows the corresponding calibration curve, showing the log current-concentration dependence of the signal.

[0150] Figures 20A-20F show examples of endogenous cortisol monitoring. Figure 20A shows circadian rhythms and cortisol levels. Cortisol levels are found to be higher in the morning and decrease at night. Figure 20B shows the protocol used for sweat finger analysis. Sweat is collected in a collection gel for 30 seconds, then a 2-minute incubation time is allowed for MIP interaction with the analyte, after which the signal is acquired using a handheld potentiostat (scale bar: 1 cm). Figure 20C shows CA of sweat cortisol response on the MIP-modified electrode for subjects 1-3. The solid black line corresponds to the sweat collection gel background, the red line is the cortisol signal measured in the morning, and the blue line corresponds to the signal measured in the evening. Figure 20D shows the electrochemical signal (solid colors) obtained from the sweat finger sensor (red is the signal obtained during the morning and blue in the evening), and validation of the immunosensor response (hatched bars) with sweat collected by pilocarpine IP stimulation. Figure 20E shows cortisol responses during the morning and evening obtained using finger sweat sensors for seven patients. Figure 20F shows continuous cortisol monitoring during the day using finger sweat sensors for three subjects. Subjects b and c included a 30-minute exercise routine (indoor cycling) at 1:00 pm and 5:00 pm, respectively.

[0151] Figures 21A-21F show examples of cortisol sensing during acute stimulation with CPT. Figure 21A shows the release of cortisol in natural sweat from fingertip sweat pores into the hydrogel. Figure 21B shows the timeline of the sensing sequence during ice-water CPT stimulation. The subject's hand is immersed in ice water for 3 min, while the subject's other hand is sampled every 5 min with a touch-based cortisol sensor (scale bar: 1 cm). Figures 21C-21E show the change in cortisol concentration in three subjects during 20 min after CPT: (a) generally, cortisol levels peak at the 10 min mark, (b) their corresponding amperograms as blanks at 0 min and 10 min after CPT. Figure 21F shows the change in cortisol levels in seven subjects at 0 min and 10 min after CPT stimulation.

[0152] Figures 22A-22E show an example of in vivo cortisol detection using a wearable sensor patch. Figure 22A shows a schematic of the adapted protocol used for on-body testing, which relies on applying the sensor to the subject's forearm after 15 min of indoor cycling exercise, followed by 2 min of incubation and 1 min of CA (scale bar: 1 cm). Figure 22B shows the designed stretchable wearable electrode during stretching and bending (scale bar: 1 cm). Figures 22C(a)-(d) show the tensile strength of a 1.0 mm [Fe(CN)6] electrode while undergoing repeated bending (Figures 22C(a),(b)) and stretching (Figures 22C(c),(d)). 3- / 4- Figure 22D shows the CV and corresponding peak current of the cortisol sensor patch at 7 am (red curve) and 5 pm (blue curve). Figure 22E shows the correlation between the MIP-based wearable cortisol sensor and the immunosensor for detection of cortisol concentration in human sweat.

[0153] The disclosed technology may be implemented in some embodiments to provide touch-based, stress-free cortisol sensing methods and devices.

[0154] Tracking fluctuations in cortisol levels is important in understanding the body's endocrine response to stress stimuli. Traditional cortisol sensing relies on a centralized laboratory setting, while wearable cortisol sensors are limited to slow and complicated assays. Here, we describe a touch-based, noninvasive molecularly imprinted polymer (MIP) electrochemical sensor for fast, simple, reliable, and stress-free detection of sweat cortisol. The sensor easily measures fingertip sweat cortisol via highly selective binding to a cortisol-imprinted electropolymerized polypyrrole coating. The MIP network is embedded in a Prussian blue redox probe that provides direct electrical signaling of the binding event to achieve highly sensitive label-free amperometric detection. Using a highly permeable sweat-wicking porous hydrogel, instantaneously secreted fingertip sweat can be conveniently and rapidly collected without any assistance. By eliminating the time lag, such a rapid (3.5 min) fingertip assay allows for the capture of rapid fluctuations in cortisol levels compared to previous methods. Such advantages are demonstrated by tracking cortisol responses throughout a short cold pressor test and throughout the day's circadian rhythm, along with gold standard immunoassay validation. A stretchable epidermal MIP sensor for directly tracking cortisol in exercise-induced sweat is also described. Rapid contact-based cortisol sensors offer an attractive, accessible, and stress-free means for quantitative stress management.

[0155] Cortisol is a steroid hormone released by the human body in response to psychological and physiological stress, and thus plays a major role in regulating the body's stress response, as well as metabolic and immune responses. Chronic stress, represented by high cortisol levels, is associated with a high risk of anxiety, depression, cardiovascular disease, and weakened immune responses. Therefore, effective, rapid, and reliable cortisol detection is extremely useful in comprehensive self-monitoring, health management, and dynamic stress response profiling toward personalized health care. In a rapidly evolving world where personal health is the center of attention, simple, rapid, distributed testing, and non-invasive monitoring of cortisol are important to provide guidance for personal stress management.

[0156] Cortisol can be found in a variety of biofluids, including saliva, blood, urine, sweat, and interstitial fluid. Traditional detection of cortisol in these biofluids is performed in centralized laboratory settings and relies on competitive immunoassays between target cortisol and enzyme-labeled analytes, followed by optical or electrochemical measurements of the enzymatic reaction products. While offering high sensitivity, such multi-step, complex, and lengthy immunosensing procedures are hardly adaptable to distributed settings or wearable applications. Among cortisol-containing biofluids, sweat and saliva are the most accessible. However, compared to saliva, sweat does not exhibit major matrix and biofouling effects. Thus, recent efforts have been carried out to convert immunosensors for distributed sweat cortisol sensing, including the ability to track the cortisol circadian cycle. However, such competitive immunoassay approaches involve 5 min of sweat stimulation, 15 min of competition time, along with tagging and washing steps that make them impractical for personalized, hassle-free cortisol monitoring. Label-free impedance immunoassay detection has also been proposed for wearable cortisol sensing based on monitoring cortisol binding-induced interfacial changes. The limited stability and high cost of cortisol antibody bioreceptors and enzyme-labeled cortisol are another challenge for wearable and dispersive cortisol immunoassays. Artificial receptors based on molecularly imprinted polymers (MIPs) have been shown to be useful in selective recognition of sweat cortisol. However, such MIP-based sensing generally involves the addition of external redox signaling probes, e.g., ferrocyanide, which limits their on-body operation. These early sweat cortisol sensing protocols require long physical activities or additional chemical iontophoresis (IP) sweat stimulation procedures to generate and collect sweat. Such sweat generation methods may interfere with the user's daily workflow and may induce psychological or physiological stress that alters cortisol levels, resulting in inaccurate stress assessments. Thus, the sweat collection step for cortisol monitoring is a major barrier that impedes the development of simple, rapid, and accurate sweat cortisol sensing.Given the challenges, new stress-free, non-invasive and rapid cortisol sensing methods are urgently needed.

[0157] The disclosed technology may be implemented in some embodiments to provide an effective novel stress-free cortisol sensing platform that allows for fast, reliable, and simple detection of cortisol in sweat via fingertip contact. Natural sweating has been shown to be advantageous for sweat sampling in recent years compared to commonly used active sweat stimulation methods (exercise, heat, and IP). Unlike other body locations, fingers, which have the highest density of eccrine sweat glands, can generate high sweat volumes. The disclosed technology may be implemented in some embodiments to leverage such natural sweat sampling methods to develop new stress testing platforms, relying on highly scalable screen-printed electrodes modified with selective MIP recognition layers. Simple, rapid, and easy-to-use cortisol sensing is realized through a series of material innovations. To rapidly and effectively collect sweat from the fingertip, a highly porous, permeable, and sweat-absorbent polyvinyl alcohol (PVA) hydrogel is developed using sucrose as a water-soluble template to create a porous network (Figure 18C(c) inset). Compared to non-porous hydrogels, such templated porous hydrogels exhibited superior permeability and lower impedance. Thus, natural sweat is easily collected by simple fingertip touch, ensuring that only endogenous cortisol levels are measured compared to kinetic-based contrast sweat cortisol sensors. To enable one-step, rapid, reproducible, sensitive, and selective cortisol sensing, electropolymerized polypyrrole (PPy) MIP electrodes are synthesized in the presence of cortisol as a template, along with Prussian Blue (PB) as an embedded redox probe, thereby eliminating the need for complicated labeling procedures or external redox probes. Subsequent elution of cortisol from the membrane is achieved via peroxidation of PPy, which induces a structural change in the polymer that releases the templated cortisol molecules (Figure 18A). This change is confirmed using various surface characterizations and molecular simulations. Templated elution resulted in surface recognition cavities complementary to the shape and size of the target cortisol molecules.The incorporation of PB within the MIP PPy network results in a "built-in" electrochemical signaling probe that eliminates the need for an external redox probe, thereby greatly simplifying on-body testing compared to common MIP sensors based on such solution-phase redox probes. Thus, the resulting user-friendly cortisol sensor, integrating MIP recognition and built-in PB transduction elements, relies on chronoamperometric measurement (CA) of the PB oxidation current. Selective binding of cortisol within the imprinted cavities results in the blocking of the PB electron transfer pathway, thus resulting in a decrease in the PB oxidation current. The extent of such a decrease in current reflects the sweat cortisol concentration and thus can serve as an analytical signal (Figure 18B). Such a change in current is also confirmed by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The incorporation of a built-in PB redox transducer within the imprinted polymer, and systematic optimization of the experimental parameters, enabled fast (3.5 min) label-free CA cortisol sensing at a low potential of +0.1 V, as determined by CV.

[0158] The resulting MIP-based electrochemical sensing, together with a low-cost, scalable single-use screen-printed fingertip cortisol sensor and compact handheld instrumentation (Figure 18C), provides convenient semi-continuous profiling of changing cortisol levels. To demonstrate the applicability of such a MIP sensing platform, a stretchable epidermal patch (Figure 18D) is also developed for tracking cortisol levels during physical activity. Utilizing the formed stretchable ink and adapting an "island-bridge" structure with skeletal layer reinforcement, the sensor demonstrated stable performance after repeated bending and stretching for in vivo cortisol monitoring applications. The entire "contact-incubate-detect" protocol requires only 3.5 min, which is more than 10 times faster than common cortisol measurements, thus offering a distinct advantage for capturing rapidly fluctuating cortisol levels in response to acute stimuli. Using such a rapid and simple cortisol testing platform, casual and stress-free cortisol sensing can be realized toward tracking cortisol level changes within the circadian cycle (Figure 18E). Fluctuations in cortisol levels during physical stimulation, which are important for altering endogenous cortisol levels and indicating injury, fatigue, and dehydration / malnutrition, can also be captured using such a sensing platform (Figure 18F). Thus, coupling the simplicity and speed of touch-based fingertip sweat analysis with label-free MIP-based electronic detection enables dynamic stress response profiling towards personalized healthcare and management of individual stress and mental health.

[0159] Example implementation for optimizing and characterizing touch-based cortisol sensing

[0160] The new MIP detection relies on the selective binding of cortisol to the imprinted PPy membrane to interfere with the electron transfer process of the embedded PB redox probe. Non-imprinted PPy layers lack such recognition ability and show no change in their signal in the presence of cortisol. Figure 19A shows these processes on MIP and non-imprinted polymer (NIP) surfaces. Incoming cortisol molecules can occupy the MIP cavities and interfere with the charge transfer of the PB. The extent of such interactions, and the overall sensitivity of the contact-based assay, depends on the cortisol concentration and needs to be carefully optimized. The interaction between cortisol and the MIP is first studied in 0.1 m phosphate buffer (PBS) to establish a stable, interference-free environment. The effect of incubation time allowing the binding of cortisol to the MIP layer was investigated using a 10 × 10 -6 m cortisol solution from 5 s to 10 min (Figure 19B). The PB signaling current decreases rapidly with increasing incubation time, reflecting increased cortisol interaction with the MIP, until it reaches a near steady state at 2 min.

[0161] Therefore, a 2 minute incubation time is used in all subsequent experiments. -9 m~10×10 -6 The results show that cortisol can be detected over a wide range of concentrations. A well-defined response is observed over this concentration range, resulting in a logarithmic dependence of current level on cortisol concentration. I [nA]=(-38.9±0.4)log(C cortisol ) [×10 -9 m] + (504.1 ± 0.3), R2 = 0.9996 (n = 3), was obtained as a regression equation. -9Note the clear current response to varying concentrations of cortisol at 10 × 100 μm. In contrast, a control experiment with a non-imprinted PPy electrode (Figure 19G) showed a negligible response to a similar addition of cortisol, reflecting the lack of cortisol-binding cavities within the PPy layer. As the MIP sensor is intended for use as a disposable device, sensor reproducibility is important to obtain reliable results. Five MIP cortisol sensors were fabricated, with 10 × 10 -9 The synthesis and detection reproducibility of the MIP electrode was evaluated using its response to 100 μg / ml cortisol. As shown in Figure 19E, the sensor exhibited a highly reproducible cortisol response with a relative standard deviation (RSD) of 1.42%. Selectivity is another important parameter essential for accurate stress profiling. The MIP sweat cortisol sensor provides selective recognition of the cortisol target and provides effective discrimination against a wide variety of common sweat components that may potentially interfere during the transduction step. Figure 19F shows the response of glucose (Glu, 50×10 -6 m), lactic acid (LA, 5 mm), urea (5 mm), ascorbic acid (AA, 50 × 10 -6 m), acetaminophen (AP, 50 × 10 -6 m), and uric acid (UA, 50 × 10 -6 m), followed by incubation with physiologically relevant concentrations of various common interfering species, including 1 × 10 -6 Figure 2 shows the current response of the sensor after addition of m cortisol. While no response is observed in the presence of excess of all of these potentially interfering species, the MIP sensor shows a clear signal in the presence of cortisol, reflecting highly specific MIP cortisol recognition.

[0162] After confirming the repeatability, selectivity, and concentration dependence in the controlled PBS medium, the MIP sensor is further characterized in an artificial sweat (AS) environment in a porous PVA hydrogel to simulate the real sweat sensing application of the touch-based fingertip platform. As shown in Figure 19H, the touch-based sensing is performed by collecting natural sweat from the fingertip by touching the hydrogel for a preselected time, followed by incubation and amperometric detection at +0.1 V. Thus, the contact time and incubation time are evaluated and optimized. 1 × 10 -6 m of cortisol (1×1cm 2 A PVA hydrogel immersed in AS containing 0.1% EDTA (0.1% EDTA, 50 mg) is used to simulate the interaction of cortisol in the hydrogel during the incubation step. The results shown in Figure 19I indicate that an incubation time of 2 min (Notes S3, Supporting Information), corresponding to a sweat volume of 300-30,000 nL, is optimal for the touch-based sensing operation. The contact time is optimized by placing the subject's finger on the hydrogel for variable times prior to the incubation step. As shown in Figure 19J, the current steadily decreased with increasing contact time from 5 s to 30 s and leveled off at longer contact times. Thus, the optimal conditions for touch-based sensing are determined to be an incubation time of 2 min and a touch time of 30 s, which are adopted for subsequent experiments. Consistent with the solution-phase experiments in Figure 19G, no change in the current signal is observed using the NIP surface layer, confirming that the observed response is solely due to the specific MIP recognition of cortisol present in the natural sweat of the fingertip. To ensure that the measured signal reflects the interaction of finger sweat cortisol with the MIP electrode and not due to other mechanical factors (pressure, friction, etc.), the finger is wrapped in Saran plastic film before touching the hydrogel. As shown in Figure 19L, no current response is observed using the wrapped finger, reflecting a lack of sweat transfer into the hydrogel. The quantitative aspect of the MIP fingertip sweat sensor relies on monitoring the decreased PB current in the presence of increasing cortisol concentrations. Using hydrogels soaked in various cortisols in AS, 10 × 10-9 m~1×10 -6 A calibration curve was constructed over the cortisol range of 1000 nA. The resulting current response was logarithmically dependent on cortisol concentration, I[nA]=(-60.3±4.2)log(C cortisol ) [×10 -9 m] + (241.0 ± 10.9), with an R2 of 0.9853 (n = 3) (Figures 19M and 19N). It is noteworthy that the calibrations in Figures 19D and 19N showed good reproducibility in both PBS and AS (with hydrogel environment) over the entire concentration range, considering the similar error ranges shown in both plots. Calibration with the immunosensor is also performed in AS. Such a calibration equation is used in all subsequent in vivo experiments, considering the similarity of the AS medium to natural sweat from a fingertip. Also, a 10 × 10 -9 It should be noted that the clear response to cortisol at 100 μg / mL reflects the remarkable sensitivity of the new fingertip-based MIP cortisol sensor. Such high sensitivity of the cortisol sensor was 38.8 nA log[nm] in PBS and AS, respectively. -1 and 60.31nA[nm] -1 The signal-to-noise ratio (SNR) corresponds to SNR PBS = 116 and SNR AS = 25, and the detection limits are 0.9 and 0.2 × 10 -9 The signal-to-noise ratio and detection limit of the cortisol sensor, as well as the response of the sensor to different pH, temperature, and applied pressure, have also been characterized; the sensor showed good stability using these different operating conditions.

[0163] Cortisol monitoring and validation during the circadian cycle

[0164] The performance of the new cortisol sensor is first evaluated by monitoring the variation of endogenous cortisol levels during the diurnal cycle. Many studies have shown the correlation of cortisol levels with circadian rhythms, with greater cortisol concentrations present in the morning, decreasing during the day, and finally reaching lower levels in the evening (Figure 20A). Dynamic tracking of such cortisol levels in a semi-continuous manner is of considerable importance for assessing an individual's chronic stress level. Therefore, the daily variation of the response of the touch-based sweat cortisol sensor is monitored and validated. Cortisol levels of five patients are measured at 7 am and 5 pm on the same day using fingertip sweat, with validation by immunoassay of pilocarpine-stimulated sweat samples. Using optimized contact and incubation times, the cortisol signal is acquired with a portable potentiostat, requiring 3.5 min for the entire assay (Figure 20B). Cortisol levels range from 86 to 200 × 10 -9Considerable differences in the m range are observed for all subjects using the finger-based cortisol sensor. Figure 20C shows the amperometric cortisol responses of three patients for this morning / evening experiment. The background signal is measured using only sweat collector gel on the sensor surface, and then sweat cortisol measurements are performed in the morning (red curve) and evening (blue curve); a new sensor is used to record each response. Immunosensor-based validation of the fingertip MIP sensor involved stimulated sweat using 10 min of pilocarpine-based iontophoretic (IP) extraction on the user's forearm, followed by 20 min of collection on a PDMS microfluidic epidermal device placed on the sweat stimulation area (Figure S13, Supporting Information). Figure 20D shows the correlation between the MIP-fingertip sweat sensor and the corresponding immunoassay (solid and hatched bars, respectively). These data show a strong correlation (with Pearson r = 0.96) between cortisol sweat concentrations estimated by the fingertip MIP sensor and the corresponding immunoassay. Following the sweat validation study, the new sweat finger sensor is used to monitor the morning and evening cortisol levels of an additional patient, showing clear differences in concentrations during these periods (Figure 20E). The rapid and convenient use of the finger cortisol sensor is demonstrated by monitoring the cortisol levels of several patients throughout the day (Figure 20F). For this, the sensor response is recorded every 2 hours, over a 12-hour period, from 7 am to 7 pm. A gradual decrease in sweat cortisol levels is observed for all patients between morning and evening measurements. Furthermore, exercise during the day momentarily increases circulating cortisol levels (Figure 20F(a), (b)). Thus, two patients are asked to include an exercise routine (including a 30-minute indoor bike ride) in their daily activities during such semi-continuous cortisol monitoring. Sweat cortisol levels increased immediately after exercise and decreased to endogenous levels within 2 hours. It is noteworthy that the sweat-inducing protocol is used only at rest, as natural sweating occurs during exercise. Attempting sweat stimulation during physical activity mixes chemical and exercise-induced sweat. Therefore, to evaluate and validate the exercise stress stimulation, a sweat induction protocol is used immediately after the exercise routine (Figure 20F(b), (c)).This experiment clearly demonstrates the need to have casual sweat cortisol sensing, since cortisol-inducing exercise can have an effect on endogenous cortisol levels. In addition to substantially simpler operation, the use of fingertip sweat MIP sensors has a distinct speed advantage compared to induced sweat immunoassays, which is crucial for capturing instantaneous and sharp fluctuations in cortisol levels.

[0165] Monitoring cortisol responses to acute physical stimuli

[0166] The ability to monitor rapid fluctuations in sweat cortisol in near real-time is demonstrated by using a stress-induced cold pressor test (CPT) (Figures 21A-21F). The CPT, which is performed by immersing the subject's hand in an ice-water container for 3 min, is a common and well-validated laboratory stressor that directly activates the hypothalamic-pituitary-adrenal axis to release cortisol. Therefore, to monitor stress-induced fluctuations in cortisol levels from the CPT, participants are asked to immerse their non-dominant hand in an ice-water bath for 3 min (Figure 21B), after which their cortisol levels are measured every 5 min for up to 20 min using a contact-based cortisol sensor. Each experiment is performed at 5 pm using the same protocol, including a 30 s contact time and a 2 min incubation time. Figures 21C-21E show dynamic cortisol profiles and current signals at 0 min (blank solid line) and 10 min (blue solid line) after removing the hands from ice water for three different subjects, indicating that rapid fluctuations in cortisol levels can be rapidly captured from sweat collected at the fingertip. In all three cases, the maximum concentration of cortisol was reached after 10 min and nearly recovered after 20 min. The touch-based fingertip cortisol sensor offers a distinct advantage for tracking such rapid CPT-induced fluctuations in cortisol levels compared to other cortisol sensing mechanisms that require long biofluid extractions or complicated sensing procedures. It is worth noting that pilocarpine-induced sweat collection procedures longer than 20 min (IP time + collection time) are not used for validation, because the time lag of the corresponding collected sweat samples cannot time-resolve the rapid CPT-induced fluctuations. Furthermore, additional healthy subjects (n = 7) had cortisol concentrations measured before and after 10 min of CPT, as shown in Figure 21F. All participants showed significantly higher cortisol levels after CPT stimulation compared to pre-CPT levels. Overall, the data in Figures 21A-21F clearly demonstrate the ability to detect short-term spikes in circulating cortisol induced by external stimuli. Commonly used IP sweat stimulation techniques are unable to detect such fast fluctuations in sweat cortisol due to their corresponding long time lag.This is shown by the corresponding cortisol concentrations in stimulated sweat after cold water stress.

[0167] A stretchable epidermal cortisol sensor patch for sweat detection

[0168] Besides its use in finger-based natural sweat sensing, the reliable and highly selective MIP-based cortisol sensing mechanism can be easily adapted to various wearable form factors for various sensing applications. As demonstrated from Figures 21A-20F, acute physical stimuli such as exercise can effectively increase cortisol levels in an individual. Finger-based cortisol sensors require the subject to steadily press the sensor for sweat collection. However, because sweat can accumulate rapidly during an exercise session, continuous sensing of cortisol from exercise-induced sweat is possible without an additional collection step, and flexible MIP-based epidermal patches are fabricated using a flexible, stretchable substrate and stretchable screen-printable inks, including serpentine structures and skeletal layer shape confinement to limit deformation on the MIP sensing area (Figure 18D). In particular, the stretchable silver ink is printed as interconnecting "bridges", while the dielectric skeletal layer is printed under the electrodes and contact points as islands to ensure that no strain is applied to the electrodes, thus establishing a stable "island-bridge" configuration. A flexible bi-layer flexible substrate with Ecoflex and polyurethane is fabricated to ensure conformal contact of the sensor to the curved body surface while ensuring substrate bonding with the ink. Past studies have demonstrated the advantageous combination of a flexible substrate with an "island-bridge" configuration, which ensures mechanical durability of the electrodes against rigorous movement. Since the structure and choice of substrate material does not affect the electrochemical performance of the sensor, the epidermal patch shows analytical performance similar to that of the finger-based cortisol sensor, with the addition of excellent mechanical durability. Based on the compelling performance of the flexible cortisol MIP sensor in AS medium, on-body evaluation of the epidermal patch is performed on three patients at 7 am and 5 pm on the same day, when high and low cortisol levels are predicted based on established circadian rhythms. Figure 22A shows the experimental protocol used for the physical test; it involves applying an epidermal patch to the patient's forearm after 15 min of indoor cycling activity while the subject is still moving, and measuring the amperometric response after a 2 min incubation period.

[0169] The mechanical stability of the sensor is first tested using CV with the aim of evaluating the electrochemical behavior of the new MIP sensor during severe mechanical deformation. 3- / 4- The effect of bending and stretching deformation on the electrochemical performance was evaluated using CV in a PBS solution containing a redox probe. The sensor is bent to 90° and repeatedly stretched to 20% strain, and its CV is recorded every 10 cycles to compare the sensor performance throughout the deformation cycles. As a single-use sensor, 50 cycles of deformation are considered critical, and the deformation is performed for 60 cycles to ensure the stability of the sensor. As shown in Figure 22C, the wearable sensor can maintain its stable performance within 60 cycles of bending, resulting in highly reproducible CVs and peak currents throughout this bending experiment (Figure 22C(a), (b)). Similarly, no visible changes in the voltammograms, including the corresponding peak currents, are observed during these 50 stretching deformation cycles (Figure 22C(c), (d)).

[0170] After checking the mechanical and electrical elasticity of such sensors, they are applied in a physical examination. Figure 22D shows the current signals obtained for the three patients, showing a very similar trend for all participants with lower current signals (i.e., higher cortisol levels) in the afternoon compared to the morning results. These clear signals, together with the low noise levels and background signals, demonstrate the reliability of the sweat uptake and the sealed electrical connection. Comparing these current signals with the calibration curve in Figure 19C, morning cortisol concentrations were 324, 405, and 363 × 10, respectively. -9 m, and afternoon concentrations were 63, 78, and 37 × 10 -9m. Immunoassay measurements are also used to verify cortisol concentrations using sweat samples collected from the subjects at the same time. These data show good agreement between the on-body results of the wearable patch sensor and the immunoassay tests. Thus, such epidermal sensor exercise sweat sensing shows great potential towards rapid sensing of cortisol levels in real time, even during highly dynamic physical exercise.

[0171] The disclosed technology can be implemented in some embodiments to provide a simple, label-free, labor-free, and low-cost detection platform for rapid sensing of cortisol concentrations in natural fingertip sweat using electrochemically synthesized MIP thin films with built-in PB redox probes. Using the developed porous PVA hydrogel, cortisol in passive natural sweat can be easily sampled without the need for stress-inducing exercise or lengthy extraction. With short contact and incubation times, the synthesized PPy-based cortisol MIPs enable label-free, rapid, and direct measurement of cortisol concentrations from the reduced current response of the PB redox probe embedded in the polymer network. Such a fast fingertip assay eliminates the time delay characteristic of common cortisol assays, thus enabling near real-time monitoring of rapidly changing cortisol concentrations. This touch-based fingertip sweat sensing platform can be used to monitor long-term cortisol level fluctuations in multiple subjects within the circadian cycle, and measurements can be validated using established immunoassays involving IP-stimulated sweat. By avoiding the need for any stress-inducing activity (e.g., exercise) for sweat sample collection, the new sweat-based MIP-based method provides accurate cortisol measurements in a stress-free manner. In addition, the fast and easy sampling of fingertip sweat allows for the capture of cortisol level fluctuations during acute stimulating events such as CPT. As a platform detection technology, MIP-based detection is also adapted for the form factor of a stretchable and wearable patch for direct detection of sweat cortisol levels during exercise, eliminating sampling time and further facilitating detection speed. The scope of such MIP-based fingertip can be expanded for the detection of other hormones and biomarkers. Further improvements could be achieved by using pH, temperature, and flow sensors in parallel to account for sweat and body parameter fluctuations.Overall, the new MIP-based fingertip cortisol sensing leverages many material innovations and provides a reliable and practical approach for rapid, effortless stress monitoring that can be used to manage personal stress or mental health, guiding future research in this field and thus having a profound impact on the fields of wearable sensors, mobile health, and personalized healthcare.

[0172] In some implementations, finger-based sensor electrodes can be fabricated as follows: Electrodes for finger-based cortisol sensors are fabricated by screen printing using a semi-automated MMP-SPM printer and a custom stainless steel stencil developed using AutoCAD software with dimensions of 12 inches by 12 inches and 75 μm thick. The electrodes are printed layer by layer. First, a silver / silver chloride ink is printed on a poly(ethylene terephthalate) (PET) substrate as the interconnect and reference electrode, followed by a layer of carbon ink as the working and counter electrodes. Each layer is cured in an oven at 80° C. for 10 minutes. Finally, a polymer insulator composed of SEBS dissolved in toluene (35 wt%) is printed on the electrode to define the working electrode area and insulate the exposed interconnect.

[0173] In some implementations, a stretchable sensor patch can be made as follows: A stretchable substrate is manufactured by printing a thin layer of Ecoflex on the adhesive side of a Perme-Roll Lite film. A stretchable silver ink is formulated by mixing SEBS resin (31.5% by weight in toluene) with silver flakes in a planetary mixer at 1800 revolutions per minute (RPM) for 5 minutes. A stretchable carbon ink is formulated by mixing the same SEBS resin, toluene, graphite, and Super-P in a 12:3:8.5:1.5 weight ratio at 2250 RPM for 10 minutes. A dielectric ink is first printed on the Perme-Roll side of the stretchable substrate as a scaffold layer and cured in an oven at 80°C for 10 minutes.

[0174] Stretchable silver is then printed as the interconnects and stretchable carbon is printed as the working and counter electrodes. Both inks are cured in an oven at 80 °C for 5 minutes. Ag / AgCl ink is printed as the reference electrode and cured in an oven at 80 °C for 10 minutes. Finally, SEBS resin is printed to define the electrode areas and insulate the interconnects and cured in an oven at 80 °C for 10 minutes.

[0175] In some embodiments, the molecularly imprinted polymer can be synthesized as follows: The screen-printed electrodes are subjected to 10 cycles (50 mVs) over a potential range of -1.5 to +1.5 V in (0.5 m H2SO4) solution. -1 The sensor is then washed twice with deionized water and dried at room temperature. The fabrication process of the MIP film was carried out using a 0.02 mol L -1 Pyrrole, 5mmolL -1 FeCl3, 5 mmolL -1 K3[Fe(CN)6], 6 mmol L -1 of cortisol, and 0.1 mol L -1 in a PBS solution (pH = 7.4) containing 100 μg of hydrochloric acid at a scan rate of 50 mVs. -1 The electrodes were prepared by electropolymerization using CV in the potential range of -0.2 to +0.9 V for 10 cycles at 50 mVs. After the electropolymerization process, the electrodes were washed twice with deionized water to remove any remaining compounds. Then, the electrodes were washed twice with CV in the potential range of -0.2 to +0.8 V for 20 cycles at 50 mVs in PBS. -1 ), the incorporated cortisol was extracted from the PPy-PB substrate by peroxidizing PPy-PB with CV, creating a complementary cavity.

[0176] For the preparation of NIPs, the same preparation method as that of MIPs is applied, except for the cortisol molecule as the template during the polymerization step. Although the polymerization layer did not contain the template, the PPy overoxidation step is carried out to ensure that other experimental conditions are the same as the MIP sensor. Finally, the prepared NIP-based electrode is washed twice with deionized water and dried at room temperature until use.

[0177] In some embodiments, porous PVA hydrogels can be produced as follows. The production of porous PVA hydrogels is based on previous work with modifications. First, a solution of PVA (MW≈89 000) dissolved in water at a weight ratio of 1:10 and KOH dissolved in water at a weight ratio of 1:5 is prepared. Then, 14 g of KOH solution is added dropwise to 10 g of PVA solution with stirring, followed by dissolving 2.6 g of sucrose in the mixture to form a hydrogel precursor. Then, 15 g of precursor is poured into a Petri dish (diameter≈9 cm) and left in a vacuum desiccator to remove excess water and crosslink until only 1 / 3 of the precursor's weight remains. The crosslinked gel is then immersed in 0.1 m PBS buffer to remove the sucrose template and excess KOH until the gel is at a neutral pH. The gel can then be cut into the desired size and shape and stored in PBS or AS for subsequent use. The resulting hydrogel had a uniform thickness of 400 μm.

[0178] In some embodiments, artificial sweat can be prepared as follows: AS is prepared by dissolving the major sweat components: NaCl (85×10 -3 m), KCL(13×10 -3 m), lactate (17×10 -3 m), and urea (16 × 10 -3 m) is added to the PVA gel. A buffer solution is used in the AS formulation to prevent signal fluctuations caused by changes in sweat pH. For fingertip sweat cortisol testing, the PVA gel is loaded with 40 μL of AS before touching it to the sensor.

[0179] The in vitro sensors can include the following features: The electrochemical performance of all MIP-based sensors is evaluated in 0.1 m PBS (pH 7.4), AS, and PVA gels with each solution. CA is performed under a potential of +0.1 V (vs. Ag / AgCl) for 60 seconds. Calibration curves of MIP- and NIP-based sensing platforms are performed using 1 × 10 mAb in PBS. -9 m~10×10 -6 m, or 10 × 10 in AS -9 m~1×10-6 This is obtained by measuring the cortisol concentration range of 50×10 -6 m glucose, 5 × 10 -3 m lactate, 5 × 10 -3 m urea, 50 x 10 -6 m Ascorbic acid, 50 x 10 -6 m Acetaminophen, 50 x 10 -6 The response to various relevant interfering species, such as uric acid at 1 × 10 m, was measured, and furthermore, in the presence of all the interfering substances, 1 × 10 -6 Selectivity is examined by measuring the response to the addition of 10 μg of cortisol. -9 The mechanical elasticity of the MIP-based wearable sensor is evaluated by measuring its response to cortisol at 1000 nm using five different MIP-based sensors. The mechanical elasticity of the MIP-based wearable sensor was evaluated by transferring it onto a transparent plastic substrate to mimic the flexibility of skin, and measuring a force of 1.0 × 10 after repeated bending at 90° and stretching by 25%. -3 m[Fe(CN)6] 3- / 4- It is assessed by measuring the CV response in solution, which is recorded after every 10 repeated stretches and flexions up to 60 times.

[0180] For circadian rhythm measurements (7 am and 5 pm), each healthy user washed their hands before the experiment and touched the PVA gel for 30 seconds. After a 2 min incubation time, the CA is recorded for 60 seconds at an applied potential of +0.1 V and the concentration is calculated based on the previous calibration plot obtained from the in vitro experiment. Meanwhile, sweat is induced using IP and collected to verify the concentration of cortisol using the immunosensor. Continuous cortisol monitoring is performed in three subjects (one without exercise, two with exercise, for 30 min at 12:30 pm and 4:30 pm), recording the signal every 2 hours from 7 am to 7 pm. A fresh sensor is used for each measurement.

[0181] Three patients participated in the cold pressor test (5pm) by immersing their left hand in a container of iced water for 3 min. After 3 min, participants removed their hand from the iced water and measured cortisol levels using their right hand at 5 min intervals to track cortisol fluctuations. In addition, seven healthy patients contributed to the cold pressor test by measuring before and after 10 min of hand immersion. The measurement procedure was CA, with sequential hand washing, 30 s contact, 2 min incubation, and a fresh sensor used for each measurement.

[0182] High energy return on investment harvesting by passive fingertip sweating

[0183] Self-powered wearable systems relying on bioenergy harvesters generally require excessive energy input from the human body and are highly inefficient when calculating overall energy costs. A harvester independent of the external environment for steady state has not yet been developed. The disclosed technology can be implemented in some embodiments to provide a touch-based lactate biofuel cell that utilizes the high passive sweat rate of the fingertip for bioenergy harvesting. Powered by finger touch, such a harvesting process can continuously collect hundreds of mJ of energy during sleep without active input, representing the most efficient approach compared to any reported on-body bioenergy harvester. To maximize energy harvesting, a complementary piezoelectric generator is integrated under the biofuel cell to further capture mechanical energy from finger pressure. The harvester can rapidly and efficiently power sensors and electrochromic displays to enable independent self-powered sensing. A passive sweat-based harvester establishes a practical, high energy-to-investment return for future self-sustainable electronic systems.

[0184] Wearable electronics have seen tremendous growth over the past decade. Current wearable electronics are primarily powered by miniaturized electrochemical energy storage devices (e.g., batteries, supercapacitors) that have limited energy and power density that cannot power electronics for extended operating times. To address this challenge, researchers have focused on introducing energy harvesters to extend system runtime while reducing energy consumption. Self-powered sensors that autonomously generate signals can reduce system power consumption, but cannot provide enough energy to the electronics for actual measurement or data transmission. Recent advances in energy harvesters have enabled self-sustaining systems that continuously harvest energy from sunlight, movement, temperature gradients, or biofuels to power sensors and electronics intermittently or continuously. However, harvesters based on inconsistent external environments cannot provide energy on command, while mechanical and biochemical energy harvesters require intense movement and involve high mechanical energy investment, making them highly inefficient, inconvenient, and impractical. Therefore, an energy harvester that relies on a passive, constant input from the human body, independent of irregular external environments, movements, or motion, is considered to be the ultimate goal of energy harvesting devices.

[0185] Among all the aforementioned energy harvesters, lactate-based biofuel cells (BFCs) have shown considerable promise as self-powered sensors and bioenergy harvesters for electrically powered electronics. Relying on the high lactate concentration in human sweat, epidermal BFCs can easily generate energy using a lactate oxidase (LOx) bioanode complemented by an oxygen reduction reaction (ORR) on the cathode. However, despite their great potential to power wearable electronic devices, the ability to harness the abundant sweat bioenergy has been hindered by the inherent inaccessibility of natural sweat. Although sweat is generated autonomously from the human body in most of the epidermal space, its flow rate is extremely low to realize efficient bioenergy harvesting. Thus, wearable BFCs generally require intense and prolonged exercise before a significant amount of sweat can accumulate on the bioelectrodes for power generation. Although epidermal BFCs with high power density have been reported, the operation of such BFC-powered systems requires a large amount of energy input towards continuous sweat generation, resulting in extremely low conversion efficiency (<1%) when considering the mechanical energy input (Table 1 below). Therefore, alternative approaches to access sweat biofuel without intensive exercise are urgently needed for the routine and practical application of BFCs in wearable systems.

[0186] The disclosed technology can be implemented in some embodiments to provide a high energy return on investment (EROI) harvesting device that is powered by natural passive fingertip sweat and does not require mechanical input to generate instantaneous power. Optimized for collecting natural sweat from the finger, the disclosed technology can be implemented in some embodiments to provide flexible, porous, water-absorbing three-dimensional (3D) carbon nanotube (CNT) foams (e.g., some examples shown in Figures 28-30 and 32) that are implemented as BFC electrodes (e.g., anode and cathode electrodes). The 3D CNT foam BFC electrodes can be decorated with LOx and nanoporous Pt on the anode and cathode sites for lactate oxidation and oxygen reduction, respectively, for bioelectrocatalytic power generation (Figure 23A).

[0187] 23A-23D show diagrams and data plots illustrating an exemplary embodiment and implementation of the operation of a touch-based biofuel cell (BFC) and bioenergy harvesting system according to the present technology. FIG. 23A shows a schematic of an exemplary analyte harvesting BFC device designed for BFC harvesting of lactate biofuel from natural finger sweat, including a LOx-modified anode and a Pt-modified cathode formed from 3D CNT foam and positioned below an exemplary embodiment of a sweat-permeable layer (e.g., a cast porous PVA hydrogel) and above an exemplary lead zirconate titanate (PZT) chip. FIG. 23B shows optical and SEM images of the cast porous PVA hydrogel and CNT foam. FIG. 23C shows diagrams of three operating conditions of the BFC harvesting energy from (i) passive continuous contact, (ii) active pressing, and (iii) repeated active pressing. Figure 23D shows an exploded view of an exemplary integrated BFC piezoelectric energy generating sample collection device configured to generate chemical and mechanical energy harvested from constituents in natural sweat transferred across a sweat-permeable layer from finger pressure on the device. Figure 23E shows photographic images of (i) a self-powered sensing system with integrated harvester, sensor, and ECD, and (ii) a device for sensing sweat composition from natural finger sweat.

[0188] 23A and 23D, an example of a touch-based biofuel cell and bioenergy harvesting (BFC-BH) system 2300 according to some embodiments of the disclosed technology is shown. The example BFC-BH system 2300 can include a biofuel cell (BFC) assembly 2310 integrated with a piezoelectric energy generating (PENG) assembly 2320 and an example embodiment of a sweat-permeable layer 115 shown in FIG. 23D as a sweat-permeable layer 2305. The BFC assembly has two or more electrodes 2314 including an anode electrode 2314A and a cathode electrode 2314C, which are coupled to a current collector 2312. The current collector 2312 can include two or more conductive material structures (e.g., configured to have a planar or other geometric shape) to electrically couple at least one conductive material structure to the anode and cathode, respectively. In some embodiments, the BFC assembly 2310 can include a substrate 2311 on which a current collector 2312 is disposed, and the current collector 2312 is bonded to an electrode 2314. The sweat-permeable layer 2305 is configured to be bonded to the plurality of electrodes 2314 and can include a flexible, porous hydrogel material, such as a PVA gel embodiment (described herein). In some implementations, the plurality of electrodes 2314 can include a flexible, porous, water-imbibed three-dimensional (3D) carbon nanotube (CNT) foam, as shown in FIGS. 28-30 and 32. In some implementations, the anode 2314A can be modified with a substance configured to facilitate a reaction with a target analyte to generate a detectable electrical signal, such as an enzyme and / or a mediator, including LOx in the example shown in FIG. 23A. The cathode 2314C may include nanoporous Pt (e.g., Pt particles or Pt coated particles embedded in exemplary 3D CNT foam) to facilitate lactate oxidation and oxygen reduction, respectively, for bioelectrocatalytic power generation. In some implementations, the sweat permeable layer 2305 may include, for example, a porous polyvinyl alcohol (PVA) hydrogel that can facilitate continuous sweat transfer from the fingertip to the BFC electrode while removing the Laplace pressure of sweat droplets to retain fuel for continuous harvesting.The PENG assembly 2320 includes a piezoelectric substrate or chip 2322 that can undergo non-destructive mechanical deformation when the BFC-BH system 2300 is depressed by a user's fingertip, from which electrical energy is generated. In some embodiments, the piezoelectric chip 2322 includes a PZT. The piezoelectric chip 2322 is located directly below the BFC energy harvester assembly, e.g., below the optional substrate 2311, and is actuated by a slight finger depression. The power generated in the piezoelectric material (e.g., PZT) increases with increasing pressure, frequency, and deformation. In some embodiments, the PENG assembly 2320 optionally includes two or more spacers 2326 located below the piezoelectric chip 2322 and above an optional base substrate (not shown). In implementations, the spacers 2326 can be used to control and affect the power generated by the piezoelectric material 2322, for example, based on controlling the thickness of the spacers 2326.

[0189] FIG. 24 shows data from an exemplary in vitro and in vivo characterization of an exemplary touch-based BFC and bioenergy harvesting system: (a) areal power density of the BFC at different lactate concentrations (1, 5, 10, 15, 20, 25 mM) characterized using LSV at 5 mV / s; (b) areal power density of the BFC at different potentials characterized via 10 min CA; (c) (i) power of the BFC at 0.4 V in PBS with different lactate concentrations, (ii) power calibration plot of the BFC at different lactate concentrations in PBS and in PVA gel; (d) comparison of the power of the BFC touched by a coated finger for 3 min and by a bare finger for 3 min; (e) power profile of the BFC during continuous pressing for 30 min using anodes decorated with and without LOx enzyme; (f) power profile of refueling by pressing the BFC for 3 min after 1 h of rest; (g) power profile of the BFC during pressing for 30 s every 5 min. Fig. 2d-g, Pressurization pressure, 50 kPa; CA voltage, 0.4 V. h, (i) Illustrative diagram of a BFC attached to a finger for long-term continuous energy harvesting. (ii) Power profile during 1 h of typical desk work involving intermittent BFC pressing. (iii) Power profile of a BFC passively harvesting bioenergy from a finger overnight (10 h sleep).

[0190] FIG. 25 shows data from an exemplary optimized embodiment of an exemplary touch-based BFC and bioenergy harvesting system: (a) power profile and energy harvested within 5 minutes of a BFC touched with 50 kPa pressure by a bare finger that was cleaned and waited for various periods before contacting for 30 seconds; (b) power profile and energy harvested within 5 minutes of a BFC touched with a bare finger with different press weights for 30 seconds; (c) power profile and energy harvested within 5 minutes of a BFC touched with 50 kPa pressure by one to three fingers, with contact times of (i) 30 seconds and (ii) 3 minutes; (d) power profile and energy harvested within 5 minutes of a BFC touched with 50 kPa pressure by a bare finger for different times (5 to 180 seconds; i to iv); (e) power profile and energy harvested with one finger with 50 kPa pressure for 5 minutes with different press frequencies.

[0191] FIG. 26 shows data from an exemplary embodiment of a touch-based BFC and integrated harvesting system: (a) a system diagram of an integrated BFC-PZT touch energy harvesting system, including energy boost and conditioning circuits; (b) an example diagram of finding the optimal energy harvesting operating settings; (c) two operating modes based on (i) pressing one pair of integrated harvesters with one finger, (ii) pinching with two fingers, and sandwiching two pairs of integrated harvesters in a sandwich configuration; (d) one BFC harvester pressed with six BPM frequencies (i) charging capacitors with different capacitances, and (ii) charging capacitors with corresponding charging times; (e) (i) 100μ charge times with one PZT harvester, one BFC harvester, and one integrated harvester pressed with six PBM frequencies. f capacitor, (ii) their corresponding charging times; (f) (i) charging a 100 μf capacitor using one integrated harvester pressed at different frequencies, (ii) their corresponding charging times; (g) (i) charging a 100 μf capacitor using only one and two sets of integrated harvesters pressing at six PBM frequencies, (ii) their corresponding charging times; (h) (i) charging a 100 μf capacitor from 2 V to 4 V, (i) using only two BFC harvesters pressing at six PBM frequencies, (ii) their corresponding charging times, (iii) using two integrated BFC-PZT harvesters pressing at 6 BPM frequency, (iv) their corresponding charging times, (v) charging a 220 μf capacitor from 2 V to 3 V using two integrated BFC-PZT harvesters pressing at 6 BPM frequency, (vi) their corresponding charging times.

[0192] 27A-G show diagrams and data plots illustrating an example embodiment and implementation of the operation of a self-powered sensor display system according to the present technology. FIG. 27A shows an exploded view of a device schematic including two pairs of BFC-PZT harvesters, a two-electrode sensor, an ECD panel, and associated MCU and power management circuitry. FIG. 27B shows an example system diagram of a self-powered system. FIG. 27C shows an example of a low-power ECD: (i) a schematic of the exploded view, (ii) readouts on the display panel. FIG. 27D shows (i) a diagram of a two-electrode ion-selective sodium sensor, (ii) calibration and selectivity of the sodium sensor. FIG. 27E shows a photograph of a self-powered sensing system detecting sodium concentration in tap water and 1:100 diluted seawater. FIG. 27F shows (i) a diagram of a two-electrode vitamin-C sensor, (ii) calibration and selectivity of the vitamin-C sensor. FIG. 27G shows an example of the time scale of vitamin C testing after taking a vitamin tablet (top) and corresponding photographic images of the ECD readings at different times after taking the tablet tested using a self-powered sensing system.

[0193] Unlike other body parts, the amount of sweat produced by the fingertips is quite large (80-160g / day). -1 ). Recent reports have demonstrated the advantages of such fingertip natural sweat generation for sweat analysis compared with common sweat stimulation methods (e.g., exercise, iontophoresis, or heat). Such efficient fingertip sweat generation is extremely attractive for powering BFCs without the need for sweat-inducing exercise. A porous polyvinyl alcohol (PVA) hydrogel was further used to eliminate the Laplace pressure of sweat droplets to facilitate continuous sweat transfer from the fingertip to the BFC electrodes, while retaining fuel for continuous collection (Figure 23B, Figure 33). The finger contact-based BFC was able to achieve a 1 cm per minute ... 2Hundreds of mJ of energy can be harvested continuously per minute, or more than 30 mJ of energy per hour can be harvested by a single finger press consuming only 0.5 mJ of mechanical energy, resulting in a high EROI of 6000%; repeated contacts can result in refueling and enhanced convection, further boosting the power to harvest more energy in a shorter time (Figure 23C). Implementing a microgrid design concept for self-powered electronic systems, this contact-based BFC was combined with a PZT piezoelectric generator to further increase the harvesting efficiency from finger presses, thereby achieving synergistic energy collection (Figure 23D). As a practical application, such an efficient hybrid harvester was used to power an electronic sensing system including a vitamin C or sodium ion sensor with a dedicated low-power electrochromic display (ECD) to operate independently from external devices (Figure 23E). Overall, the described touch-based BFC harvester demonstrates extremely high harvesting efficiency and EROI compared to any previously reported on-body bioenergy harvester (Table 1 below). The paradigm shift from "work for power" to "life for power" enhances the practicality of existing on-body bioenergy harvesting technologies and offers new and unique possibilities to establish reliable, independent, next-generation autonomous electronics systems.

[0194] Characterization and optimization

[0195] The fabrication of a touch-based BFC that effectively harnesses the natural fingertip sweat pumping under repeated pressing relies on soft, durable, porous, sweat-wicking CNT foam electrodes. These flexible CNT foam electrodes are prepared in a formulated CNT-elastomer composite using water-soluble particle templating and solvent exchange. Through optimization (Figure 34), a CNT foam-based fingertip BFC was fabricated measuring 1 × 1 cm. 2 The BFC is designed with a total dimension of 5 mVs, where one cathode is paired with two anodes (Figure 23B). The operating conditions of the BFC are first optimized using in vitro testing. BFCs are conventionally designed with a 5 mVs-1 The BFCs are characterized using linear scan voltammetry (LSV) with a scan rate around 0.05 V, which is used to measure the power of the BFCs for various fuel concentrations and peak power potentials (Figure 24(a)). However, such methods cannot accurately indicate the long-term harvesting performance at equilibrium, as the capacitive charging current constitutes a significant portion of the measured power output. Therefore, to accurately evaluate the power and optimal operating conditions of the BFCs, a 10 min extended chronoamperometry (CA) with a potential step from 0.55 V to 0.2 V is used. The open circuit voltage of the cell exhibits a ca value of approximately 0.55 V, coinciding with the onset potentials of the LOx / NQ-driven oxidation and Pt-catalyzed reduction reactions, which were set at -0.2 V and 0.35 V vs Ag / AgCl, respectively (Figures 35-36). A Pt-based ORR is selected as the cathode half-cell on the enzyme biocathode to minimize the risks involved in enzyme immobilization and eliminate the energy harvesting variations due to environmental changes. A potential of 0.40 V corresponds to 43 μW cm in the presence of 15 mM lactate. -2 provided the most favorable performance in terms of power density (based on anode area) of 1 mM (Figure 24(b)). As expected, the response of the BFC to increasing lactate concentrations ranging from 1 mM to 25 mM in both liquid PBS and PVA hydrogel media (under an applied pressure of 50 kPa) tested at the optimized (0.4 V) potential (Figure 24(c)i and ii) resulted in higher bioelectrocatalysis currents. Figure 24(c)ii demonstrates that an additional applied press pressure of 50 kPa using PVA gel results in slightly higher power output compared to PBS media. This behavior, characterized by electrochemical impedance spectroscopy (EIS), is attributed to the low impedance of the porous PVA gel as well as the reduced electrode resistance of the electrode upon application of pressure (Figure 38).

[0196] Figure 24(d) shows an example of a proof-of-concept power response of a touch-based BFC. The power-time profile displays a rapid increase in power to about 30 μW upon pressing the BFC with a bare finger (green part). In comparison, no power generation is observed for a similar contact of the BFC with a coated finger (black part), reflecting the absence of fuel transfer. Such a comparison clearly demonstrates that power generation in the BFC is fully fueled by the natural sweat of the fingertips. Due to differences in sweat rates and lactate concentrations in different individuals, the harvestable power may vary from person to person, giving an advantage of BFC-generated power to individuals with higher fingertip sweat rates (Figures 39-40). The harvesting behavior of the BFC during continuous pressing was further verified over a period of 30 min, which generated more than 20 μW of power per finger and harvested more than 39.5 mJ of power over a period of 30 min (Figure 24(e)). In contrast, the BFC without the LOx enzyme is unable to generate any significant energy. The ability of the contact-based BFC to continuously harvest energy from sweat transferred from a short period of contact (3 min) is shown in Figure 24(f), where the BFC is able to harvest energy over an hour and refuel upon contact with a porous PVA hydrogel. It is worth noting that without an enclosure, the harvested sweat cannot maintain the PVA gel hydrated due to a faster evaporation rate, and the gel is rehydrated every hour (Figure 41). As shown in Figure 24(g), repeatedly and frequently pressing the BFC is beneficial to increase its harvesting power, with the force increasing after each press. Such behavior can be exploited to rapidly increase the harvested power without exerting a constant force on the device, and can be further exploited by using more BFCs for multiple fingers to reach higher power (Figure 42). The simplicity and practicality of this touch-based BFC harvester is demonstrated in different scenarios such as during normal desk work involving typing and mouse clicking (Fig. 24(h) and Fig. 43), or during night sleep where no mechanical input is applied (Fig. 24(i) and Fig. 43).These data demonstrate that the BFC can harvest energy continuously in either scenario without the need for environmental or mechanical energy input, capturing over 28.4 mJ of energy during 1 h of desk work, or up to 389 mJ of energy during 10 h of sleep.

[0197] To further explore and optimize the utilization of the contact-based bioenergy harvesting process, several variables influencing power generation are systematically studied, including sweat accumulation time (after washing and before contact), contact pressure, contact time, number of fingers used, and contact frequency. Towards a practical goal of quickly powering the device shortly after contact, the power and total energy generated during 5 min of contact are monitored and compared. First, the effect of sweat accumulation time before touching the BFC is examined using a time range of 1-10 min and the corresponding power generation is monitored during a 30 s contact time. Although longer waiting times are expected to increase the power due to the accumulation of lactate on the fingertip, no significant difference in power is observed for different waiting times (Figure 25(a)). However, such differences are more pronounced through longer operating times of 30 min, when the stabilized power and total energy harvested from the BFC increased slightly with increasing waiting times (Figure 45). Subsequently, the power generation was monitored at 1 cm while increasing pressures such as 10, 25, 50, and 100 kPa. 2 The effect of applied finger pressure on BFC performance can be shown by touching the device in Figure 25(b). Figure 25(b) shows that stronger pressing force results in higher output, which translates into greater harvested energy in a fixed time. A pressure weight of 50 kPa was determined to be most appropriate in this example, as applying more pressure requires extra effort with negligible gain in energy recovery.

[0198] An exemplary embodiment of a sweat-permeable layer including a hydrogel, such as the exemplary porous PVA gel of FIG. 25(b), has a structure that facilitates the movement of naturally generated sweat (containing analytes) from a subject's fingertip, such that the device does not require sweat induction, whether the subject requires exercise to induce sweating, or generates heat to induce sweating, or requires iontophoretic effects or chemical stimulation to induce sweat production from the user. The sweat-permeable layer including a hydrogel can transmit naturally generated sweat (including low volumes, e.g., microdroplets) across the side of the sweat-permeable layer in contact with the fingertip on the side in contact with the sensor (e.g., electrode). Additionally, the sweat-permeable layer is configured to enhance the quality of the detectable electrical signal from the facilitated electrochemical reaction of the analytes in the transmitted sweat (e.g., small droplets having dimensions of tens or hundreds of nanometers or tens or hundreds of microns). For example, pressure applied by a user's finger minimizes the diffusion paths and reduces the electrode impedance (see exemplary data in FIG. 38) at the sensing electrodes based on, for example, the increased conductivity of the exemplary carbon foam-based BFC structure and porous hydrogel layer (e.g., PVA gel) when under compression. Furthermore, due to the flexible and durable structure of the porous carbon foam-based BFC and porous PVA gel, for example, no mechanical damage is observed throughout the compression process of the sweat-permeable layer.

[0199] As shown in Figure 25(b), the power harvested from the fingers is directly proportional to the number of fingers in devices deployed with different pressing durations (30 s and 3 min), with a 3 min pressing with 3 fingers harvesting as high as 17 mJ over a 5 min period, translating to an average power of 56.7 μW and an energy ROI of over 1000%, considering the small amount of energy used in pressing the fingers (~0.5 mJ / finger / press). As shown earlier in Figure 24, pressing can increase the instantaneous power of the BFC, and pressing time affects the total amount of energy harvested within a short period of time. As expected, the BFC pressing time has a significant effect on the energy generation (Figure 25(d)). These data show the power-time profile recorded as the BFC contact time is increased from 5 s to 180 s. The profile shows that longer contact times lead to higher power generation and collection of additional energy, indicating that sweat can continuously diffuse through the gel during the contact period. To further explore the benefits of repeated fueling for power harvesting within short periods of time (toward rapidly powering electronics), the 180 s contact time and the remaining 120 s idling time are divided into 5, 15, 30, and 50 segments corresponding to contact frequencies of 1, 3, 5, and 12 beats per minute (BPM), respectively. Such characterization is also important for further integration with PENG harvesters that require repeated pressing for energy harvesting. As shown in Figure 25(d) and Figure 46, the total energy harvested within 5 min increases to a full 8 mJ per finger at a contact frequency of 3 BPM and begins to decrease as the BPM is increased to 12, reflecting a period of rapid decline as the finger is removed from the gel.

[0200] Integrated touch energy harvester

[0201] After optimizing the operation of the fingertip BFC, we evaluate the potential of an efficient bioenergy harvesting approach towards practical autonomous and sustainable power supply of wearable devices. To guarantee the applicability of self-powered devices, the system is expected to retain a sufficient amount of harvested energy with the ability to power up the electronics as quickly as possible for the pulsed operation mode. To this end, the energy input from the harvester, the energy storage for regulation, and the system energy consumption must be carefully characterized along with the budgeting of the energy flow to ensure a highly efficient system operation. Thus, the energy harvesting capability of the BFC is first tested by charging a capacitor, which can then be used to power the electronics in a pulsed manner. With a low potential input from the BFC, a low-power booster with an energy regulation circuit is designed to boost the BFC voltage to charge the capacitor up to 4V. Furthermore, to fully exploit the energy input associated with the finger pressing action, a PZT-based PENG was integrated with the BFC in a clever layout using the same device footprint to simultaneously harvest the corresponding mechanical energy. Such integration allows for synergistic harvesting of bioenergy associated with the same finger pressing motion and requires careful consideration of the characteristics of the individual harvesters to maximize their power generation while minimizing their limitations. Due to the high AC voltage nature of the PENG, its input is regulated via a bridge rectifier before connecting to the capacitor. A system diagram of the integrated BFC-PENG harvester is shown in Figure 26(a). The energy harvesting of the PENG relies on the mechanical deformation of a PZT chip located directly under the BFC energy harvester and is activated by a slight finger press. The power generated in the PZT increases with increasing pressing force, frequency, and deformation (controlled by the spacer thickness) (Figures 47-48). Therefore, the best performance of the integrated system is expected at contact frequencies exceeding those of the BFC system alone, as shown in Figure 26(b).After successful integration of a single pair of mechanical and biochemical energy harvesters (Figure 26(c)(i)), an identical pair of PENG harvesters is attached on the other side of the BFC in a sandwich-like manner to effectively harvest mechanical energy by a pinching motion, thereby harvesting the maximum amount of power without expanding the device footprint (Figure 26(c)(ii)). Employing an optimal pressing frequency of 6 BPM and a pressure of 50 kPa, the charging speed of the BFC is tested against an external capacitor in the range of 47-470 μF (Figure 26(d)). The charging time of the capacitor increases with increasing capacitance, with the dominant contribution of the BFC as the primary energy source. To measure the charging behavior of the energy harvesters, 100 μF capacitors with a set voltage window between 2 V and 4 V are used to examine their corresponding charging times. While the biochemical and mechanical energy harvesters acting independently are able to charge a selected 100 μF capacitor within 8 and 20 minutes, respectively, the integrated system completed this task within only 4 minutes (Figure 26(e)). Note that such synergistic behavior is expected to go beyond the simple addition of power from both harvesters, since an increase in the total energy input also increases the conversion efficiency of the booster circuit.

[0202] 26 and 27A-B, the energy management circuit includes a low power booster (e.g., booster 2708) that can be configured as a DC-DC step-up converter, which boosts the voltage and lowers the current to provide a voltage (e.g., >2V) sufficient to power an electronic device. The example BFC 2704 of FIG. 27B has an input voltage of only 0.5-0.7V, and the boost converter (e.g., booster 2708) is designed to boost its output voltage and store the voltage in an energy storage device (e.g., capacitor, supercapacitor, battery, etc.) for powering the electronic device. An integrated circuit including a boost converter and a charge regulator that prevents overcharging and overdischarging of the energy storage device (such as shown in FIG. 51B) can be used to regulate the power output from the biofuel cell for subsequent powering of an electronic device such as a microcontroller unit (such as shown in FIG. 51A).

[0203] The energy harvesting operation is also optimized with respect to the finger pressing frequency. As discussed earlier, a pressure of 50 kPa was found to be optimal in terms of power-friendly output ratio. Therefore, the effect of contact frequency on bioenergy harvesting is evaluated using a pressure of 50 kPa with pressing frequencies ranging from 1 to 24 BPM to determine the optimal pressing frequency that can charge a 100 μF capacitor in the shortest time. As shown in Figure 26(f), the charging rate of the 6 BPM pressing pattern charges the capacitor faster compared to the 3 BPM and 12 BPM pressing frequencies, resulting in the fastest charging rate. The trend observed in Figure 26(f) is consistent with the profile shown in Figure 26(b). This is because a lower frequency (3 BPM) provides less mechanical energy input to the PZT, while a higher charging frequency reduces the biochemical energy harvesting efficiency (Figure 25(e)), resulting in the optimal charging rate at 6 BPM with the lowest charging time. At an optimized pressing frequency of 6 BPM, the performance of a single BFC harvester can be compared to its sandwich configuration (two back-to-back integrated devices) (Figure 26(g)). As expected, the double-sided harvesting device using two-finger pressing motion for energy harvesting at 6 BPM charges a 100 μF capacitor to 4 V within about 2 min, compared to the 4 min charging time observed with a single PZT integrated BFC setup. Finally, the continuous energy harvesting capability of the double-sided BFC-PZT was investigated at the previously optimized conditions (50 kPa at 6 BPM). As shown in Figure 26(h)(i)-(ii), the two pairs of BFC harvesters can effectively and consistently charge the capacitor in ~2.8 min over a 30 min period. Similarly, the integrated BFC-PZT harvester pair also provided consistent energy generation and a faster charging time of ~2.3 min (Figure 25(h)(iii)-(iv)). The charging process was also tested on subjects with lower sweat rates, resulting in a slight increase in charging time (Figure 49). Additionally, to further reduce the charging time of the system, a capacitor with a larger capacitance can be used to collect a similar amount of charge at a lower voltage.As shown in Fig. 26(h)(v)-(vi), the 220 μF capacitor is charged in a voltage window between 2 V and 3 V, which takes only about 92 seconds, significantly faster compared to charging a 100 μF capacitor to 4 V. Such a change can be beneficial for rapid powering of electronic devices, and the lower voltage can also limit the power consumption of MCUs (Fig. 52). The PZT integrated sandwich BFC system has been shown to be the most efficient one to continuously and repeatedly charge a capacitor by its polarization. The integrated system enables substantial energy harvesting using the pinching motion and natural sweat flow with negligible energy input from the fingertip. Considering the energy input of pressing a finger every 10 seconds (~1 mW), such energy harvesting behavior is attractive compared to typical tribo / piezoelectric harvesters and BFCs that require movement or motion as energy input (>100 W). These results illustrate the feasibility of hybrid BFC-PZT harvester integration for practical applications, demonstrate the most favorable energy ROI reported to date among all bioenergy harvesters, and set a new standard in bioenergy collection efficiency for wearable harvesters.

[0204] Self-powered sensing system

[0205] 27B, a self-powered sensing system implemented according to some embodiments of the disclosed technology includes an energy harvester 2701, an energy management circuit 2705, a microcontroller unit (MCU) 2714, an electrochromic display (ECD) 2716, and a sensor 2718. In some implementations, the energy harvester 2701 includes a piezoelectric generator 2702, such as a PZT, and a biofuel cell (BFC) 2704, such as a touch-based biofuel cell. In some implementations, the energy management circuit 2705 includes a bridge rectifier 2706, a voltage booster circuit 2708, an energy storage device 2710, and an analog switch 2712. To demonstrate the practical utility of a finger-based integrated bioenergy harvester for powering electronic devices, a potential difference sensing system with an ECD panel operated in a pulse session is developed (FIG. 27A). The system consists of an energy regulation component that separately manages the low-voltage continuous input from the BFC 2704 via the booster circuit 2702 and the high-voltage AC pulse input from the PZT 2702 chip via the bridge rectifier 2706 (FIG. 27B, FIG. 50-FIG. 51). Both rectified energy inputs are collected in an energy storage device 2710, such as a capacitor, supercapacitor, or battery. The overvoltage protection feature of the booster circuit 2702 is utilized, which is connected to an analog switch 2712 that controls the supply of energy from the capacitor 2710 to the MCU 2714. In some implementations, the energy regulation component may further include a regulation circuit to regulate the output voltage of the booster circuit 2702 (e.g., to prevent overcharging and / or over-discharging). The low-power MCU 2714 reads the voltage input from the sensor and selects with 10-bit analog-to-digital (ADC) resolution to control the "on" and "off" of 10 individual ECD pixels. An ECD fabricated by layer-by-layer screen printing (FIG. 27C(i), FIG. 55) is chosen for its low energy consumption, since it only requires energy when refreshing the display content.The pixel includes a seven-segment numeric display, with two pixels for displaying the range of sensing ("x0.1" and "x10") and one pixel for displaying "mM" as the unit of chemical sensing when the system boots for the first time (Figure 27C(ii), Figure 56). The system design does not require the integration of any wireless communication electronics, as the system requires external electronics (e.g., smartphone, smartwatch, computer) for data transmission and processing to obtain the sensing results. To maximize the system energy utilization efficiency while ensuring the operation of the ECD, the power and charging consumption for the MCU and ECD are carefully characterized, and the capacitance for energy storage is optimized at 220 μF with an operating window of 3 V to 2 V (Figures 52 to 54 and 57). Two pairs of integrated BFC-PZT harvesters configured back-to-back are connected to the system to provide the harvested biochemical and mechanical energy from the pinching motion of the thumb and index finger. For data collection, sensors can be connected to system ADC channels and results are displayed via the ECD with a resolution of one significant figure (Tables 2-3 below).

[0206] Two types of sensors, namely potentiometric sodium sensor and vitamin C sensor, are used to demonstrate the applicability of such a self-powered sensing system. The potentiometric sodium sensor relies on measuring the potential difference between a sodium ion selective membrane on the working electrode and a silver / silver chloride (Ag / AgCl) reference electrode when in contact with a sodium sample solution (Figure 27D(i)). The electrode-electrolyte interface results in a sodium concentration gradient (between the thin film and the solution), resulting in a potential signal that is logarithmically dependent on the sodium concentration. Such potentiometric detection is applicable for a wide range of clinically or environmentally important electrolytes. Figure 27D shows the calibration of the fabricated sodium sensor, demonstrating a gradient of 59.3 mV per decade of sodium concentration. It also shows good selectivity for potassium ions, indicating that the change in sensor potential is negligible. As shown in Figure 27E, the system can be activated by pressing the energy harvester to monitor different sodium concentrations in tap water and 1:100 diluted seawater.

[0207] Vitamin C sensing generally relies on current measurements that are converted to potentiometric measurements via a controlled load. Such sensors are usually called "self-powered" sensors and rely on an autonomous oxidation reaction on the working electrode along with a complementary reduction reaction on the counter electrode, similar to those of BFCs (e.g., enzymatic glucose, lactate, or alcohol sensors). In this case, the sensing principle is based on the electrocatalytic oxidation of vitamins, generating a proportional current flow, which is further converted to a potentiometric signal (ΔE) under an applied load. Vitamin C sensors rely on the selective nonenzymatic oxidation of ascorbic acid (AA) on the anode, catalyzed by an immobilized tetrathiafulvalene-tetracyanoquinodimethane (TTF-TCNQ) charge transfer complex; silver oxide (AgO) is used as the cathodic material, which provides a stable potential through its reduction (Figure 27F(i)). Sensing of vitamin C in stimulated sweat has been described previously and is adapted here to detect vitamin C levels in natural fingertip sweat. The load between the two electrodes is optimized at 10 MΩ. Calibration experiments in Figure 27F(ii) and Figure 59 show the sensitivity of the vitamin-C sensor, while the corresponding interference studies show the high selectivity of the sensor for common sweat constituents including glucose, urea, lactate, and acetaminophen. For this, a hydrogel similar to that used in the BFC is pre-soaked for artificial sweat and placed over the sensor to absorb fingertip sweat upon contact. The on-body use of touch-based vitamin C sensing is optimized for sweat generation time (60 s) and sweat collection time (120 s) (Figure 60). As shown in Figure 27G, a human subject is asked to take a vitamin pill and sense vitamin C levels continuously over a 30-minute period. The ECD can rapidly update the obtained vitamin C concentration (every 1-2 min), and the sensing system can capture the dynamics of the rise and fall of vitamin C concentration in natural fingertip sweat (Figure 61). Unlike other studies that use various energy harvesters for self-powered sensing and require rapid movements and rigorous exercise, our system efficiently harvests energy from the slow pressing motion of a finger quickly and continuously, and can easily power complex electronic systems.Therefore, the integrated harvesting system shows clear advantages in practical applications as an independent, self-powered electronic system for personalized health and nutritional adequacy, or environmental monitoring.

[0208] The disclosed technology can be implemented in some embodiments to provide a biofuel energy harvester with extremely high energy ROI that effectively harvests energy from natural fingertip sweating and finger pressing motion, as well as its practical application in self-powered and fully integrated sensing devices. The demonstrated concept of utilizing continuous naturally pumped sweat and intuitive finger pinching motion for energy generation and operation of low power electronics shifts the current paradigm of bioenergy harvesting devices from "work for power" to "life to power". The concept is demonstrated by energy harvesting while sleeping or during low intensity desk work, converting kinetic energy and chemical energy traces resulting from our daily activities into electrical form. Utilizing effortless and continuous fingertip sweating as an energy source, the BFC harvester is further boosted by a piezoelectric PZT harvester that fully utilizes the intuitive finger motion of pinching. 2cm 2 With a small footprint of 100m, this system demonstrates high energy harvesting efficiency compared to previously reported bioenergy harvesters that require vigorous motion or extreme sweat-inducing exercise, while providing similar energy collection performance. Pairing the low-power ECD with a touch-based harvester platform presented an energy-efficient electrochemical sensing system that can be applied to a wide variety of sensors for personalized health and nutrition monitoring applications, beyond the demonstrated sweat vitamin C and sodium sensors.

[0209] The integrated system is designed around smart and highly efficient utilization of limited bioenergy, together with complementary synergistic harvesters, optimized energy storage units, low-power energy management integrated circuits, MCUs, and displays, to achieve fast-response, extended, and autonomous operation. The possibility of utilizing passive sweat for self-powered sensors can be added, and the sensor power or open circuit voltage can be correlated with the concentration of target analytes in sweat. Such highly efficient, user-friendly, biocompatible energy harvesting technology, coupled with system integration and corresponding smart energy organization, offers considerable promise for establishing self-sustainable, reliable, and independent next-generation epidermal electronic systems for healthcare and health tracking.

[0210] Manufacturing Technology Examples

[0211] Example flexible CNT foam production

[0212] The fabrication procedure of flexible CNT foam is described in Figure 28. Specifically, MWCNT-COOH (0.25 g), graphite (0.05 g), and NaHCO3 (6.0 g) are mixed well with a mortar and pestle to obtain a gray powder, followed by adding 3.0 g of SEBS (dissolved in toluene at 4 (SEBS):10 (toluene) wt%) and stirring at 1800 rpm for 5 min. Subsequently, 2.5 ml of toluene is added and mixed again at 1800 rpm for 5 min to obtain a homogenous paste. The resulting paste is then cast into a rectangular-shaped structure with a height of 1 mm, carefully controlled by placing two glass slides (1 mm thick) at a controlled distance. Immediately, the cast part is transferred to ethanol for 20 min to perform a solvent exchange that prevents the collapse of the structure during the evaporation of the solvent (toluene), and then dried under ambient conditions. Afterwards, the dried CNT foam was immersed in 0.5 M hydrochloric acid for 3 h to completely remove the NaHCO3 template; this method resulted in a highly porous structure of the CNT foam. The resulting porous CNT foam was washed several times with distilled water and dried at 80 °C to obtain flexible CNT foam.

[0213] Fabrication of a Biofuel Cell Example

[0214] Each CNT foam was cut into 0.3 cm 2 The MWCNTs were cut into strips (1 cm × 0.3 cm) and two of them (for the anodes) were immersed in a 10 mM EDC / NHS solution for 6 h to activate the carboxylic acid groups of the MWCNTs. The CNT foams were then washed several times with DI water before being deposited on a silver current collector using carbon ink and the cathode was placed between the two anodes. Each bioanode was then drop-cast with 10 μl of 0.2 M NQ (dissolved in 1:9 acetone:ethanol) followed by LOx (10 mg ml -1 40 mg ml of BSA -1 , 10 μL) for 3 h. To immobilize the enzyme, 5 μl each of 1% chitosan and 1% glutaraldehyde in 0.1 M acetic acid is dropped onto the anode and then kept overnight at 4 °C. Otherwise, the cathode is subjected to fixed potential co-electrodeposition of Pt and Cu at -0.75 V for 600 s followed by dealloying of Cu for 40 cycles by cyclic voltammetry over the potential range of 0 V to 1.5 V (scan rate 50 mV s -1 After rinsing several times with DI water, 1% Nafion is dropped onto the cathode and kept at room temperature until use.

[0215] Preparation of Porous PVA Hydrogels of the Examples

[0216] The fabrication of porous PVA hydrogels is adapted from previous studies. First, a solution is prepared in which PVA is dissolved in water at a weight ratio of 1:10 and KOH is dissolved in water at a weight ratio of 1:5. Then, 14 g of KOH solution is added dropwise to 10 g of PVA solution with stirring, followed by dissolving 2.6 g of sucrose in the mixture to form the hydrogel precursor. 15 g of precursor is then poured into a Petri dish (diameter ~9 cm) and left in a vacuum desiccator to remove excess water and crosslink until only 1 / 3 of the precursor's weight remains. The crosslinked gel is then immersed in 0.1 M PBS buffer to remove the sucrose template and excess KOH until the gel is at a neutral pH. The gel can then be cut into the desired size and shape and stored in PBS or AS for subsequent use.

[0217] Fabrication of an Electrochromic Display Example

[0218] The ECD is designed using AutoCAD and then screen printed layer by layer onto SEBS sheets. The ECD design is separated into a front panel and a back panel, which are separated by a layer of white, opaque insulator and PSS electrolyte, and assembled via heat sealing.

[0219] Fabrication of the sensor example

[0220] The sodium sensor is fabricated using flexible silver and carbon inks, which are printed layer by layer on a SEBS substrate and coated with SEBS resin to define the electrode area, with a 2 mm2 thin film as the working electrode. 2 The carbon electrode was exposed and 1 mm was used as the reference electrode. 2 First, a 0.1 M FeCl3 solution is dropped onto the silver electrode to chlorinate the surface and form AgCl.

[0221] PVB (78.1mg ml -1 ) and an excess of potassium chloride (50 mg ml -1 ) was applied to a chlorinated surface (1.5 μl mm-2 ) on the dry mixture layer (2 μl mm ) to prevent salt leaching. -2 1 mg of sodium ionophore X, 0.77 mg of Na-TFPB ion exchanger, 33 mg of PVC, and 66 mg of DOS were dissolved in 660 mL of nitrogen-purged THF and drop-cast onto a carbon electrode (2 μl mm -2 ) to formulate a mixture for a sodium ion selective electrode.

[0222] The vitamin C sensor is fabricated using flexible silver, carbon, and silver oxide inks. The inks are printed layer by layer onto a SEBS substrate and coated with SEBS resin to define the electrode area, measuring 2 mm. 2 Carbon electrodes and 4mm 2 The silver oxide electrode was exposed. A 10 MΩ resistor was solvent welded between the two electrodes as a discharge load. A 5 mM solution of TTF-TCNQ dissolved in ethanol:acetone (1:1) was applied to the carbon electrode (1 μl mm -2 ) and then 1 μl mm -2 Chitosan layer (1 wt % in 0.1 M acetic acid) and 0.125 μl mm -2 Add glutaraldehyde layer (0.5% in water) dropwise.

[0223] A more detailed manufacturing process of the sensor is shown in FIGS.

[0224] Electrical circuit design example

[0225] The circuit consists of four main components: MCU, analog switch, booster, and bridge rectifier. The PCB design is shown in Figure 51. The individual components are then soldered onto the PCB via a standard reflow process. The integrated circuit can perform energy harvesting, storage, and power management. The MCU with integrated ADC can read out from the sensors and display the corresponding results via the ECD.

[0226] Assembly of an Exemplary Self-Powered Sensing System

[0227] The adapter connecting the two sets of BFC and PZT chips is designed using AutoCAD and screen printed on a SEBS sheet (Figure 31). The front and back PZT chips are connected separately to the adapter, and the two PZT chips are placed back to back and separated by two spacers (1 mm thick) placed on the two ends of the chips. The foam BFC electrodes are then fixed to their corresponding positions using conductive carbon ink and modified using the procedure described above. The connector is then connected to a PCB via a "solvent welding process" following previous studies. Similarly, the display and sensor are connected to a PCB using the same process to complete the assembly of the self-powered sensing system.

[0228] A highly efficient fingertip biofuel harvesting system: Towards autonomous self-powered sensing and display

[0229] FIG. 28 shows the synthesis of CNT foam: (a) CNT composite paste preparation; (b) fabrication steps of CNT foam using the paste.

[0230] Figure 29 is 1 x 3 cm 2 A photograph of a bent strip of CNT foam is shown.

[0231] Figure 30 shows the water absorption performance of CNT foam: (a) Schematic of the carbon foam water absorption test. A 1.5 cm x 2.5 cm piece of CNT foam is sandwiched between two glass slides with Kimwipe paper (same thickness as carbon foam) on top of the foam. The plate containing water is prepared with green dye for visibility; (b) Time-lapse photographic images after immersing the CNT foam in water for 7 s (ii), 15 s (iii), 30 s (iv), and 60 s (v). Water successfully passes through the CNT foam within 7 s.

[0232] FIG. 31 shows the assembly of CNT foam for BFC and PZT chips: (a) a current collector is first printed on a SEBS sheet and trimmed to shape; (b) a piece of CNT foam (1 cm × 0.3 cm) is attached against a silver current collector using a carbon composite ink; (c) two PZT chips are attached to their corresponding contact points and folded back in a folded-back fashion.

[0233] FIG. 32 shows SEM images and corresponding EDS mapping of CNT foam cathodes: (a) cross-section of p-Pt CF cathode; (b) front view of p-Pt-CF cathode; (c) EDS mapping of carbon and Pt on the cross-section of p-Pt CF cathode.

[0234] Figure 33 shows cryo-SEM images of cross-sections of porous and non-porous PVA hydrogels: (a) SEM image of a PVA hydrogel using sucrose as a template. The structure of the gel is highly porous, allowing for rapid penetration of sweat; (b) PVA hydrogel without sucrose template.

[0235] FIG. 34 shows optimization of BFC anode-to-cathode area ratio: (a) CA of BFC at 0.4 V with different anode-to-cathode area ratios in 20 mM lactate environment; (b) Bar graph summarizing the power obtained with different anode-to-cathode ratios after 10 min of CA.

[0236] Figure 35 shows the LSV characterization of cathodes with different electrode materials. Pt deposited on a flat screen-printed carbon electrode and CNT foam, as well as p-Pt on CNT foam are compared (scan rate: 5 mV s -1 ).

[0237] FIG. 36 shows the results for 15 mM lactate (scan rate: 5 mVs -1 The LSV characteristics of the anode with and without a CrN layer are shown.

[0238] FIG. 37 shows the LSV response of the BFC after area ratio optimization: (a) 0.2, 1, and 5 mVs -1A BFC (1 cm) with a cathode and anode ratio of 1:2 in 0.5 M PBS containing 20 mM lactate was used at a scan rate of 100 Hz. 2 ) LSV power response; (b) 5mVs -1 Potential vs. current density polarization curves during LSV measurements. The cathodic potential began to decrease from 0.4 V to 0.23 V, while the anodic potential increased from -0.2 V to 0.23 V.

[0239] Figure 38 shows the EIS Nyquist plots of a two-electrode BFC covered by a porous PVA hydrogel with different applied pressures. The hydrogel is immersed in 0.1 M PBS before testing (scan range: 1 MHz to 0.1 Hz; amplitude: 10 mV).

[0240] Figure 39 shows an optical microscope image of a finger with bromophenol dye applied. Bromophenol green is used as a sweat indicator in sweat analysis, which is initially colorless and turns blue at pH 5.4 and above. All subjects wash their hands before the experiment and monitor sweat rate using their index finger, taking micrographs up to 10 minutes. The density of the blue dots indicates the difference in sweat rate for each subject.

[0241] FIG. 40 shows the BFC performance using subjects with different natural fingertip sweat rates: (a) Power derived from different subjects with different sweat rates using CA at 0.4 V; (b) Bar plot representing the total harvested energy for 30 min from different subjects.

[0242] Figure 41 shows the stability of the hydrogel in a long-term collection study. The BFC is covered with the PVA hydrogel, pressed for 3 minutes, and left for 1 hour. After 1 hour, without rehydrating the hydrogel, the BFC is pressed again for 3 minutes. The hydrogel was unable to retain water without encapsulation, and the electrodes lost connection after 90 minutes.

[0243] Figure 42 shows repeated pressing of the BFC. One BFC device is repeatedly pressed with one finger for 30 seconds every 5 minutes. The power generated by the BFC after contact increased from ~15 μW to ~40 μW by repeatedly refueling the device. The total energy harvested in 30 minutes is 67.7 mJ.

[0244] FIG. 43 shows energy harvesting from low intensity desk work. The BFC is wrapped around the subject's right index finger (different from FIG. 24(h)(ii)) for 1 hour. The subject is asked to perform normal tasks such as typing, clicking a mouse, or writing. The graph records the power of the BFC during the hour of such activity while discharging at 0.4V. A total of 35.3mJ of energy is harvested within the hour.

[0245] FIG. 44 shows energy harvesting from a night of inactivity during sleep: (a) Power harvested by wrapping one BFC around the index finger of a subject (same subject as in FIG. 24(f)(iii)) measured over 10 hours of sleep. Total energy harvested is 364.4 mJ; (b) Power harvested by wrapping one BFC around the index finger of a subject with a lower sweat rate (different subject from a) measured over 6.5 hours of sleep. Total energy harvest is 253.0 mJ.

[0246] Figure 45 shows the power harvested from a BFC pressed by a finger at different sweating times. The subject's finger was washed and thoroughly dried, and then the BFC device was pressed for 30 seconds after waiting for (a) 1 minute, (b) 3 minutes, (c) 5 minutes, and (d) 10 minutes. The energy harvested in the first 5 minutes was similar, but the amount of energy harvested at 30 minutes showed more differences. Applied pressure 50 kPa, discharge voltage 0.4 V.

[0247] Figure 46 shows the power supply of the BFC pressed at different frequencies. The BFC is pressed for 5 minutes with different frequencies while maintaining 60% of the contact time: (a) 0.5 bpm (press for 72 seconds, release for 48 seconds), (b) 1 bpm (press for 36 seconds, release for 24 seconds), (c) 3 BPM (press for 12 seconds, release for 8 seconds), (d) 4 bpm (press for 9 seconds, release for 6 seconds), (e) 6 BPM (press for 6 seconds, release for 4 seconds), (f) 12 bpm (press for 3 seconds, release for 2 seconds).

[0248] Figure 47 shows the OCV of a PZT chip pressed at the center with different pressures. 1×2 cm 2 The PZT chip is located at the center (1 cm 2 The specimens are pressed at (a) 10 kPa, (b) 25 kPa, (c) 50 kPa, and (d) 100 kPa d in the area (corresponding to the BFC) with 0.5 mm high spacers on either side of the back surface.

[0249] Figure 48 shows energy harvesting using a PZT chip with different operating conditions. 2 The PZT chip is pressed against the center: (a) with spacers of various heights (0.1, 0.5, 1 mm), (b) with pressures (10 kPa, 25 kPa, 50 kPa, 100 kPa), and (c) with frequencies (3 bpm, 4 bpm, 6 bpm, 12 bpm). Pressing with 100 kPa and a 1 mm-high spacer showed the fastest charging rate compared to other conditions, but applying 100 kPa could damage the PVA hydrogel, while a 1 mm spacer could result in cracks on the PZT chip. Therefore, a pressure of 50 kPa and a spacer height of 0.5 mm are determined to be optimal for subsequent experiments.

[0250] Figure 49 shows charging a capacitor using the integrated device in subjects with different sweat rates. The integrated harvester (with two BFCs and two PZT chips) is powered by two subjects with different sweat rates (different from Figure 26(h)(i)), pushing the system to repeatedly charge a 100 μF capacitor from 2 V to 4 V.

[0251] FIG. 50 shows a system flow chart of the integrated system and the corresponding voltage values.

[0252] FIG. 51 shows a schematic diagram of the integrated circuit board: (a) circuit layout of the AtTiny441 MCU; (b) circuit layout of the bq25505 booster, analog switch, and bridge rectifier.

[0253] Figure 52 shows the MCU power consumption under different operating voltages.

[0254] Figure 53 shows the capacitor charge flow to the MCU. A 220 μF capacitor is charged to different voltages and discharged to the MCU. As shown, there is no significant benefit to increasing the capacitor voltage over the runtime of the system.

[0255] FIG. 54 shows the MCU output voltage and charge to the ECD: (a) the voltage of different capacitors discharged from 4V; (b) the amount of charge available from the MCU to the display from a capacitor charged to 4V with different capacitances.

[0256] FIG. 55 shows an example of layer-by-layer printing and assembly of an ECD panel.

[0257] FIG. 56 shows photographic images of a printed ECD displaying different content.

[0258] Figure 57 shows the current and charge consumption of a printed ECD: (a) photographic image displaying two sizes of pixels on a panel, including seven smaller pixels at the top and three larger rectangular pixels at the bottom; (b)-(c) turn-on current b and charge c required for the small pixels at different voltages; (d)-(e) turn-on current d and charge e required for the small pixels at different voltages.

[0259] FIG. 58 shows examples of layer-by-layer printing and drop casting of sensors: (a) printing and drop casting of a Na+ sensor; (b) printing and drop casting of a Vitamin C sensor.

[0260] Figure 59 shows Vitamin C sensor calibration; (a) Potentiometric signal obtained in 0.1 M PBS pH 7.4 upon spiking Vitamin C concentrations from 200 to 1000 μM while releasing at a 10 MΩ load; (b) Calibration curve and exponential fit curve (n=3). RSD=1.05%.

[0261] FIG. 60 shows the optimization of a vitamin C sensor: (a) the voltage of the sensor before touching the sensor and after being pressed by a finger with different sweat accumulation times (10, 30, 60, 120, 180, and 300 seconds). The optimal waiting time of 1 minute is determined; (b) the voltage of the sensor before and after being pressed by a finger with different pressing times (10, 30, 60, 120, 180, and 300 seconds). The optimal pressing time of 2 minutes is determined; (c) a control test with a coated finger. The sensor showed no response to the pressure applied by the finger.

[0262] Figure 61 shows vitamin C determination in sweat from fingertips for two subjects. The potentiometric response is measured 20, 60 and 120 minutes after taking a 1,000 mg vitamin C pill. Fresh hydrogel is used for each measurement: (a) subject 1, (b) subject 2.

[0263] Manufacturing Technology Examples

[0264] Characterization of flexible carbon foams

[0265] (1) Manufacturing of flexible CNT foam

[0266] To synthesize flexible water-wicking CNT foam, a mixture of carboxylated multi-walled carbon nanotubes (MWCNT-COOH, CNT) and graphite as conductive carbon filler, sodium bicarbonate (NaHCO3) as template, and styrene-ethylbutylene-styrene block copolymer (SEBS) as elastomeric binder is mixed into a paste using toluene solvent. As shown in Figure 28(a), 0.25 g of CNT and 0.05 g of graphite are added into a glass bottle, followed by 6.0 g of NaHCO3. Then, 3.0 g of SEBS resin (4 g of SEBS dissolved in 10 ml of toluene) is carefully added to the previous mixed powder and mixed thoroughly at 1800 revolutions per minute (rpm) for 5 minutes. Then, 2.5 ml of toluene is further added to the previous mixture and mixed again at 1800 rpm for 5 minutes. The composite CNT paste is then ready for subsequent use.

[0267] To fabricate the CNT foam (Figure 28(b)), the composite paste is cast into a stencil with the desired thickness and size. In this study, a 1 mm thick glass slide is used as the stencil to control the thickness of the foam. Immediately after casting, the deposited paste is immersed in ethanol for 20 min for toluene-ethanol solvent exchange, which solidifies the SEBS and prevents the collapse of the foam structure during the drying process. After the solvent exchange, the carbon foam is dried naturally at room temperature without a heating process. To dissolve all the NaHCO3 while making the CNT morphology hydrophilic, the CNT foam is immersed in 0.1 M hydrochloric acid (HCl) for 5 h and washed with distilled water several times to remove the acid residue from the carbon foam. Finally, the carbon foam is dried in an oven at 80 °C and kept at ambient conditions. As shown in Figures 29-30, the fabricated CNT foam is flexible, hydrophilic, exhibits good water permeability, and can soak up water.

[0268] (2) BFC modification and optimization

[0269] To functionalize the CNT foam as a biofuel cell (BFC), the foam was first cut into 1 × 0.3 cm 2 The porous platinum (Pt) electrode (p-Pt-CF) was fabricated by co-electrodeposition of copper (Cu) and Pt onto the carbon-foam electrode at -0.75 V, followed by 50 mVs. -1Electrochemical etching (de-alloying) of Cu was carried out using cyclic voltammetry from 0 V to 1.5 V for 40 cycles at 1000 V. The resulting p-Pt CFs are highly porous and are deposited throughout the 3D CNT foam as shown in the scanning electron spectroscopy (SEM) images with electron dispersive X-ray spectroscopy (EDS) in Figure 32. The resulting electrodes clearly demonstrate the advantages of the 3D p-Pt CF structure over Pt-SPC and Pt CFs in terms of onset reduction potential and current density resulting from O2 electrocatalytic reduction on Pt as evidenced by the linear sweep voltammetry (LSV) in Figure 35. While the carbon foam-based Pt cathode showed sufficient cathodic current derived from an onset potential of 0.3 V, the p-Pt cathode showed a higher oxygen reduction reaction (ORR) onset potential derived from 0.4 V (vs. Ag / AgCl) and a higher current density over the operating potential range of the cathode. The anode was fabricated by decorating carbon foam with 1,4-naphthoquinone (NQ), LOx, and chitosan to ensure efficient electron mediation and uniform LOx surface coverage. The anode was also characterized using LSV and showed an increase in anodic current with an onset potential of -0.2 V (vs. Ag / AgCl) upon increasing lactate concentration from 0 to 15 mM (Figure 36).

[0270] As shown from the single electrode characterization above, it is observed that the current from the cathode is significantly higher compared to the anode. To ensure maximum utilization of both the anode and cathode in a limited area, the area ratio between the anode and cathode is optimized. As shown in Figure 34, the system discharged under 0.4 V using chronoamperometry (CA) is mostly limited by the anode. Thus, the current from the cathode is limited to 0.6 cm. 2 :0.3cm 2 The anode and cathode of the are selected to maximize the power supply in a limited area.

[0271] The assembled BFC with a 2:1 ratio was tested using linear scan voltammetry (LSV) at various scan rates, which also demonstrated a high charge current of 5 mVs due to the large double layer charging current on the highly porous electrode. -1 Approximately 500 μW / cm2 and 0.2 mVs -1 Approximately 100 μW cm -2 The results showed a large discrepancy in the power change between the CA and the LSV (Figure 37). Therefore, to accurately measure the power of a BFC, the CA is preferred over the LSV to eliminate the effects of high charging currents. Individual electrode potential shifts between the two-electrode LSV were also observed using an external reference electrode, showing that the power-limited anode potential shifted from -0.2 V to +0.23 V vs. the cathode, which shifted only from +0.4 V to +0.23 V.

[0272] All the above in vitro tests are carried out in 0.5 mM phosphate buffer (PBS) at pH 7.4.

[0273] Sweat rate measurement

[0274] To analyze the performance of contact-based BFC for different subjects with different passive sweat rates of the fingers, the sweat rates of the individuals are qualitatively compared. To qualitatively estimate the sweat volume, bromophenol green is used as a sweat indicator to obtain traces of sweat glands. Bromophenol green is initially colorless, and at pH >5.4, blue coloration is observed. Since the pH of sweat is between 5 and 7, bromophenol can be used to visualize the number of sweat glands and the amount of sweat.

[0275] A 5% by weight bromocresol green solution is prepared by dissolving it in silicon oil and sonicating for 20 minutes. After washing and drying the hands thoroughly, the oil is applied to the index fingers of three subjects, and microscopic optical images are taken up to 10 minutes. As shown in Figure 39, the three subjects exhibit different sweating rates in the first 10 minutes, with subject 1 exhibiting the most number and colored areas on the diagram, followed by subject 3, and subject 2 exhibiting the least amount of sweating.

[0276] Since the amount of fuel and its lactate concentration determine the power of a contact-based BFC, the power of the BFC is tested by pressing a finger on the BFC for 30 seconds followed by resting for 30 minutes on all three subjects with different sweat rates. The amount of power and energy collected within 30 minutes is shown in Figure 40, which shows that the sweat rate is positively correlated with the power and energy collected from the BFC, with subject 1 giving the most energy of 12mJ, followed by subject 3 with 7mJ and subject 2 with 5.5mJ.

[0277] Power management, sensing, and display control circuit design examples

[0278] The integrated circuit is designed to condition and store the energy harvested from the BFC and the PZT chip, and use the stored energy to power a microcontroller that records the signal from the sensor and displays the sensing results on an electrochromic display (ECD). The circuit design is modified based on previous work. To condition the BFC power, a voltage booster is used that increases the BFC's low voltage (0-0.6V) to 2-5.5V. An integrated energy management function in the booster regulates the programmable maximum voltage (V BAT_OK_HYST ) and minimum voltage (V BAT_OK The digital output from the booster is equal to the voltage across the connected capacitor V BAT_OK_HYST When the voltage rises above V BAT_OK It turns off when it drops below this level. This is used to control an analog switch which controls the connection of the capacitor to a microcontroller. A bridge rectifier is used to rectify the AC input from the PZT generator and the regulated output is connected to a capacitor to store the harvested energy. The circuit diagram is shown in Figure 51.

[0279] The power consumption of the microcontroller is characterized by supplying a constant potential (Figure 52), and the power varies from 6 mW to 16 mW depending on the voltage applied. The charge required for the ECD is also characterized, which requires at least 150 μC and 100 ms of operation time, regardless of the voltage used. Therefore, to maximize the efficiency of the system, a lower operating voltage is preferred, since less charge is used for the MCU. At the same time, as shown in Figure 4h, charging a 220 μF capacitance from 2 V to 3 V takes less time than charging a 100 μF capacitor from 2 V to 4 V, while more charge can be stored for the ECD color change, since lower voltage charging requires lower quality energy. Therefore, a system storage capacitor of 220 μF is selected, which is used to charge and discharge in the window between 2 V and 3 V, giving a theoretical charge of 220 μC, with ~150 μC available for the ECD.

[0280] Fabrication and Characterization of Exemplary Electrochromic Displays

[0281] (1) ECD variation and optimization

[0282] The ECD substrate was made of styrene-ethylene-butylene-styrene triblock copolymer (SEBS) and was prepared by doctor blade casting (thickness 500 μm) of SEBS resin dissolved in toluene (40 wt%) and curing it in an oven at 80°C for 1 hour.

[0283] The ECDs are fabricated using layer-by-layer screen printing with customized inks. The ink formulations are adapted from previous studies. The printing of the ECDs relies on four inks: an electrochromic poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) ink, a silver ink for interconnects, an opaque insulator ink composed of SEBS and TiO2, and a sodium polystyrene sulfonate-based electrolyte ink. The PEDOT:PSS ink contains PEDOT:PSS paste, toluene, deionized (DI) water, sodium dodecylbenzene sulfonate (DBSS), and fluorosurfactant FS-65 in a 10:1.7:1.5:0.1:0.14 weight ratio. The silver ink contains silver flakes, SEBS, and toluene in a 1:0.16:0.5 weight ratio. The opaque insulator ink contains TiO2, SEBS, and toluene in a 1:6:10 weight ratio. The PSSNa electrolyte membrane ink is formulated by mixing PSSNa, D-sorbitol, glycerol, TiO2, and polyacrylamide precursor in a mass ratio of 4:1:1:0.8:2. The PAM precursor is formulated by mixing acrylamide, DI water, potassium peroxydisulfate, and N,N'-methylenebisacrylamide in a ratio of 1:10:0.05:0.01. All inks are mixed in a planetary mixer at 2500 rpm for 10 minutes or until homogenous.

[0284] The ECD panel is composed of a color-charged front panel and a back panel to control local color changes. The layer-by-layer printing steps are shown in Figure 55. That is, the PEDOT:PSS ink is first printed on the SEBS substrate and cured in an oven at 100 °C for 2 hours. The silver interconnect and opaque insulator layers are printed and each layer is cured at 80 °C for 10 minutes. Before assembly, the electrolyte membrane is printed on the back panel and briefly heated in an oven at 80 °C for 15 seconds to crosslink the PAM in the electrolyte membrane. Then, the front panel is aligned and covered onto the bottom panel. Finally, the device is heat sealed on all four sides to complete the device assembly.

[0285] (2) Characteristics of ECD

[0286] The color change of PEDOT:PSS depends on the redox reaction between the two electrodes, with reduced PEDOT:PSS showing a dark blue color (ON) and oxidized PEDOT:PSS showing a translucent blue color (OFF). By design, the top seven pixel segments of the panel can display a single digit, while the bottom three larger pixels show multipliers of ×0.1 and ×10, and units of mM. By combining 10 pixels, 30 levels of concentration can be displayed using the printed ECD panel (Figure 56), displaying the letter "L" when the concentration is below the detection range, or "H" when the concentration is above the detection range.

[0287] Since the voltage and amount of charge available from the capacitors are limited, the minimum voltage and charge required for the ECD color change are characterized to maximize system efficiency. Since the charge required for the ECD is mostly determined by the electrode area, the charge required and turn-on behavior of the ECD should be characterized differently for the top 7 smaller pixels and the bottom 3 pixels. As shown in Figure 57, both pixels change color above 1.5V with turn-on times of less than 100ms. The small pixels consume 10-20μC per pixel to change color and the large pixels consume 50-100μC per pixel. The minimum charge required to change the display content is 30-150μC in total, depending on the number of pixels required to be turned "on".

[0288] Fabrication, Characterization, and Optimization of Exemplary Sensors

[0289] (1) Sensor construction

[0290] Na +The sensors and vitamin C sensors are screen printed and modified by drop casting. Silver ink, carbon ink, SEBS resin, Ag2O ink. The formulations of the SEBS resin and silver ink are described in this patent document. The carbon ink formulation is adapted from a previous study where graphite, Super P carbon black, SEBS, and toluene are added in a weight ratio of 6:1:3.4:6. The Ag2O ink formulation is adapted from a previous study where Super P carbon black, Ag2O, SEBS, and toluene are mixed in a mass ratio of 0.05:0.95:0.18:0.82. Both inks are mixed in a planetary mixer at 2500 rpm for 10 minutes or until homogenous before printing. After printing each layer, the inks are cured in an oven at 80 °C for 10 minutes. The printing and variation of both sensors are shown in Figure 58. The formulations and protocols for drop casting are described in the methods section of the main text.

[0291] (2) Characterization and optimization of vitamin C sensor

[0292] Vitamin C analytical performance was studied in vitro and the sensor showed high repeatability (RSD=1.05%) (Figure 59) and selectivity (Figure 27F). An exponential calibration curve is obtained for programming the MCU to convert the voltage from the sensor to a display (Table 3). To optimize the use of the sensor to sense natural sweat from a fingertip, sweat accumulation time and contact time are optimized based on previously reported work. Sweat accumulation time is optimized by thoroughly washing the index finger and waiting different times (10, 30, 60, 120, 180, and 300 seconds) before pressing the sensor covered by a small piece of porous PVA hydrogel for 3 minutes. Pressing time on the sensor is optimized by thoroughly washing the finger and then pressing the sensor for different amounts of time (10, 30, 60, 120, 180, and 300 seconds). After contact, the voltage of the sensor under a 10 MΩ load is recorded. As shown in Figure 60(a)-(b), the optimal waiting time before further concentration changes is determined to be 1 minute, and the optimal pressing time is determined to be 2 minutes. To verify that the voltage change is affected by the content transmitted from natural finger sweat, the sensor was also touched by a covered finger, which resulted in no voltage change on the sensor (Figure 60(c)).

[0293] With optimized accumulation and pressing times, the sensor is tested on two subjects to measure vitamin C concentration in natural finger sweat. The subjects are asked to take 1,000 mg of vitamin C supplement, and the voltage signal is measured 20, 60 and 120 minutes after vitamin intake. In each trial, a fresh sensor is used. (Figure 61). The voltage showed an increase 20 minutes after taking the tablet, then slowly decreased over 2 hours.

[0294] For each in vivo measurement, 40 µL of 0.1 M PBS is added to a small diameter PVA hydrogel. The PVA hydrogel is first pat-dried with paper to keep the electrolyte gel weight constant. All in vivo experiments are conducted in strict compliance with an IRB approved by UCSD.

[0295] Table 1: Comparison of energy return on investment (ROI) for various wearable bioenergy harvesters. [Table 1] Here the numbers are accurate to an order of magnitude and are in the range of 10 1 cm 2 Assume that the element area is

[0296] Table 2: Na + Sensor voltage-to-display content conversion. [Table 2]

[0297] Table 3: Vitamin C sensor voltage versus display content conversion. [Table 3]

[0298] Monitoring Parkinson's disease treatment using a touch-based L-dopa sweat sensor

[0299] Levodopa is considered a highly effective treatment for Parkinson's disease (PD), but the high variability of levodopa plasma concentrations with oral levodopa-carbidopa treatment often results in suboptimal efficacy, especially during the progression of PD.

[0300] Orally administered levodopa (l-dopa) is considered the "platinum" standard of PD treatment for its impact on disability and discomfort as well as its cost-effectiveness.

[0301] Large and inconsistent fluctuations in plasma concentrations complicate long-term management of PD patients with conventional levodopa formulations.

[0302] After administration of levodopa / carbidopa microtablets

[0303] The objective was to investigate the pharmacokinetic profile of levodopa and carbidopa and to evaluate motor function after administration of a single microtablet dose in patients with Parkinson's disease.

[0304] Due to the flexibility that microtablets offer, individualization of therapy may become easier with regard to fine-tuned dosing.

[0305] Levodopa (L-Dopa) is the "gold standard" drug for the symptomatic treatment of Parkinson's disease (PD) patients. However, its long-term use is associated with the development of motor and non-motor complications, mainly due to its fluctuating plasma levels. The disclosed technology, in some embodiments, can be implemented to provide personalized therapeutic drug monitoring for PD patients upon ingestion of a standard oral pill formulation, centered on dynamic tracking of L-Dopa levels in naturally secreted thermoregulatory sweat. The detection method relies on instantaneous collection of fingertip sweat (via touching the porous hydrogel on the electrode surface) on a porous hydrogel that mediates sweat transport to a tyrosinase enzyme-modified electrode, and sweat L-Dopa is indirectly measured via the reduction current of the subsequent dopaquinone enzyme product. Individual responses to L-Dopa tablet ingestion are demonstrated within a small number of healthy human subjects, along with pharmacokinetic correlations of finger-touch-based sweat and capillary blood samples. This non-invasive detection method holds considerable promise for patient-specific dose adjustments and optimal treatment outcomes toward personalized treatment, including fine-tuning L-Dopa dosing.

[0306] Parkinson's disease (PD) is a chronic, progressive neurodegenerative disorder affecting more than 6 million people worldwide. Levodopa (L-Dopa), a precursor of dopamine, is the most effective drug for the management of PD and is considered the gold standard treatment. However, long-term administration of oral L-Dopa is associated with the development of motor and non-motor complications, mainly resulting from fluctuations in plasma L-Dopa levels. L-Dopa has a narrow therapeutic window, as suboptimal dosages make patients stiff, slow, and tremor-prone, while excessive dosages produce excessive involuntary movements. The therapeutic window narrows with disease progression, leading patients to take higher doses at more frequent intervals. Another complication is the high interpatient variability in response to L-Dopa therapy, which requires patient-specific dosing procedures. Such inconsistent and large fluctuations in plasma drug concentrations hinder the management of PD patients, resulting in suboptimal therapeutic effects, especially during disease progression. Therefore, a device that can rapidly monitor the levels of L-Dopa near or on the patient would be highly advantageous towards L-Dopa dose adjustment, thus avoiding motor fluctuations in PD patients. Nevertheless, no device is available for continuous monitoring of individualized therapeutic levels of L-Dopa. The current "gold standard" method for detecting plasma L-Dopa levels relies on liquid chromatography-mass spectrometry (LC-MS) techniques performed in centralized laboratories, which cannot be adopted in clinical practice due to their invasiveness, long turnaround times, and the need for specialized equipment and skilled personnel, restricting their use to rare occasions for limited pharmacokinetic studies. Therefore, to address this challenge, mobile, distributed, and wearable electrochemical sensors in the form of strips, microneedles, and sweat bands have been proposed. Although such electrochemical platforms offer the possibility of frequent monitoring of L-Dopa, they are mainly limited to in vitro demonstrations, with a single case of in vivo study of sweat bands reported in association with the uptake of fava beans (not the standard pill formulation), which makes its application for realistic therapeutic monitoring of PD patients unclear. Not including the standard pill formulation XXX.Furthermore, due to the fact that L-dopa shares a similar mechanism of absorption with dietary amino acids, the large amount of protein that beans contain significantly limits L-dopa absorption from the gastrointestinal tract into the circulatory system.

[0307] The disclosed technology may be implemented in some embodiments to provide personalized therapeutic drug monitoring for PD patients, centered on dynamic non-invasive tracking pharmacokinetic profiles of L-Dopa levels in secreted sweat upon ingestion of a standard pill formulation. Sweat is a non-invasively recoverable biofluid that contains a wealth of information of trace levels of health-related biochemical markers. Wearable sweat sensors have shown great potential for monitoring physiological health (e.g., hydration), disease diagnosis and management (e.g., diabetes and gout), and therapeutic drug monitoring (e.g., pain management). However, the presence of skin as a mechanical barrier prevents continuous access to this information-rich biofluid, and thus a trigger system (i.e., physical movement, thermal stimulation, or iontophoresis) is required to provide continuous access to sweat samples. In contrast to such active stimulation methods, the natural sweat pathway has demonstrated enormous potential for achieving simple, easy, and continuous access to sweat fluid for chemical analysis. The high density of eccrine sweat glands (~400 cm -2Taking advantage of the high sweating rate and resulting high sweating rate, fingertip biosensors have been reported recently for the sensing of important sweat biomarkers (e.g., glucose, vitamin C, and cortisol). Taking advantage of such natural thermoregulatory sweat samples, a fingertip L-dopa biosensor based on some embodiments of the disclosed technology can continuously monitor the dynamic profile of sweat L-dopa upon ingestion of a standard anti-Parkinson's drug containing L-dopa-carbidopa (100:25 mg) (Figure 5A(a)). Current signal differences measured at 10 min intervals showed an increase in sweat L-dopa signal immediately after ingestion of the drug, reaching its peak level, after which the signal declined to its background level (Figure 5A(b)). Interestingly, signal validation of the sweat samples obtained performed on capillary blood samples showed a similar pharmacokinetic profile with a negligible (~10 min) lag time. The analysis of L-Dopa simply relies on touching the porous hydrogel on the electrode surface with the index finger to allow rapid transfer of natural sweat to the electrode modified with immobilized tyrosinase enzyme (Figure 5B), where sweat L-Dopa is oxidized to dopaquinone via its reaction with the immobilized tyrosinase enzyme. The enzymatically generated dopaquinone is electrochemically reduced back to L-Dopa at an applied potential of -0.3 V, and the resulting amperometric signal correlates with the dynamically fluctuating levels of L-Dopa. Such a noninvasive, fast, and simple touch-based procedure shows considerable promise in guiding dose adjustment in PD patients via capturing real-time fluctuations in sweat L-Dopa levels.

[0308] Towards individualised treatment including fine-tuned L-Dopa dosing for the management of PTMs of other drugs and other diseases

[0309] Referring back to Figures 5A-5B, the techniques of the present disclosure can be implemented in some embodiments to provide a fingertip L-Dopa biosensor. Figure 5A(a) is a schematic of the finger touch-based procedure before and after ingestion of an anti-Parkinson's drug, and Figure 5A(b) is a typical current-time profile recorded every 10 minutes. Figure 5B(a) is a schematic of the mechanism underlying L-Dopa detection, which (a) begins with touching the sensor with the index finger, and (b) transfers natural sweat containing L-Dopa from the skin surface through a porous hydrogel to the electrode surface, which is measured electrochemically with a tyrosinase-immobilized electrode.

[0310] Referring again to Figures 6A-6C, the disclosed technology may be implemented in some embodiments to provide L-Dopa monitoring using a touch-based sensor. Figure 6A shows the time course of a cycle of L-Dopa detection in fingertip sweat, including measurements before contact (2 min), after contact (2 min), and after contact (2 min), and waiting for the next cycle (4 min). Figure 6B shows the dynamic pharmacokinetic profile during ingestion of an L-Dopa / C-Dopa pill by a subject. Figure 6C shows the corresponding chronoamperograms taken from time -10 min to +60 min (for clarity, the three initial amperograms at -40, -30, and -20 min are not shown), with the black and red curves representing before and after contact, respectively.

[0311] L-dopa detection is achieved by coupling L-dopa oxidation catalyzed by the tyrosinase enzyme (catecholase activity) with the subsequent electrochemical reduction of the corresponding quinone product, dopaquinone, at low potential (Figure 6A). The formed reaction cycle not only enhances the sensitivity through the amplification of the resulting current signal, but also prevents electrode fouling by inhibiting spontaneous polymerization reactions of the unstable quinone molecule. The tyrosinase enzyme is simply immobilized on the surface of a screen-printed carbon electrode, which is subsequently cross-linked with glutaraldehyde to prevent leaching of the enzyme (Figure 6A). To establish the optimal potential for L-dopaquinone reduction, the amperometric signals obtained at various potentials ranging from -0.1 to -0.4 V (vs. the internal pseudoreference Ag / AgCl) are compared upon the addition of 10 μM L-dopa. The current response dramatically increases when the potential is decreased from -0.1 to -0.3 V, after which the signal decreases, probably due to interference from the oxygen reduction reaction. Therefore, an optimal potential of -0.3 V is selected and used for subsequent in vitro and on-body experiments. Figure 6A shows the amperometric responses obtained as a result of 5-30 µM L-Dopa addition, giving a clear linearity within the entire physiological concentration range. The reproducibility of the L-Dopa sensor is assessed by measuring a 10 µM L-Dopa concentration on six different electrode sensors. The results revealed a high manufacturing reproducibility of the sensor with a relative standard deviation (RSD) of 2.6%. Furthermore, a carryover experiment is performed to evaluate the reproducibility of the sensor using 0 and 15 µM L-Dopa multiple times (n = 5) showing negligible drift on the signal.

[0312] The performance of the sensor to follow L-Dopa pharmacokinetics is characterized in healthy patients following administration of an L-Dopa / C-Dopa (100:25 mg) pill, a common oral medication for PD patients. C-Dopa is an amino acid (dopa) decarboxylase enzyme inhibitor and is combined with L-Dopa to increase the bioavailability of the drug. C-Dopa is an o-diphenolic compound that is oxidized by the tyrosinase enzyme and thus may interfere with target L-Dopa detection. The selectivity of the sensor is achieved by detecting L-Dopa / C-Dopa at a concentration ratio of 4:1, similar to the tablet composition. The overall response obtained from C-Dopa showed ~20% when the same concentration as L-Dopa was used, whereas only ~6% of the current was observed when a quarter concentration, reflecting the relative amount in the drug, was applied. These selectivity tests indicate minimal interference of C-Dopa, which is desired for accurate and reliable L-Dopa detection. Typical measurements of target L-Dopa after tablet ingestion are performed at 10 min intervals. Figure 6A shows the optimal time course of a single 10-min cycle of the on-body L-Dopa sensing protocol. This involves first recording background current on a buffer-soaked porous hydrogel-coated electrode (without fingertip contact) by XXX for 2 min, followed by placing the index finger (covering the working electrode) on the gel for 2 min during which sweat diffuses onto the electrode surface, followed by stepping the potential to -0.3 V and recording the current signal for 2 min. Following each cycle, the subject is asked to wait 4 min before beginning the next cycle. Figure 6B shows a typical peak-shaped dynamic profile of a subject's sweat L-Dopa levels over a 100-min test period, including five and six measurements before and after taking a tablet, respectively. The corresponding raw current signal and background are shown in Figure 6C. These data in Figures 6B and 6C show very little change before tablet ingestion, with the L-Dopa current signal beginning to increase 10 min after tablet ingestion, reaching its peak maximum at time 30 min, after which the signal decreases back to its background level at approximately 1 h after tablet ingestion. These results clearly demonstrate that the touch-based L-Dopa sensor can successfully track the fluctuations in L-Dopa sweat levels.As shown below, the temporal profile of such a peak shape closely matches the corresponding blood L-Dopa concentration (with a short, ~10 min time delay).

[0313] Referring again to FIG. 7, the individualized pharmacokinetic profiles of the L-Dopa drug in three different healthy subjects are shown: (A) Chronoamperograms (a-c) obtained every 10 min for the three subjects, black and red curves showing before and after contact current measurements; (B) Associated temporal current profiles for the three subjects. The blue dotted lines in each figure indicate the time of tablet ingestion.

[0314] To gain further insight into the individualized bodily responses upon drug ingestion, the performance of the finger touch biosensor is evaluated for three different individuals while taking the same drug under conditions kept identical (Figure 7(a)-(c)). The subjects are asked not to eat any food or drink caffeinated liquids for 2 h before the experiment to eliminate any undesired interference from dietary proteins or caffeine. The same protocol as in Figure 6A is followed to carry out sweat collection and signal recording in each subject. Interestingly, all three subjects showed similar temporal profiles of sweat L-Dopa levels, reaching their maximum peak 30 min after tablet ingestion. In addition, different peak currents are observed within the three subjects, with the highest and lowest signals obtained for the first subject (a) and the third subject (c), respectively. This may be partly due to the different sweat secretion rates of the subjects, but also implies a large inter-individual variability in the pharmacokinetic profile upon L-Dopa drug ingestion.

[0315] Figure 62 shows pharmacokinetic correlation of response to L-dopa using natural sweat and capillary blood samples: (A)-(B) Continuous monitoring of sweat (black) and blood (red) L-dopa every 10 min in different subjects; (C)-(D) Results of control experiments performed with electrodes without pill consumption (C) and without enzyme modification (D). The letter "P" indicates the time of tablet ingestion.

[0316] Although plasma is the "gold standard" matrix for therapeutic monitoring of L-Dopa, this analytical method relies on LC-MS-intensive instruments. To further confirm the reliability of the protocol we developed based on touch-based sweat L-Dopa detection, we investigated the feasibility of data validation between sweat and blood samples. Two subjects performed fingertip sweat sensing in parallel with electrochemical measurements of finger-pricked capillary blood samples using an enzymatic L-Dopa sensor. As shown in Figure 62(A)-(B), the sweat sensor can probe and successfully detect L-Dopa fluctuations in microliter blood samples, as well as fingertip natural sweat samples. Similar temporal profiles were observed in the blood and sweat experiments, with a lag time of approximately 10 min. Blood XX is elevated after taking the pill compared to XX. This first demonstration of pharmacokinetic correlation for sweat and blood L-Dopa profiles suggests significant potential of the developed procedure for tracking the dynamic pharmacokinetic behavior of PD patients in a noninvasive and continuous manner. In addition, control experiments are also performed without tablet ingestion (FIG. 62(C)) and using a non-enzymatic electrode (FIG. 62(D)). In both control experiments, the current signal remains small and unchanged, indicating the signal specificity of the fingertip touch-based sweat sensing procedure.

[0317] Non-invasive sweat measurements therefore offer considerable potential for tracking the pharmacokinetic profile of L-dopa following single-dose microtablet administration.

[0318] FIG. 63 shows an exemplary method 6300 for determining a concentration of an analyte in at least one of an individual's blood, sweat, or interstitial fluid (ISF) based on some embodiments of the disclosed technology.

[0319] In some implementations, the method 6300 includes: in step 6310, a sweat sample from an individual's finger or other sweat gland-covered skin surface is acquired by a device, deposited on a sweat-permeable layer of a device disclosed in this patent document; in step 6320, a plurality of measurement levels of an analyte are acquired using a signal from the device disclosed in this patent document; in step 6330, for each of the plurality of measurement levels of the analyte, a measurement of the concentration of the analyte in the individual's blood is acquired; in step 6340, linear slope and intercept parameters are acquired for the dependence between the acquired measurement of the concentration of the analyte in the individual's blood and the acquired measurement level of the analyte in the individual's sweat; in step 6350, the linear slope and intercept parameters are used to convert the new measurement level of the analyte in the individual's sweat to an estimate of the concentration of the analyte in the blood.

[0320] FIG. 64 shows an exemplary method 6400 for determining a concentration of an analyte in at least one of an individual's blood, sweat, or interstitial fluid (ISF) based on some embodiments of the disclosed technology.

[0321] In some implementations, the method 6400 includes: in step 6410, a sweat sample from an individual's finger is acquired by a device disclosed in this patent document, the sweat sample being deposited on a sweat-permeable layer of the device; in step 6420, a plurality of measurement levels of an analyte are acquired using a signal from the device disclosed in this patent document; in step 6430, for each of the plurality of measurement levels of the analyte, a measurement of the concentration of the analyte in the individual's blood is acquired; in step 6440, an exponent parameter, a multiplier parameter, and an intercept parameter are obtained for the dependence between the acquired measurement result of the concentration of the analyte in the individual's blood and the acquired measurement level of the analyte in the individual's sweat; in step 6450, the exponent parameter, the multiplier parameter, and the intercept parameter are used to convert the new measurement level of the analyte in the individual's sweat to an estimated concentration of the analyte in the individual's blood.

[0322] FIG. 65 shows an exemplary method 6500 for determining the concentration of an analyte in the blood of an individual based on some embodiments of the disclosed technology.

[0323] In some implementations, the method 6500 includes: In step 6510, a sweat sample from the individual's finger is obtained by the device, the sweat sample being deposited on a sweat-permeable layer of the device disclosed in this patent document; in step 6520, a plurality of groups of measurements of an analyte level in the individual's sweat using a signal from the device disclosed in this patent document, the sweat being collected by the device from the individual's finger in contact with the sweat-permeable layer of the device; in step 6530, for each group of measurements of an analyte level in the individual's sweat, a corresponding group of measurements of the analyte concentration in the individual's blood is obtained; in step 654030, for each group of measurements of an analyte level in the individual's sweat, values ​​of a linear slope parameter and an intercept parameter, respectively, relating to the dependence between the measurements in the group and the measurements in the corresponding group of measurements of the analyte concentration in the individual's blood are obtained; in step 6550, an average value of the linear slope parameter and an average value of the intercept parameter are determined for the group of measurements of an analyte level in the individual's sweat; in step 6560, the concentration of the analyte in the individual's blood is determined based on the determined average value of the linear slope parameter and the determined average value of the intercept parameter.

[0324] FIG. 66 illustrates an example method 6600 for generating power using sweat analytes according to some embodiments of the disclosed technology.

[0325] In some implementations, the method 6600 includes: in step 6610, placing a device on a skin surface having sweat glands to collect sweat analytes for biocatalysis in a plurality of electrodes and generating a current from the plurality of electrodes of the device disclosed in this patent document. Sweat is collected by the device from a finger on the skin covered with sweat glands through a sweat-permeable layer of the device; in step 6620, applying pressure to the device via finger pressure to generate a current from the plurality of electrodes and collecting energy in a storage unit, either directly in the highly porous electrodes of the device or through a volumetric adjustment circuit.

[0326] FIG. 67 illustrates an exemplary method 6700 for determining a concentration of a biological fluid analyte in an individual according to some embodiments of the disclosed technology.

[0327] In some implementations, the method 6700 includes: in step 6710, acquiring by the device a sweat sample from the individual's finger, the sweat sample being deposited on a sweat-permeable layer of the device disclosed in this patent document; in step 6720, acquiring a plurality of measurement levels of a biofluid analyte in the individual's sweat using a self-generated signal or an open circuit voltage from the device; In step 6730, for each of a plurality of measurement levels of a biofluid analyte in the individual's sweat, a voltage signal without external action of a constant voltage or current is obtained by discharging through a resistive load between the anode and cathode of the plurality of electrodes; in step 6740, for each of a plurality of measurement levels of a biofluid analyte in the individual's sweat, regulated power or power stored in power electronics that obtains a signal from the plurality of electrodes is discharged from the device's biofuel cell.

[0328] FIG. 68 shows an example of a device 6800 for collecting sweat for estimating blood analyte concentrations or utilizing analyte redox reactions for energy generation based on some embodiments of the disclosed technology.

[0329] 68, the device 6800 comprises: a substrate 6810; a plurality of electrodes 6820 disposed on the substrate 6810 and operable to detect an analyte in sweat of an individual; a sweat-permeable layer 6830 including a hydrogel and having a first side and a second side opposite the first side. The first side of the sweat-permeable layer is in contact with the plurality of electrodes 6820, the plurality of electrodes 6820 being disposed between the substrate and the first side of the sweat-permeable layer, and the sweat-permeable layer allows an analyte in sweat applied to the second side to permeate through the sweat-permeable layer through the first side of the sweat-permeable layer 6830 to reach the plurality of electrodes 6820.

[0330] Thus, various implementations of the features of the disclosed technology may be made based on the above disclosure, including the examples listed below.

[0331] Examples 1 to 47

[0332] Example 1. A device for sweat-based estimation of blood analyte concentration, comprising: a substrate; a sensor disposed on the substrate and operable to detect an analyte in sweat; and a sweat-permeable layer having a first side and a second side disposed opposite the first side. The first side of the sweat-permeable layer is in contact with the sensor, the sensor being disposed between the substrate and the first side of the sweat-permeable layer, and the sweat-permeable layer is structured to allow sweat applied to the second surface to pass through the sweat-permeable layer and reach the sensor through the first surface of the sweat-permeable layer.

[0333] Example 2. The device of example 1, wherein the sensor is one of an electrochemical sensor, an affinity-based sensor, or an optical sensor.

[0334] Example 3. The device of example 1, wherein the sweat-permeable layer comprises a layer of hydrogel.

[0335] Example 4. The device of Example 3, wherein the hydrogel comprises one of polyvinyl alcohol (PVA), agarose, or glycerol.

[0336] Example 5. The device of example 1, wherein the analyte is glucose and the sensor comprises an electrochemical sensor including a reference electrode, a working electrode, and a counter electrode, the reference electrode comprising silver, and the working electrode comprising Prussian blue and glucose oxidase.

[0337] Example 6. The device of Example 1, comprising a processor and a memory, the memory storing instructions that, when executed by the processor, convert an output signal from the sensor corresponding to a concentration of an analyte in sweat into a numerical value corresponding to the concentration of the analyte in blood.

[0338] Example 7. A method for determining a blood analyte concentration, comprising: obtaining several measurements of the level of an analyte in an individual's sweat using a signal from a sensor of an apparatus described in any of Examples 1-6, where the sweat is collected by the device from the person's finger touching a sweat-permeable layer of the device; obtaining a measurement of the concentration of the analyte in the individual's blood for each of the several measurements of the level of the analyte in the individual's sweat; obtaining linear slope and intercept parameters for the dependence between the concentration of the analyte in the individual's blood and the measurements of the level of the analyte in the individual's sweat; using the linear slope and intercept parameters to convert new measurements of the level of the analyte in the individual's sweat into estimates of the concentration of the analyte in the individual's blood.

[0339] Example 8. A method for determining a blood analyte concentration, comprising: obtaining several groups of measurements of an analyte level in an individual's sweat using a signal from a sensor of an apparatus according to any one of Examples 1 to 6, the sweat being collected by the device from a person's finger touching a sweat-permeable layer of the device; obtaining, for each group of measurements of an analyte level in the individual's sweat, a corresponding group of measurements of the analyte concentration in the individual's sweat by obtaining a corresponding group of measurements of the analyte concentration in the individual's sweat; obtaining, for each group of measurements of an analyte level in the individual's sweat, values ​​of a linear slope parameter and an intercept parameter for the dependence between the measurements in the group and the measurements in the corresponding group of measurements of the analyte concentration in the individual's blood; determining an average value of the linear slope parameter and an average value of the intercept parameter for the group of measurements of an analyte level in the individual's sweat; determining the concentration of the analyte in the individual's blood using the measurements of the analyte level in the individual's sweat provided by the device using the determined average value of the linear slope parameter and the determined average value of the intercept parameter.

[0340] Example 9. A sweat collection device for estimating a concentration of an analyte in an individual's blood or utilizing an oxidation-reduction reaction of the analyte for energy generation, comprising: a substrate; one or more electrodes disposed on the substrate and operable to detect the analyte in sweat and / or collect energy from the analyte in sweat; a sweat-permeable layer having a first side and a second side opposite the first side, the first side of the sweat-permeable layer in contact with the one or more electrodes such that the one or more electrodes are disposed between the substrate and the first side of the sweat-permeable layer; and the sweat-permeable layer is configured to allow sweat applied to the second side to permeate through the sweat-permeable layer and reach the one or more electrodes through the first side of the sweat-permeable layer.

[0341] Example 10. The device of Example 9, wherein the one or more electrodes are part of one of an electrochemical sensor, an affinity-based sensor, an optical sensor, a catalytic fuel cell, or a biocatalytic fuel cell.

[0342] Example 11. The device of example 9, wherein the sweat-permeable layer comprises a layer of hydrogel.

[0343] Example 12. The device of example 11, wherein the hydrogel comprises at least one of polyvinyl alcohol (PVA), polyacrylic acid (PAA), polymethyl methacrylate (PMMA), polyethylene oxide (PEO), polyacrylamide (PAM), cellulosic materials, agar, gelatin, agarose, alginate, glycerol, ethylene carbonate, or propylene carbonate.

[0344] Example 13 The device of example 12, wherein the cellulosic material is one of cellulose, methylcellulose, ethylcellulose, carboxymethylcellulose, or hydroxyethylcellulose.

[0345] Example 14. A device according to any one of Examples 11 to 13, wherein the hydrogel is disposable after each use of the device.

[0346] Example 15. A device according to any of Examples 11 to 13, wherein the hydrogel is reusable.

[0347] Example 16. The device of Example 15, further comprising a container or compartment, the container or compartment configured for disposing the hydrogel in the container or compartment, storing the hydrogel in the container or compartment, and retrieving the hydrogel from the container or compartment.

[0348] Example 17. The device of any of Examples 9-16, wherein the analyte is glucose and the one or more electrodes form an electrochemical sensor including a reference electrode, a working electrode, and a counter electrode, the reference electrode comprising silver, and the working electrode comprising Prussian blue and glucose oxidase.

[0349] Example 18. The device of any of Examples 9-16, wherein the analyte is lactate, the one or more electrodes comprise an electrocatalytic anode and a cathode, the cathode comprising one of a catalyst configured to promote an oxygen reduction reaction comprising at least one of platinum, carbon black, carbon nanotubes, bilirubin oxidase, laccase, a platinum-cobalt alloy, a platinum-iron alloy, a platinum-gold alloy, a platinum-nickel alloy, or an oxidant that can be reduced comprising one of a silver oxide, a nickel oxide, or a manganese oxide, and the anode comprising lactate oxidase and a reaction mediator.

[0350] Example 19. The device of example 18, wherein the reaction mediator is one of tetrathiafulvalene (TTF), naphthoquinone (NQ), ferrocene, or a derivative of ferrocene.

[0351] Example 20. The device of example 19, wherein the derivative of ferrocene is one of methylferrocene or dimethylferrocene.

[0352] Example 21. The device of example 18, wherein the reaction mediator is tetrathiafulvalene (TTF), naphthoquinone (NQ), ferrocene, or a complex of a derivative of ferrocene with one or more compounds.

[0353] Example 22. The device of Example 21, wherein the reaction mediator is tetrathiafulvalene tetracyanoquinodimethane.

[0354] Example 23. The device of example 9, wherein an electrode in the one or more electrodes comprises a carbonaceous material, an elastomeric binder, and a redox active material, and the electrode is structured to have porosity created by adding template particles to the electrode during its manufacture and subsequently removing the template particles from the electrode.

[0355] Example 24. The device of example 23, wherein the carbonaceous material comprises one of graphite, carbon black, carbon nanotubes, or graphene.

[0356] Example 25. The device of example 23, wherein the elastomeric binder comprises one of a styrenic triblock copolymer, a fluorinated rubber, a polyethylene vinyl acetate, a polyurethane, an ecoflex, or a polydimethylsiloxane.

[0357] Example 26. The device of example 25, wherein the styrenic triblock copolymer is one of polystyrene-polyisoprene-polystyrene or polystyrene-polybutylene-polyethylene-polystyrene.

[0358] Example 27. The device of example 25, wherein the fluorinated rubber is poly(vinyl fluoride-tetrafluoropropylene).

[0359] Example 28 The device of example 23, wherein the template particles comprise one of a salt, sucrose, a metal, or a polymer.

[0360] Example 29 The device of example 28, wherein the salt is one of sodium chloride or sodium bicarbonate.

[0361] Example 30. The device of example 28, wherein the metal is one of Mg or Zn.

[0362] Example 31. The device of example 28, wherein the polymer is styrene.

[0363] Example 32. The device of example 23, wherein the redox active material comprises one of a conductive polymer, a 2-D material, or an MXene.

[0364] Example 33. The device of example 32, wherein the conductive polymer is poly(3,4-ethylenedioxythiophene) polystyrene sulfonate.

[0365] Example 34. The device of example 32, wherein the 2-D material is molybdenum disulfide.

[0366] Example 35. The device of example 32, wherein the MXene is Ti2C3.

[0367] Example 36. The device of Example 9, wherein an electrode in the one or more electrodes comprises a conductive polymer and a redox active material co-deposited with the conductive polymer on the electrode, and the electrode is structured to have one or more recognition cavities structured to selectively bind the analyte.

[0368] Example 37. The device of example 36, wherein the conductive polymer is one of polypyrrole, polyethyleneimine, or polyaniline.

[0369] Example 38. The device of example 36, wherein the redox-active material comprises a mediator or an organic dye.

[0370] Example 39. The device of Example 9, comprising a voltage regulation circuit coupled to at least one of the one or more electrodes and configured to collect electrical energy generated by the device and store the energy in an energy storage device.

[0371] Example 40. The device of example 39, wherein the energy storage device is one of a capacitor, a supercapacitor, a battery, or a combination thereof.

[0372] Example 41. A method for generating power using collected sweat analytes, comprising: placing a device described in any of Examples 9-40 on a skin area covered with sweat glands to collect analytes for biocatalytic reactions in one or more electrodes of the device and generating a current from the one or more electrodes of the device, wherein sweat is collected by the device from the skin area covered with sweat glands through a sweat-permeable layer of the device; collecting the generated current in a storage unit directly or through a voltage regulating circuit; and discharging from the storage unit.

[0373] Example 42 The method of Example 41 further includes applying pressure to the device against the area of ​​skin using a finger.

[0374] Example 43 The method according to example 42, wherein the pressure application is performed sporadically or periodically.

[0375] Example 44 The method according to example 41, wherein the storage unit is an electrode of the device.

[0376] Example 45. A method for determining the concentration of an analyte in the blood of an individual, comprising: obtaining several measurements of the level of the analyte in the individual's sweat using a signal from an apparatus described in any of Examples 9 to 39, where the sweat is collected by the device from a person's finger touching a sweat-permeable layer of the device; obtaining a measurement of the concentration of the analyte in the individual's blood for each of several measurements of the level of the analyte in the individual's sweat; obtaining an exponent parameter, a multiplier parameter and an intercept parameter for the dependence between the obtained measurements of the concentration of the analyte in the individual's blood and the obtained measurements of the level of the analyte in the individual's sweat; converting new measurements of the level of the analyte in the individual's sweat into estimates of concentration using the exponent parameter, multiplier parameter and intercept parameter.

[0377] Example 46. Methods, systems, and devices described herein.

[0378] Example 47. Any combination of the above examples.

[0379] Examples A1 to A51

[0380] In some embodiments according to the present technology (Example A1), a device includes: a substrate; a plurality of electrodes disposed on the substrate and operable to detect an analyte in sweat of an individual; a sweat-permeable layer including a hydrogel and having a first side and a second side opposite the first side, the first side of the sweat-permeable layer being in contact with the plurality of electrodes such that the plurality of electrodes are disposed between the substrate and the first side of the sweat-permeable layer, and the sweat-permeable layer is configured to transfer naturally produced sweat including the analyte from an individual's fingertip by transmitting the naturally produced sweat through the sweat-permeable layer from the second side to the first side to reach the plurality of electrodes.

[0381] Example A2 includes the device of any of Examples A1-A37, further comprising a processor configured to estimate the concentration of the analyte in the individual's blood by comparing the concentration of the analyte in the sweat to the concentration of the analyte in the blood measured by a reference device.

[0382] Example A3 includes the device of example A2 or any of examples A1-A37, and further includes a memory configured to store instructions that, when executed by a processor, convert an output signal from the device corresponding to the concentration of the analyte in sweat into a numerical value corresponding to the concentration of the analyte in blood.

[0383] Example A4 includes the device of any of Examples A1-A37, and further includes a voltage regulation circuit including a voltage generator coupled to the plurality of electrodes to generate electricity by using an oxidation-reduction reaction of an analyte in sweat, and an energy storage device coupled to the voltage generator to store the generated electricity.

[0384] Example A5 includes the device of example A4 or any of examples A1-A37, wherein the voltage regulation circuit increases a voltage when connected to the multiple electrodes to increase and store an input signal from the multiple electrodes in the energy storage device.

[0385] Example A6 includes the device of any of Examples A1-A37, wherein the plurality of electrodes is part of one of an electrochemical sensor, an affinity-based sensor, an optical sensor, a catalytic fuel cell, or a biocatalytic fuel cell.

[0386] Example A7 includes the device of any of Examples A1-A37, wherein the hydrogel includes at least one of polyvinyl alcohol (PVA), polyacrylic acid (PAA), polymethyl methacrylate (PMMA), polyethylene oxide (PEO), polyacrylamide (PAM), a cellulosic material, agar, gelatin, agarose, alginate, glycerol, ethylene carbonate, or propylene carbonate.

[0387] Example A8 includes the device of Example A7 or any of Examples A1-A37, wherein the hydrogel is structured to have a plurality of pores with a pore size of at least 50 nm that inhibit bulk fluid flow.

[0388] Example A9 includes the device of Example A8 or any of Examples A1-A37, where the hydrogel is made by adding templating particles to the hydrogel after crosslinking, and then removing the templating particles from the hydrogel.

[0389] Example A10 includes the device of example A7 or any of examples A1-A37, where the cellulosic material includes at least one of cellulose, methyl cellulose, ethyl cellulose, carboxymethyl cellulose, or hydroxyethyl cellulose.

[0390] Example A11 includes the device of any of Examples A7-A10 or any of Examples A1-A37, where the hydrogel is disposable after each use of the device.

[0391] Example A12 includes the device of any of Examples A7-A10 or any of Examples A1-A37, in which the hydrogel is crosslinked directly on the surface of the multiple electrodes.

[0392] Example A13 includes the device of any of Examples A7-A10 or any of Examples A1-A37, where the hydrogel is reusable.

[0393] Example A14 includes the device of example A13 or any of examples A1-A37, further including a container configured to store the hydrogel and to retrieve the hydrogel.

[0394] Example A15 includes the device of any of Examples A1-A37, wherein the analyte is glucose, and the plurality of electrodes form an electrochemical sensor including a reference electrode, a working electrode, and a counter electrode, the reference electrode includes silver, and the working electrode includes Prussian blue and glucose oxidase.

[0395] Example A16 includes the device of any of Examples A1-A37, wherein the analyte is lactate, the plurality of electrodes includes an electrocatalytic anode and a cathode, the cathode includes a catalyst configured to promote an oxygen reduction reaction including at least one of platinum, carbon black, carbon nanotubes, bilirubin oxidase, laccase, a platinum-cobalt alloy, a platinum-iron alloy, a platinum-gold alloy, a platinum-nickel alloy, or an oxidant that can be reduced including at least one of silver oxide, nickel oxide, or manganese oxide, and the anode includes lactate oxidase and a reaction mediator.

[0396] Example A17 includes the device of Example A16 or any of Examples A1-A37, wherein the reaction mediator includes at least one of tetrathiafulvalene (TTF), naphthoquinone (NQ), ferrocene, or a derivative of ferrocene.

[0397] Example A18 includes the device of Example A17 or any of Examples A1-A37, where the derivative of ferrocene includes at least one of methylferrocene or dimethylferrocene.

[0398] Example A19 includes the device of Example A16 or any of Examples A1-A37, where the reaction mediator includes tetrathiafulvalene tetracyanoquinodimethane.

[0399] Example A20 includes the device of any of Examples A1-A37, wherein the plurality of electrodes includes a first electrode including a carbonaceous material, an elastomeric binder, and a redox active material, wherein the first electrode is structured to have porosity created by adding template particles to the first electrode and then removing the template particles from the first electrode.

[0400] Example A21 includes the device of Example A20 or any of Examples A1-A37, wherein the carbonaceous material includes one of graphite, carbon black, carbon nanotubes, or graphene.

[0401] Example A22 includes the device of Example A20 or any of Examples A1-A37, wherein the elastomeric binder includes at least one of a styrenic triblock copolymer, a fluorinated rubber, a polyethylene vinyl acetate, a polyurethane, an ecoflex, or a polydimethylsiloxane.

[0402] Example A23 includes the device of Example A22 or any of Examples A1-A37, wherein the styrenic triblock copolymer includes at least one of polystyrene-polyisoprene-polystyrene or polystyrene-polybutylene-polyethylene-polystyrene.

[0403] Example A24 includes the device of Example A22 or any of Examples A1-A37, where the fluorinated rubber includes poly(vinyl fluoride-tetrafluoropropylene).

[0404] Example A25 includes the device of example A9 or example A20 or any of examples A1-A37, wherein the template particles include at least one of a salt, a saccharide, a metal, or a polymer.

[0405] Example A26 includes the device of Example A25 or any of Examples A1-A37, wherein the salt includes at least one of sodium chloride or sodium bicarbonate.

[0406] Example A27 includes the device of Example A25 or any of Examples A1-A37, where the metal includes at least one of Mg or Zn.

[0407] Example A28 includes the device of Example A25 or any of Examples A1-A37, where the sugar includes at least one of glucose, sucrose, fructose, maltodextrin, starch, or maltose.

[0408] Example A29 includes the device of Example A25 or any of Examples A1-A37, wherein the polymer includes polystyrene, polyethylene glycol, polyacrylamide, polyacrylic acid copolymer, polyethyleneimine, or polyvinyl alcohol.

[0409] Example A30 includes the device of example A20 or any of examples A1-A37, where the redox-active material includes one of a conducting polymer, a 2-D material, or an MXene.

[0410] Example A31 includes the device of Example A30 or any of Examples A1-A37, where the conductive polymer includes poly(3,4-ethylenedioxythiophene) polystyrenesulfonate.

[0411] Example A32 includes the device of Example A30 or any of Examples A1-A37, where the 2-D material includes molybdenum disulfide.

[0412] Example A33 includes the apparatus of Example A30 or any of Examples A1 to A37, and the MXene includes Ti2C3, Ti2C, V2C, or Ti4N3.

[0413] Example A34 includes the device of any of Examples A1-A37, where the plurality of electrodes includes a conductive polymer, a redox-active material, and a target analyte molecule of the device.

[0414] Example A35 includes the device of Example A34 or any of Examples A1-A37, wherein the conductive polymer includes at least one of polypyrrole, polyethyleneimine, polyaniline, or poly(3,4-ethylenedioxythiophene) polystyrenesulfonate formed by direct dispersion deposition or by applying a constant voltage / current or a range of voltages that are repeatedly scanned over a controlled amount of time.

[0415] Example A36 includes the device of Example A34 or any of Examples A1-A37, where the redox-active material includes a mediator or organic dye that is codeposited onto one or more of the electrodes during electrodeposition of the conductive polymer.

[0416] Example A37 includes the device of Example A34 or any of Examples A1-A36, wherein the target analyte molecule includes at least one of cortisol, insulin, levodopa, or a protein, and the plurality of electrodes includes a molecularly imprinted polymer electrode formed by applying a constant voltage, a repeatedly scanned voltage range, an aqueous solution, or an organic solution for a controlled time, whereby at least one of cortisol, insulin, levodopa, or a protein leaks from the plurality of electrodes, and the molecularly imprinted polymer electrode includes a recognition cavity that selectively binds an analyte in sweat.

[0417] In some embodiments according to the present technology (Example A38), the device includes: a piezoelectric chip; two or more electrodes including an anode electrode and a cathode electrode formed on the piezoelectric chip, the electrodes operable to detect an electrical signal associated with a chemical reaction associated with an analyte contained in the sweat input in the area of ​​the surface of the anode electrode and the cathode electrode; a current collector including two or more conductive material structures disposed between the piezoelectric chip and the two or more electrodes, electrically coupling at least one of the conductive material structures to the anode electrode and at least another one of the conductive material structures to the cathode electrode; a sweat-permeable layer including a hydrogel and having a first side and a second side opposite the first side. The first side of the sweat-permeable layer is in contact with the two or more electrodes and is configured to move sweat naturally generated from the fingertip of the individual from the second side pressed by the fingertip of the individual toward the first side by penetrating the sweat-permeable layer to reach the two or more electrodes. When the second side of the sweat-permeable layer is pressed with the individual's fingertip, the piezoelectric chip undergoes a non-destructive mechanical deformation and generates electrical energy from the non-destructive mechanical deformation of the piezoelectric chip.

[0418] Example A39 includes the device of any of Examples A37-A45, wherein the two or more electrodes are operable to measure a parameter of the analyte in the sweat based on the detected electrical signal.

[0419] Example A40 includes the device of any of Examples A37-A45, further including a substrate disposed below the piezoelectric chip; and two or more spacers disposed below the piezoelectric chip and above the substrate, the spacers having a first thickness that facilitate non-destructive mechanical deformation of the piezoelectric chip.

[0420] Example A41 includes the device of any of Examples A37-A45, where the hydrogel includes a porous polyvinyl alcohol (PVA) hydrogel.

[0421] Example A42 includes the device of any of Examples A37-A45, wherein two or more of the electrodes include three-dimensional (3D) carbon nanotube (CNT) foam.

[0422] Example A43 includes the device of Example A42 or any of Examples A37-A45, where the cathode electrode includes particles comprising platinum within the pores or cavities within the 3D CNT foam of the cathode electrode.

[0423] Example A44 includes the device of Example A43 or any of Examples A37-A45, where the analyte includes lactate and the anode electrode includes lactate oxidase (LOx) within the pores or cavities of the 3D CNT foam of the anode electrode.

[0424] Example A45 includes the device of Example A44 or any of Examples A37-A43, wherein the anode electrode further includes at least one of an enzyme or a mediator.

[0425] In some embodiments according to the present technology (Example A46), a method for determining a concentration of an analyte present in at least one of an individual's blood, sweat, or interstitial fluid (ISF) comprises: obtaining a sweat sample by a device according to any one of claims 1 to 45 by depositing the sweat from the individual's finger onto the sweat-permeable layer of the device; obtaining a plurality of measurements of a level of the analyte using a signal from the device; obtaining a measurement of the concentration of the analyte in the individual's blood for each of the plurality of measurements of the level of the analyte; obtaining linear slope and intercept parameters for a dependence between the obtained measurements of the concentration of the analyte in the individual's blood and the obtained measurements of the level of the analyte in the individual's sweat; using the linear slope and intercept parameters to convert the new measurement of the level of the analyte in the individual's sweat into an estimate of the concentration of the analyte in the individual's blood.

[0426] In some embodiments according to the present technology (Example A47), a method for determining a concentration of an analyte present in at least one of an individual's blood, sweat, or interstitial fluid (ISF) comprises: obtaining a sample of sweat by depositing the sweat from the individual's finger onto the sweat-permeable layer of the device using a device described in any one of claims 1 to 45; obtaining a plurality of measurements of a level of the analyte using a signal from the device; obtaining a measurement of the concentration of the analyte in the individual's blood for each of the plurality of measurements of the level of the analyte; obtaining an exponent parameter, a multiplier parameter, and an intercept parameter for the dependence between the obtained measurements of the concentration of the analyte in the individual's blood and the obtained measurements of the level of the analyte in the individual's sweat; using the exponent parameter, the multiplier parameter, and the intercept parameter to convert the new measurement of the level of the analyte in the individual's sweat to an estimate of the concentration of the analyte in the individual's blood.

[0427] In some embodiments according to the present technology (Example A48), a method for determining a concentration of an analyte present in an individual's blood comprises: obtaining a sweat sample by a device according to any one of claims 1 to 45 from deposition of a sweat sample from the individual's finger onto a sweat-permeable layer of the device; obtaining a plurality of groups of measurements of the level of the analyte in the individual's sweat using a signal from the device; for each group of measurements of the level of the analyte in the individual's sweat obtaining a corresponding group of measurements of the concentration of the analyte in the individual's blood; for each group of measurements of the level of the analyte in the individual's sweat obtaining values ​​of a linear slope parameter and an intercept parameter for the dependence between measurements in the group and measurements in the corresponding group of measurements of the concentration of the analyte in the individual's blood; determining an average value of the linear slope parameter and an average value of the intercept parameter for a typical measurement level of the analyte in the individual's sweat; determining the concentration of the analyte in the individual's blood based on the determined average value of the linear slope parameter and the determined average value of the intercept parameter.

[0428] According to some embodiments (Example A49) of the present technology, a method for generating power using sweat analytes includes the steps of: placing a device on a skin surface having sweat glands to collect sweat analytes for biocatalytic reactions in multiple electrodes, and generating a current from the multiple electrodes of the device of any one of claims 1 to 45, wherein sweat is collected from a finger of infected covered skin by the device through a sweat permeable layer of the device; applying pressure to the device via finger pressure to generate a current from the multiple electrodes, and collecting power directly in the highly porous electrodes of the device or via a voltage regulation circuit to a storage unit.

[0429] In some embodiments according to the present technology (Example A50), a method for determining a concentration of a biofluid analyte in an individual includes: obtaining a sweat sample from the individual's finger by a device described in any of claims 1-45, the sweat sample being deposited on a sweat-permeable layer of the device; obtaining a plurality of measurements of the level of the biofluid analyte in the individual's sweat using a self-generated signal or open circuit voltage from the device; obtaining a voltage signal without externally applying a constant voltage or current, for each of the plurality of measurements of the level of the biofluid analyte in the individual's sweat, by discharging through a resistive load between the anode and cathode of the plurality of electrodes; discharging power from the device's biofuel cell, which is conditioned or stored in power electronics that obtains signals from the plurality of electrodes, for each of the plurality of measurements of the level of the biofluid analyte in the individual's sweat.

[0430] Example A51 includes a method or apparatus including any combination of any of Examples A1-A50.

[0431] Implementations of the subject matter and functional operations described in this patent document may be implemented in various systems, digital electronic circuits, or computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or in one or more combinations thereof. Implementations of the subject matter described herein may be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-transitory computer-readable medium for execution by or for controlling the operation of a data processing apparatus. The computer-readable medium may be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter that provides a machine-readable propagated signal, or a combination of one or more of them. The term "data processing unit" or "data processing apparatus" encompasses all apparatuses, devices, and machines for processing data, including, by way of example, a programmable processor, a computer, or multiple processors or computers. In addition to hardware, an apparatus may include code that creates an execution environment for a computer program, such as code that constitutes a processor firmware, a protocol stack, a database management system, an operating system, or one or more combinations of them.

[0432] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple cooperating files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program can be deployed to be executed on one computer, or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0433] The processes and logic flows described herein may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows may also be performed by, and an apparatus may be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0434] Processors suitable for executing computer programs include, by way of example, both general-purpose and special-purpose microprocessors, as well as any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer is also operatively coupled to receive data from, transfer data to, or both of, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical, or optical disks. However, a computer need not have such devices. Computer-readable media suitable for storing computer program instructions and data include, by way of example, all forms of non-volatile memory, media, and memory devices, including semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices. The processor and memory may be supplemented by, or incorporated in, special-purpose logic circuitry.

[0435] It is intended that the specification, together with the drawings, be considered as illustrative only, and that the illustrative means are by way of example. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly dictates otherwise. Additionally, the use of "or" is intended to include "and / or" unless the context clearly dictates otherwise.

[0436] Although this patent document contains many details, these should not be construed as a limitation on the scope of any invention or what may be claimed, but rather as a description of features that may be specific to certain embodiments of a particular invention. Certain features described in this patent document in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, features may be described above as acting in a particular combination, and even as initially claimed, one or more features from a claimed combination may in some cases be carved out of the combination, and the claimed combination may be subject to subcombinations or variations of the subcombination.

[0437] Similarly, although operations are depicted in the figures in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown, or in sequential order, or that all of the illustrated operations be performed, to achieve desired results. Further, the separation of various system components in the embodiments described in this patent document should not be understood as requiring such separation in all embodiments.

[0438] Only some implementations and examples are described; other implementations, extensions, and variations can be made based on what is described and illustrated in this patent document.

Claims

1. substrate; a plurality of electrodes disposed on the substrate and operable to detect an analyte in sweat of the individual; a sweat-permeable layer having a first side and a second side opposite the first side, the first side of the sweat-permeable layer in contact with the plurality of electrodes such that the plurality of electrodes are disposed between the substrate and the first side of the sweat-permeable layer; Equipped with the sweat-permeable layer is configured such that the sweat containing the analyte naturally produced from the individual's fingertip permeates through the sweat-permeable layer from the second side to the first side to reach the plurality of electrodes. device.

2. The device further comprises: a processor configured to estimate the concentration of the analyte in the individual's blood by comparing the concentration of the analyte in sweat to a concentration of the analyte in blood measured by a reference device. The device of claim 1 , comprising:

3. The device further comprises: a memory configured to store instructions that, when executed by the processor, cause the processor to convert an output signal from the device corresponding to the concentration of the analyte in sweat into a numerical value corresponding to the concentration of the analyte in blood. The device of claim 2 , comprising:

4. The device further comprises a voltage regulation circuit; The voltage adjustment circuit includes: a voltage generator coupled to the plurality of electrodes for generating electricity by using an oxidation-reduction reaction of the analyte in sweat; an energy storage device coupled to the voltage generator for storing the generated electricity; Equipped with The device of claim 1.

5. 5. The device of claim 4, wherein the voltage regulator circuit, when connected to the plurality of electrodes, increases a voltage to increase and store an input signal from the plurality of electrodes in the energy storage device.

6. The device of any one of claims 1 to 5, wherein the plurality of electrodes is part of one of an electrochemical sensor, an affinity-based sensor, an optical sensor, a catalytic fuel cell, or a biocatalytic fuel cell.

7. The device of claim 1 , wherein the sweat-permeable layer comprises a hydrogel.

8. The hydrogel comprises polyvinyl alcohol (PVA), polyacrylic acid (PAA), polymethyl methacrylate (PMMA), polyethylene oxide (PEO), polyacrylamide (PAM), cellulosic materials, agar, gelatin, agarose, alginate, glycerol, ethylene carbonate, or propylene carbonate; The device of claim 7 , comprising at least one of:

9. The device of claim 8 , wherein the hydrogel is structured to have a plurality of pores having a pore size of at least 50 nm that inhibit bulk fluid flow.

10. The device of claim 9 , wherein the hydrogel is made by adding and subsequently removing template particles to the hydrogel after crosslinking.

11. The device of any one of claims 7 to 10, wherein the hydrogel is disposable after each use of the device.

12. The device of any one of claims 7 to 10, wherein the hydrogel is crosslinked directly on the surface of the plurality of electrodes.

13. The device of any one of claims 7 to 10, wherein the hydrogel is reusable.

14. The device comprises a container; The device of claim 13 , wherein the container is configured to store the hydrogel within the container and to retrieve the hydrogel from the container.

15. 11. The device of any one of claims 1 to 5, 7 to 10, wherein the analyte is glucose and the plurality of electrodes form an electrochemical sensor including a reference electrode, a working electrode, and a counter electrode, the reference electrode comprising silver and the working electrode comprising Prussian blue and glucose oxidase.

16. the analyte is lactate; the plurality of electrodes includes electrocatalytic anodes and cathodes; the cathode includes a catalyst configured to promote an oxygen reduction reaction; the catalyst comprises at least one of platinum, carbon black, carbon nanotubes, bilirubin oxidase, laccase, platinum-cobalt alloy, platinum-iron alloy, platinum-gold alloy, platinum-nickel alloy, or a reducible oxidizing material comprising at least one of silver oxide, nickel oxide, or manganese oxide; the anode comprises lactate oxidase and a reaction mediator; A device according to any one of claims 1 to 5 and 7 to 10.

17. 17. The device of claim 16, wherein the reaction mediator comprises at least one of tetrathiafulvalene (TTF), naphthoquinone (NQ), ferrocene, or a derivative of ferrocene.

18. The device of claim 17 , wherein the derivatives of ferrocene include at least one of methylferrocene or dimethylferrocene.

19. The device of claim 16 , wherein the reaction mediator comprises tetrathiafulvalene tetracyanoquinodimethane.

20. the plurality of electrodes includes a first electrode including a carbonaceous material, an elastomeric binder, and a redox active material; The device of claim 1 , wherein the first electrode is configured to have porosity created by adding template particles to the first electrode and then removing the template particles from the first electrode.

21. 21. The device of claim 20, wherein the carbonaceous material comprises one of graphite, carbon black, carbon nanotubes, or graphene.

22. 21. The device of claim 20, wherein the elastomeric binder comprises at least one of a styrenic triblock copolymer, a fluorinated rubber, a polyethylene vinyl acetate, a polyurethane, an ecoflex, or a polydimethylsiloxane.

23. The device of claim 22, wherein the styrenic triblock copolymer comprises at least one of polystyrene-polyisoprene-polystyrene or polystyrene-polybutylene-polyethylene-polystyrene.

24. The device of claim 22, wherein the fluorinated rubber comprises poly(vinyl fluoride-tetrafluoropropylene).

25. 21. The device of claim 10 or 20, wherein the template particles comprise at least one of a salt, a sugar, a metal, or a polymer.

26. 26. The device of claim 25, wherein the salt comprises at least one of sodium chloride or sodium bicarbonate.

27. The device of claim 25 , wherein the metal comprises at least one of Mg or Zn.

28. 26. The device of claim 25, wherein the sugar comprises at least one of glucose, sucrose, fructose, maltodextrin, starch, or maltose.

29. 26. The device of claim 25, wherein the polymer comprises polystyrene, polyethylene glycol, polyacrylamide, polyacrylic acid copolymer, polyethyleneimine, or polyvinyl alcohol.

30. The device of claim 20 , wherein the redox active material comprises one of a conducting polymer, a 2-D material, or MXene.

31. The device of claim 30, wherein the conductive polymer comprises poly(3,4-ethylenedioxythiophene) polystyrene sulfonate.

32. The device of claim 30, wherein the 2-D material comprises molybdenum disulfide.

33. The MXene is Ti 2 C 3 , Ti 2 C.V. 2 C, or Ti 4 N 3 31. The device of claim 30, comprising:

34. The device of claim 1 , wherein the plurality of electrodes comprises a conductive polymer, a redox active material, and a target analyte molecule of the device.

35. 35. The device of claim 34, wherein the conductive polymer comprises at least one of polypyrrole, polyethyleneimine, polyaniline, or poly(3,4-ethylenedioxythiophene) polystyrenesulfonate formed by direct dispersion deposition or by applying a constant voltage / current or a range of voltages that are repeatedly scanned over a controlled amount of time.

36. 35. The device of claim 34, wherein the redox-active material comprises a mediator or organic dye that is codeposited onto one or more electrodes during electrodeposition of the conductive polymer.

37. the target analyte molecule comprises at least one of cortisol, insulin, levodopa, or a protein; the plurality of electrodes comprises molecularly imprinted polymer electrodes formed by applying a constant voltage, a repeatedly scanned range of voltages, an aqueous solution, or an organic solution for a controlled amount of time, whereby the at least one of cortisol, insulin, levodopa, or a protein is configured to leak from the plurality of electrodes; The molecularly imprinted polymer electrode comprises a recognition cavity that selectively binds the analyte in sweat.

35. The device of claim 34.

38. Piezoelectric chip; two or more electrodes including an anode electrode and a cathode electrode formed on the piezoelectric chip, the electrodes operable to detect an electrical signal associated with a chemical reaction caused by an analyte contained in the individual's sweat input within a surface area of ​​the anode electrode and the cathode electrode; a current collector including two or more conductive material structures disposed between the piezoelectric chip and the two or more electrodes, electrically coupling at least one of the conductive material structures to the anode electrode and another of the conductive material structures to the cathode electrode; a sweat-permeable layer having a first side and a second side opposite the first side, the first side of the sweat-permeable layer being in contact with the two or more electrodes, the sweat being configured to pass through the sweat-permeable layer and permeate from the second side pressed by the individual's fingertip to the first side to reach an area of ​​the surface of the two or more electrodes; Equipped with the piezoelectric chip undergoes a non-destructive mechanical deformation when the second side of the sweat-permeable layer is pressed by the individual's fingertip, and generates electrical energy from the non-destructive mechanical deformation of the piezoelectric chip. device.

39. 40. The device of claim 38, wherein the two or more electrodes are operable to measure a parameter of the analyte in the sweat based on the detected electrical signal.

40. The device further comprises: a substrate disposed beneath the piezoelectric chip; two or more spacers disposed below the piezoelectric chip and above the substrate, the spacers having a first thickness that facilitates the non-destructive mechanical deformation of the piezoelectric chip; Equipped with 39. The device of claim 38.

41. 40. The device of claim 38, wherein the sweat-permeable layer comprises a hydrogel.

42. 40. The device of claim 38, wherein the two or more electrodes comprise three-dimensional (3D) carbon nanotube (CNT) foam.

43. 43. The device of claim 42, wherein the cathode electrode has particles comprising platinum within pores or cavities of the 3D CNT foam of the cathode electrode.

44. 44. The device of claim 43, wherein the analyte comprises lactate and the anode electrode comprises lactate oxidase (LOx) within the pores or cavities of the 3D CNT foam of the anode electrode.

45. 45. The device of claim 44, wherein the anode electrode further comprises at least one of an enzyme or a mediator.

46. 1. A method for determining a concentration of an analyte present in at least one of an individual's blood, sweat, or interstitial fluid (ISF), comprising: obtaining a sample of sweat deposited on the sweat-permeable layer of the device from the individual's finger with a device according to any one of claims 1 to 45; obtaining a plurality of measured levels of the analyte using signals from the device; obtaining a measurement of a concentration of the analyte in the individual's blood for each of a plurality of measurement levels of the analyte; obtaining linear slope and intercept parameters for the dependence between the obtained measured values ​​of the concentration of the analyte in the individual's blood and the obtained measured values ​​of the analyte level in the individual's sweat; converting the new measured level of the analyte in the individual's sweat into an estimate of the concentration of the analyte in the individual's blood using the linear slope parameter and the intercept parameter; The method according to claim 1,

47. 1. A method for determining a concentration of an analyte present in at least one of an individual's blood, sweat, or interstitial fluid (ISF), comprising: obtaining a sample of sweat deposited on the sweat-permeable layer of the device from the individual's finger with a device according to any one of claims 1 to 45; obtaining a plurality of measured levels of the analyte using signals from the device; obtaining a measurement of the concentration of the analyte in the individual's blood for each of a plurality of measurement levels of the analyte; obtaining an exponent parameter, a multiplier parameter, and an intercept parameter for the dependence between the obtained measured value of the concentration of the analyte in the individual's blood and the obtained measured value of the analyte level in the individual's sweat; converting the new measured level of the analyte in the individual's sweat into an estimate of the concentration of the analyte in the individual's blood using the exponent parameter, the multiplier parameter, and the intercept parameter; The method according to claim 1,

48. 1. A method for determining the concentration of an analyte present in the blood of an individual, comprising: obtaining a sample of sweat deposited on the sweat-permeable layer of the device from the individual's finger with a device according to any one of claims 1 to 45; obtaining a group of multiple measured levels of the analyte in sweat of the individual using signals from the device; obtaining, for each group of measured levels of the analyte in the individual's sweat, a corresponding group of measured concentrations of the analyte in the individual's blood; obtaining, for each group of measured levels of the analyte in the individual's sweat, values ​​of a linear slope parameter and an intercept parameter for the dependence between the measurements in that group and the measurements in a corresponding group of measured levels of the concentration of the analyte in the individual's blood; determining a mean value of the linear slope parameter and a mean value of the intercept parameter for the group of measured levels of the analyte in sweat of the individuals; determining a concentration of the analyte in the individual's blood based on the determined average value of the linear slope parameter and the determined average value of the intercept parameter; The method according to claim 1,

49. 1. A method for generating electrical power using a sweat analyte, comprising: placing the device on a skin surface having sweat glands to collect sweat analytes for biocatalytic reactions at the multiple electrodes and generating a current from the multiple electrodes of the device according to any one of claims 1 to 45, wherein the sweat passes through the sweat-permeable layer of the device and is collected by the device from a finger of skin covered with sweat glands; applying pressure to the device against the skin via finger pressure to generate a current from the multiple electrodes and collecting energy either directly in the highly porous electrodes of the device or via a voltage regulation circuit to a storage unit; The method according to claim 1,

50. 1. A method for determining a concentration of an analyte in a biological fluid of an individual, comprising: obtaining a sample of sweat accumulated on the sweat-permeable layer of the device from the individual's finger with a device according to any one of claims 1 to 45; obtaining a plurality of measured levels of the biofluid analyte in sweat of the individual using a self-generated signal or an open circuit voltage from the device; obtaining a voltage signal, without external application of a constant voltage or current, by discharging through a resistive load between an anode and a cathode of the plurality of electrodes for each of a plurality of measured levels of the biofluid analyte in the sweat of the individual; discharging conditioned or stored power from the biofuel cell of the device in power electronics that acquires signals from the plurality of electrodes for each of a plurality of measured levels of the biofluid analyte in sweat of the individual; The method according to claim 1,

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