Sweat glucose detection device based on non-enzyme wearable wireless patch and application

Abstract

The invention relates to a sweat glucose detection device based on an enzyme-free wearable wireless patch and application, and belongs to the technical field of sensors. The invention provides a sweat glucose detection device based on an enzyme-free wearable wireless patch. The sweat glucose detection device comprises a microfluidic channel, a sensor array and a signal acquisition module. Sweat is obtained by the microfluid channel, and after entering the system, the sweat flows into the mixing cavity through the Tesla valve and reacts with NaOH solid in the cavity to form an alkaline solution, so that the pH value of the sweat is adjusted to meet the working requirements of the sensor. The sensor array is used for preprocessing and detecting sweat signals, and an Au NPs / Ni-BDC NFs modified electrode in the module can monitor glucose and calibrate temperature and pH in real time. According to the wearable wireless patch system formed through the design, the limitation of the prior art is successfully overcome, continuous and enzyme-free monitoring of glucose in sweat is achieved, and a new thought is provided for application of nanometer materials in wearable equipment.

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and more specifically, to a sweat glucose detection device based on an enzyme-free wearable wireless patch and an application thereof. Background Art

[0002] Diabetes, as a widespread chronic metabolic disease, has affected billions of people worldwide. Long-term hyperglycemia not only increases the risk of complications such as retinopathy, nephropathy and cardiovascular disease, but also greatly reduces the quality of life of patients. Therefore, the effective management of diabetes depends on regular and accurate blood glucose monitoring. Traditional portable blood glucose meters provide accurate blood glucose data through fingertip blood sampling and have become an important tool for diabetic patients to self-test at home. However, invasive blood glucose monitoring methods not only bring pain, bleeding and infection risks to patients, but also have obvious defects in safety and comfort, especially for diabetic patients with slow wound healing.

[0003] With the advancement of technology, non-invasive, wearable sensors have gradually attracted attention as an alternative. These sensors can be in contact with the skin in the form of clothing, contact lenses or skin patches to monitor physiological indicators such as blood sugar in real time. In the field of glucose monitoring, sweat, as an easily accessible physiological fluid, contains rich biomarkers (such as electrolytes, metabolites and hormones), and the glucose concentration in sweat is highly correlated with blood sugar levels. Therefore, it is considered to be an ideal non-invasive blood sugar monitoring carrier. However, factors such as temperature fluctuations, pH changes and potential contamination in the sweat environment cause enzyme-based sensors to face large errors, affecting their stability and accuracy.

[0004] To overcome these challenges, researchers have turned to synthetic nanoenzyme materials because of their greater stability and adaptability in complex environments. For example, Farzaneh et al. modified Ni-Co metal organic frameworks (Ni-Co MOFs) on cotton fabrics by a hydrothermal method and successfully prepared flexible electrodes suitable for non-enzymatic glucose monitoring. Tamilarasi et al. prepared NiO2@PG composite materials with a large specific surface area by reflux and high-temperature annealing techniques, achieving stable glucose monitoring for up to 20 days. However, these nanoenzyme materials usually need to be in an alkaline environment to perform best, and the pH value of human sweat is usually acidic, which means that in practical applications, related sensors often need to collect and process sweat samples in vitro, reducing the convenience of real-time monitoring. Therefore, how to maintain the high sensitivity and stability of the sensor while adapting to the acidic environment of the human body remains a difficult problem that needs to be solved urgently. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a sweat glucose detection device based on an enzyme-free wearable wireless patch and its application. The enzyme-free wearable wireless patch device can monitor glucose in sweat in real time and in situ. The device effectively solves the technical problem that the enzyme-free glucose detection device cannot achieve in situ real-time detection when using enzyme-free glucose sensors in wearable devices.

[0006] According to a first aspect of the present invention, a sweat glucose detection device based on an enzyme-free wearable wireless patch is provided, comprising a microfluidic channel, a sensor array and a signal acquisition module; the microfluidic channel is encapsulated by two upper and lower substrates, and the microfluidic channel sequentially comprises an inlet, a one-way Tesla valve, a sodium hydroxide mixing chamber, a serpentine mixing chamber, a detection chamber and a storage chamber; the one-way Tesla valve is used to prevent sample backflow;

[0007] The sensor array is located in the detection chamber, and the sensor array includes a glucose detection sensor, a temperature sensor and a pH sensor that are independently arranged;

[0008] The wiring of the sensor array is connected to the signal acquisition module; the signal acquisition module is used to collect and calculate the signal of the sensor array and perform correction to obtain the glucose concentration of the sample in the microfluidic channel.

[0009] Preferably, the electron mediator of the glucose detection sensor is a two-dimensional nickel-based organic framework modified with gold nanoparticles.

[0010] Preferably, the two-dimensional nickel-based organic framework modified with gold nanoparticles is prepared by the following method: first, an initial metal-organic framework material constructed of coordinated octahedral divalent nickel and terephthalic acid is synthesized by a solvothermal method, and then chloroauric acid is reduced in situ using sodium borohydride to deposit gold nanoparticles on the initial metal-organic framework material to obtain a two-dimensional nickel-based organic framework modified with gold nanoparticles.

[0011] Preferably, the sweat glucose detection device further comprises an absorption pad, which is located in the storage chamber; the absorption pad is used to absorb the sample after detection.

[0012] Preferably, the glucose detection sensor is an electrochemical three-electrode system, including a working electrode, a reference electrode and a counter electrode;

[0013] According to another aspect of the present invention, there is provided an application of the device for preparing a device for detecting glucose concentration in sweat.

[0014] Preferably, the steps include:

[0015] (1) Adding a sample from the inlet into the microfluidic channel, the sample passes through the Tesla valve and enters the sodium hydroxide mixing chamber in one direction, the sodium hydroxide in the sodium oxide mixing chamber is dissolved by the sample, and the sample becomes alkaline, the alkaline sample flows into the detection chamber through the serpentine mixing chamber, and contacts the sensor array, the glucose detection sensor detects the original glucose concentration of the alkaline sample, and the temperature sensor and the pH sensor respectively detect the real-time temperature and pH; then the alkaline sample flows into the storage chamber;

[0016] (2) According to the current value I collected by the glucose sensor, the temperature value T collected by the temperature sensor, and the pH value collected by the pH sensor, the glucose concentration of the sample is calculated by the following formula:

[0017] Wherein, C is the glucose concentration in μM; I is the current value collected by the glucose sensor in μA; T is the temperature value collected by the temperature sensor, and pH is the pH value collected by the pH sensor.

[0018] In general, the above technical solution conceived by the present invention has the following technical advantages compared with the prior art:

[0019] (1) The microfluidic in-situ pretreatment strategy proposed in the present invention realizes in-situ pretreatment of sweat samples on the skin surface, solving the problem that traditional methods require in vitro artificial sample collection and processing.

[0020] (2) The present invention adds a Tesla one-way valve between the sample collection inlet of the microfluidic channel and the NaOH mixing chamber, which effectively prevents the alkaline sample from flowing back to the skin surface and avoids skin burns.

[0021] (3) The present invention utilizes two-dimensional nickel-based organic framework nanoflowers modified with gold nanoparticles as electron mediators for electrochemical detection of glucose, which significantly improves the sensitivity and stability of the glucose sensor.

[0022] (4) The specially designed signal acquisition module is used to simultaneously detect temperature and pH to perform real-time corrections on the glucose sensor, which significantly improves the accuracy of glucose detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1The present invention is a schematic structural diagram of the sweat glucose detection device of the enzyme-free wearable wireless patch; wherein, 1-lower substrate, 12-lower substrate sample inlet; 2-microfluidic channel, 21-microsome channel sample inlet, 22-one-way Tesla valve, 23-sodium hydroxide mixing chamber, 24-serpentine mixing chamber, 25-detection chamber, 26-storage chamber, 27-absorption pad; 3-sensor array, 31-FPC cable; 4-upper substrate; 5-signal acquisition module.

[0024] Figure 2 These are morphological characterization images of the Au-NPs / Ni-BDCNFs of the two-dimensional nickel-based organic framework nanoflowers modified with gold nanoparticles in Example 1: a and b are scanning electron microscope images of the Au-NPs / Ni-BDC NFs; c is a transmission electron microscope image of the Au-NPs / Ni-BDC NFs; and d is a high-resolution transition electron microscope image of the Au-NPs / Ni-BDC NFs.

[0025] Figure 3 This is a diagram of the sweat glucose detection device of the enzyme-free wearable wireless patch in Example 2: wherein a is a schematic diagram of the flexible wearable sensor array of the device; b and c are actual pictures of the assembled disposable sensor patches.

[0026] Figure 4 The performance characterization diagram of the microfluidic module with unidirectional flow characteristics of Example 3: wherein a is a numerical simulation of the entire process of sweat flowing from the inlet to the outlet of the microfluidic model; b is an actual test of the microfluidic module collecting artificial sweat; c is the distribution of liquid flow rate (i) and pressure (ii) at the inlet and outlet of the Tesla one-way valve when the microfluidic module collects sweat in the forward direction; d is the distribution of liquid flow rate (i) and pressure (ii) at the inlet and outlet of the Tesla one-way valve when sweat flows back in the microfluidic module (reverse flow).

[0027] Figure 5 The in vitro electrochemical performance characterization graphs of the sensor array of Example 4: wherein a is the current it response of the Ni-BDC-AuNPs modified glucose sensor measured in the glucose concentration range of 0-700 μM (50 μM per step); b is the current response related to the glucose concentration in the range of 0-700 μM; c is the instantaneous current-time response curve of the glucose sensor in the concentration range of 0-10 μM (increments of 10 μM); d is the anti-interference test of the glucose sensor, in which the added glucose concentration is 50 μM and the concentration of the remaining interfering substances is 1 mM; e and f are the response curves of the pH and temperature sensors, respectively.

[0028] Figure 6This is the in vivo detection evaluation of the enzyme-free patch detection device of Example 5: a is a photo of a healthy person wearing a sensor patch on different parts of the body. The scale bar is 4 cm. b is a customized mobile application for real-time data collection and glucose tracking. c is an electronic block diagram of the wearable system. AFE, analog front end; TIA, transimpedance amplifier; CA, control amplifier; MCU, microcontroller unit; BLE, low-power Bluetooth. d is a schematic diagram of the dietary intake plan and the response of glucose, pH and temperature sensors in the wearable system during human testing. e is the sweat and blood glucose trend of volunteer 1 and f is the sweat and blood glucose trend of volunteer 2 after ingesting 100 grams of carbohydrates, and g is the correlation between sweat and blood glucose after ingesting carbohydrates. h is the change in sweat and blood glucose levels of volunteers throughout the day. i is a Clarke error grid analysis comparing predicted blood glucose (based on sweat) and blood glucose meter data.

[0029] Figure 7 Glucose-current response curve of glucose sensor at different pH and fitted linear relationship. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0031] Figure 1 The figure is a schematic diagram of the structure of the sweat glucose detection device of the enzyme-free wearable wireless patch of the present invention.

[0032] The invention discloses a sweat glucose detection device based on an enzyme-free wearable wireless patch, which consists of a microfluidic channel, a sensor array and a signal acquisition module.

[0033] The microfluidic channel of the present invention sequentially comprises an inlet, a one-way Tesla valve, a sodium hydroxide mixing chamber, a detection chamber and a storage chamber; the one-way Tesla valve is used to prevent the sample from flowing back;

[0034] The microfluidic channel of the present invention is used to obtain sweat. After the sweat enters the system, it first flows into the mixing chamber through the Tesla valve and reacts with the NaOH solid in the chamber to form an alkaline solution, thereby adjusting the pH value of the sweat to meet the working requirements of the sensor.

[0035] The microfluidic channel of the present invention is designed with several key parts:

[0036] (1) a reagent tank for mixing sweat with solid NaOH to form an alkaline solution, wherein the reagent tank contains solid NaOH, and when sweat flows into the reagent tank, the NaOH dissolves to form an alkaline mixed solution;

[0037] (2) Tesla valve with one-way flow characteristics, which can effectively prevent alkaline solution from flowing back and causing skin burns;

[0038] (3) a serpentine mixer that promotes full fusion of sweat and NaOH and a detection cavity that ensures full contact between glucose and the flexible electrode array;

[0039] (4) A storage chamber for storing sweat, into which fresh sweat continuously flows to refresh the microfluidics.

[0040] The sensor array of the present invention is used for preprocessing and detecting sweat signals. In the glucose detection chamber of the device, the two-dimensional nickel-based organic framework modified with gold nanoparticles (Au NPs / Ni-BDC NFs) is used as an electron mediator to achieve high-sensitivity and long-lasting stable glucose monitoring. At the same time, the system integrates temperature and pH sensors for real-time calibration to ensure measurement accuracy.

[0041] The signal acquisition module of the present invention is used to receive and process the detection result signal transmitted by the wireless signal transmission module through a specially designed mobile phone application, and can realize real-time analysis of sweat glucose concentration during exercise.

[0042] The device system of the present invention uses a constant potential method (CA) to detect glucose. In the process, glucose is oxidized into gluconic acid accompanied by electron transfer, thereby achieving quantitative monitoring of glucose.

[0043] The device of the present invention performs in-situ pretreatment of sweat through a microfluidic channel and works in conjunction with a sensor array. The device effectively solves the application bottleneck of enzyme-free glucose sensors in wearable devices and demonstrates its wide application potential in the field of future health monitoring, especially in real-time monitoring during exercise and daily life.

[0044] The wearable wireless patch device of the present invention collects sweat through a microfluidic channel and uses sweat to dissolve solid NaOH in the flow channel to form the required alkaline environment. Subsequently, the sweat enters the detection cavity, and the two-dimensional nickel-based organic framework nanoflower (Au NPs / Ni-BDC NFs) modified by gold nanoparticles is used as the sensing layer to achieve high-sensitivity and stable glucose monitoring. The system integrates temperature and pH sensors for real-time calibration to ensure measurement accuracy, and uses a Tesla valve to prevent alkaline solution from flowing back to the skin. The signal acquisition, processing and wireless communication modules can realize real-time analysis of sweat glucose concentration during exercise through a specially designed mobile phone application. By pre-treating sweat in situ through a microfluidic channel and working in conjunction with a sensor array, this study effectively solves the application bottleneck of enzyme-free glucose sensors in wearable devices, demonstrating its potential for wide application in the future field of health monitoring, especially in real-time monitoring during exercise and daily life.

[0045] The signal acquisition module of the present invention is used to receive and process the detection result signal transmitted by the wireless signal transmission module through a specially designed mobile phone application, and can realize real-time analysis of sweat glucose concentration during exercise.

[0046] The present invention also provides a method for preparing the wireless patch device, which is specifically:

[0047] Firstly, a uniformly sized initial metal-organic framework material, namely Ni-BDC nanoflowers (Ni-BDC NFs), was synthesized by solvothermal method using N,N-dimethylformamide, ethanol and water as a mixed solvent.

[0048] First, Ni(NO3)2·6H2O (5.7 mmol) and terephthalic acid H2BDC (8.7 mmol) were dissolved in 40 mL of DMF solution and ultrasonicated for 5 min, followed by the addition of NaOH to maintain the pH value of the solution at 8. The mixture solution was then transferred to a 50 mL Teflon autoclave, heated at 150 ° C for 12 h, and the reaction product was collected by filtration, washed 5 times with a mixture of distilled water / anhydrous ethanol (1 / 1), and then dried at room temperature.

[0049] Then, 500 μL of HAuCl4·3H2O (0.1 M) was added to 500 mL of Ni-BDC NFs dispersion (0.1 mg / mL) and stirred vigorously for 30 minutes. The mixed solution was then transferred to a capped bottle and heated and stirred at 65°C for 5 hours. The precipitate was then collected by centrifugation at 8000 rpm for 5 minutes, washed three times with ethanol and water, and then freeze-dried for 12 hours. The obtained Au-NPs / Ni-BDC NFs were immersed in anhydrous ethanol for 3 days and then activated by heating at 120°C for 10 hours.

[0050] Finally, the designed electrode pattern was made into a flexible electrode array through the FPCB process, and the electrode was further functionalized. Specifically, 5 mg Au NPs / Ni-BDC NFs powder and 10 uL Nafion solution were added to 0.5 ml DMF solution, and ultrasonicated for 20 minutes to prepare the glucose sensor modification solution. 2.5 uL of the modification solution was dropped on the working electrode and then dried in a 60 ° C oven for 10 minutes, and repeated twice to prepare the glucose sensor. In a mixed aqueous solution containing 0.1 M aniline and 1 M HCL solution, cyclic voltammetry was used to continuously scan 25 times between -0.2 and 1.0 V (relative to Ag / AgCl electrode) at a scan rate of 100 mV / s to electrochemically polymerize polyaniline (PANI) on the Cu / Au surface deposited on the PI layer to prepare a pH sensor. The Ag / AgCl reference electrode was prepared by directly dip-coating the Au electrode with Ag / AgCl slurry, followed by the addition of 5ul of a solid electrolyte containing NaCl, which consisted of saturated NaCl in 6% polyvinyl butyrate (PVB) dissolved in methanol. Compared with a commercial Ag / AgCl reference electrode, the voltage drift was only , verifying the stability of the reference electrode. The temperature sensor was made of nickel manganese oxide by powder molding, sintering, and then encapsulated in a sensor array.

[0051] The following are specific embodiments

[0052] Example 1: Synthesis of Au-NPs / Ni-BDC NFs

[0053] First, a uniformly sized initial metal organic framework material, namely Ni-BDC nanoflowers (Ni-BDC NFs), was synthesized by solvothermal method using N,N-dimethylformamide, ethanol and water as mixed solvents. The MOF is constructed by coordinated octahedral divalent nickel and terephthalic acid (H2BDC), where H2BDC directly coordinates with the nickel hydroxide layer to form a three-dimensional framework. Secondly, Au NPs were deposited on Ni-MOF nanoflowers by in situ reduction of chloroauric acid (HAuCl4) using sodium borohydride. The surface oxygen atoms with unpaired electrons on Ni-BDC nanoflowers provide uniformly distributed anchoring sites for the deposition of Au NPs.

[0054] like Figure 2 As shown in Figure 2, scanning electron microscopy (SEM) images show that Ni-BDC NFs have a typical nanoflower structure, and the petals of the nanoflowers have a typical sheet-like structure, which provides a huge specific surface area for the subsequent loading of Au NPs ( Figure 2 The nanostructure of Au-NPs / Ni-BDC NFs was studied by transmission electron microscopy (TEM). Figure 2Figure c shows that high-density Au NPs can be well dispersed on Ni-BDC NFs under optimized conditions. High-resolution TEM (HRTEM) images show that Au-NPs present a uniform spherical shape with a lattice distance of 2.355 Å, corresponding to the crystal plane Au(111)( Figure 2 d) in.

[0055] Au-NPs / Ni-BDC NFs were drop-coated on the working electrode of the sensor to prepare a glucose sensor. When the sample came into contact with the electrode, the glucose contained in the sample was catalyzed by Au-NPs / Ni-BDC NFs at a potential of 0.55 V to undergo a redox reaction. The reaction process is as follows:

[0056]

[0057] In this process, glucose is oxidized to gluconic acid, and trivalent nickel is reduced to divalent nickel, accompanied by electron transfer, and the amount of electron transfer is positively correlated with the concentration of glucose. By collecting the amount of electron transfer, that is, the current size, quantitative detection of glucose can be achieved.

[0058] The gold nanoparticle-modified two-dimensional nickel-based organic framework (Au NPs / Ni-BDC NFs) of the present invention is used as an electron mediator to achieve high-sensitivity and long-lasting stable glucose monitoring. At the same time, the system integrates temperature and pH sensors for real-time calibration to ensure measurement accuracy.

[0059] Example 2 Preparation of sensor array

[0060] The designed electrode pattern was made into a flexible electrode array through the FPCB process, and the electrode was further functionalized. Specifically, 5mg Au NPs / Ni-BDC NFs powder and 10uL Nafion solution were added to 0.5ml DMF solution, and ultrasonicated for 20 minutes to prepare the glucose sensor modification solution. 2.5uL of the modification solution was dropped on the working electrode and then dried in a 60°C oven for 10 minutes, and repeated twice to prepare the glucose sensor. In a mixed aqueous solution containing 0.1M aniline and 1M HCL solution, cyclic voltammetry was used to scan continuously between -0.2 and 1.0V (relative to Ag / AgCl electrode) for 25 times at a scan rate of 100mV / s to electrochemically polymerize polyaniline (PANI) on the Cu / Au surface deposited on the PI layer to prepare a pH sensor. The Ag / AgCl reference electrode was prepared by directly dip-coating the Au electrode with Ag / AgCl slurry, followed by the addition of 5ul of a solid electrolyte containing NaCl, which consisted of saturated NaCl in 6% polyvinyl butyrate (PVB) dissolved in methanol. Compared with a commercial Ag / AgCl reference electrode, the voltage drift was only , verifying the stability of the reference electrode. The temperature sensor was made of nickel manganese oxide by powder molding, sintering, and then encapsulated in a sensor array.

[0061] The results are as follows Figure 3 As shown in the figure, Cu and Au are deposited on the patterned flexible PI film to prepare an electrode array. Functional materials are further modified on the Au electrode to prepare glucose sensors, pH sensors, temperature sensors, reference electrodes and counter electrodes ( Figure 3 a); the actual picture of the wearable device is as follows Figure 3 As shown in b, it mainly includes a microfluidic module for sweat collection and pretreatment, a flexible sensor array integrating temperature, pH and glucose, and an electronic system for signal acquisition and wireless transmission. The patch device is only the size of a coin ( Figure 3 c) in the figure shows the portability of the device in terms of wearability.

[0062] Example 3 Preparation and numerical calculation of microfluidic channels

[0063] The microfluidic channel is prepared based on the standard micro-electromechanical system (MEMS) process. SU8 photoresist is spin-coated on a 4-inch silicon wafer, and the mold of the microfluidic channel is manufactured through photolithography and development processes. Then PDMS (10:1 curing agent, DowCorning 184, 0.5% Triton X-100 is added to improve hydrophilicity) is poured into the mold and degassed in a vacuum environment to remove bubbles. Finally, it is cured in a drying oven at 80°C for 2 hours to obtain the microfluidic channel. The sensor array and solid NaOH are placed in the corresponding positions of the microfluidic channel, and then the PDMS cover layer and the microfluidic channel are plasma treated together to improve its hydrophilicity. Finally, they are assembled and bonded together to form a microfluidic sensor patch.

[0064] A 3D model of a microfluidic channel with the same actual size was created in UG12.0 and imported into COMSOL Multiphysics. The mass transfer process in the microfluidic channel was simulated by numerically solving the Stokes equations for incompressible flow:

[0065]

[0066] Where v, p, η, D and C represent flow rate, pressure, solvent viscosity, solute diffusion rate and solute concentration, respectively.

[0067] The experimental results are as follows Figure 4 As shown in the figure, in order to meet the needs of wearable devices, the microfluidic patch is specially designed to have excellent time resolution, that is, the total time required from the flow of sweat into the microfluidic channel to the end of the detection. The performance of the microfluidic patch was analyzed through numerical simulation. The results showed that the flow time of sweat from the inlet to the outlet was about 40 seconds ( Figure 4 In vitro, the microfluidic patch demonstrated similar high temporal resolution sampling performance (see Figure 4 b). Regarding the unidirectional conduction performance of the Tesla valve, two sweat flow directions, forward and reverse, were set for testing. The results show that when sweat flows in the forward direction, the flow rates at the inlet and outlet of the Tesla valve are similar, and the pressure is only reduced by 6%, indicating that the valve has little effect on the forward flow of sweat ( Figure 4 c). However, under the condition of reverse flow of sweat (backflow), the outlet flow rate of the valve is significantly lower than that of the inlet, and the pressure at the outlet is reduced by 75%. This significant difference indicates that the Tesla valve effectively blocks the backflow of sweat (see Figure 4 d) in.

[0068] Example 4 In vitro performance characterization of sensor array

[0069] The performance of the sensor was characterized using an electrochemical workstation (Chenhua 660E). The pH of the solution was adjusted by NaOH to analyze the effect of different pH on the performance of the glucose sensor. The response of the sensor to different temperatures was carried out by placing the reaction cell in a temperature-adjustable constant-temperature water bath. The pH sensor was characterized by recording the open circuit potential relative to Ag / AgCl. The characterization of the temperature sensor was carried out in an adjustable constant-temperature water bath. The sensor response was recorded using a self-built STM32-based signal acquisition device and compared with the thermometer reading. Unless otherwise stated, all in vitro electrochemical experiments of glucose sensors were performed in 0.1M NaOH solution (pH=13, temperature 25°C).

[0070] like Figure 5 As shown in the figure, based on the sensitivity at different potentials, the optimized working potential is 0.55 V. At this working potential, continuous addition of glucose exhibits a rapid amperometric response ( Figure 5 a). In the concentration range of 0-700 μM, it shows 2522 uA·uM -1 cm -2 The sensitivity, linear correlation coefficient (R 2 ) is 0.997, which covers the range of glucose in human sweat ( Figure 5 The sensor also showed a current response to a low concentration range (0-10 μM) ( Figure 5 c), the detection limit (S / N=3) was calculated to be 6.29 μM, indicating that the Au NPs / Ni-BDC NFs modified glucose electrode has highly sensitive detection performance. Further, the anti-interference, long-term stability and reproducibility of the sensor were tested. The sensor was less susceptible to interference and showed good selectivity for glucose ( Figure 5 d) in.

[0071] Since the Au-NPs / Ni-BDC NFs modified sensor requires the participation of hydroxide ions in the process of catalyzing glucose oxidation. Therefore, it is necessary to ensure the concentration of hydroxide ions to avoid affecting the reaction process. Theoretically, through the designed microfluidic channel and the optimized solid NaOH mass, the pH of the pretreated sweat is 13 (0.1M NaOH). In practical applications, the actual pH value and the theoretical value may deviate. Therefore, a pH sensor is added to calibrate the glucose sensor by real-time monitoring of the pH in the working area. Figure 5 Figure e shows the open circuit potential response of the pH sensor based on PANI modification. Similarly, temperature is also used as a calibration parameter. The performance of the NTC temperature sensor based on nickel manganese oxide is shown in Figure 5. Figure 5 As shown in f.

[0072] Considering that pH and temperature will affect the enzyme-free glucose sensor based on Au-NPs / Ni-BDC NFs modification, the current response curve of the sensor at different concentrations of glucose at pH 13 and 25°C is used as a benchmark. The influence of temperature and pH is taken into account in the slope and intercept of the current response curve, thereby achieving calibration of the sensor. The calibration method is described in detail below:

[0073] like Figure 5 As shown in b, the linear equation of the glucose sensor is (pH = 13, T = 25 ° C)

[0074] I=0.317C+12.8

[0075] Where I is the current (uA) and C is the glucose concentration (uM).

[0076] Similarly, the linear equation for a pH sensor is Figure 5 e in

[0077] OCP=-0.05pH+0.58

[0078] Where OCP is the open circuit voltage (V), pH is the pH of the NaOH solution

[0079] same, Figure 5 f in the figure shows the linear equation for the temperature sensor

[0080] R=-0.187T+12.397

[0081] Where R is the output resistance of the temperature sensor (KΩ), T is the temperature (℃)

[0082] Here it is assumed that the effects of pH and temperature on the glucose sensor are independent of each other. The effects of temperature and pH on the glucose sensor can be taken into account in the slope and intercept of the glucose-current response curve by calculating the change factors (Pslope and Pintercept for pH and Tslope and Tintercept for temperature) for each condition relative to the reference condition (pH 13 and temperature 25°C). Where:

[0083]

[0084] according to Figure 7 Using equations S1 and S2, the correction coefficients for the slope and intercept of the glucose sensor at different pH values ​​were calculated.

[0085] pH Slope Pslope Intercept Pintercept 12 0.015 0.047 2.28 0.178 12.5 0.089 0.28 5.6 0.438 13 0.317 1 12.8 1

[0086] The calibration coefficients at different pH values ​​can be fitted as follows:

[0087] P slope(c) = 0.95 pH - 11.47, R 2 =0.92(S5)

[0088] P intercept(c) =0.82pH-9.74,R 2 =0.96 (S6)

[0089] Similarly, the slope and calibration coefficient of the glucose sensor at different temperatures are calculated according to formulas S3 and S4.

[0090]

[0091] The correction coefficients at different temperatures can be fitted as:

[0092] T slope(c) =0.035T+0.08,R 2 =0.93 (S7)

[0093] T intercept(c) =0.067T-0.871,R 2 =0.9 (S8)

[0094] Assuming that temperature and pH affect the glucose sensor in a cumulative manner, the calibration slope K of the sensor can be recalculated as corrected and intercept M corrected .

[0095] K corrected =P slope(c) ×T slope(c) (S9)

[0096] M corrected =P intercept ×T intercept (S10)

[0097]

[0098] Finally, S5 and S7 are substituted into S9, S6 and S8 are substituted into S10, and then S9 and S10 are substituted into S11. The formula for calculating glucose concentration is as follows:

[0099]

[0100] Right now:

[0101] The calculation formula of glucose concentration C (in μM) is as follows:

[0102]

[0103] Wherein, I is the current value collected by the glucose sensor, in μA; T is the temperature value collected by the temperature sensor; and pH is the pH value collected by the pH sensor.

[0104] Example 5 Human Subject Validation of the Integrated System

[0105] Before the wearable sensor patch was attached to the body, the participant's forehead was cleaned with an alcohol swab and gauze. All participants were asked to ride a stationary bicycle at a power of 100W, and the sweat produced was collected by the microfluidic channel and transported to the detection chamber. When the electrode detects sweat, the program starts running and collects glucose, pH and temperature signals every 30 seconds. The sensor signal is transmitted wirelessly to the user interface, which displays the pH, temperature and corrected glucose values. The human experiments were carried out strictly in accordance with the protocol approved by the Ethics Committee of Wuhan University. The experimental participants were all publicly recruited volunteers who fully understood the experimental conditions and signed informed consent.

[0106] To verify the feasibility of the proposed scheme for in situ monitoring of sweat glucose in practical applications, we designed and assembled a sensor array, microfluidic channels, and a wireless signal acquisition system to form a vertically stacked structure that can be worn by volunteers on different parts of the body ( Figure 6 We customized and developed a mobile phone APP, which converts the raw sensor signal into actual temperature, pH value and glucose concentration through a built-in program ( Figure 6 b) in the above. Figure 6 Figure c shows the electronic circuit block diagram and schematic of the system.

[0107] To evaluate the feasibility of the patch system in daily life, we conducted a five-hour follow-up test on a healthy individual to monitor the changes in sweat glucose during activities such as office work and eating ( Figure 6 d in the figure). The subjects took the first test after sitting still for 2 hours, which was considered as the fasting state. After eating two hamburgers and 500ml of sugary cola, they sat still for 20 minutes for basal absorption and took the second test, which was considered as the postprandial state. After that, the subjects continued to sit still for 2 hours and took the third test, which was considered as the 2-hour postprandial state. All tests were performed on a stationary bicycle. The test results showed that the sweat glucose concentration of the subjects increased significantly after eating and returned to the baseline level 2 hours after the meal, which was consistent with the metabolic characteristics of healthy individuals. At the same time, the pH value showed a downward trend, mainly due to the continuous influx of fresh sweat leading to NaOH consumption. The temperature sensor data fluctuated slightly, which may be affected by ambient temperature, exercise and eating. However, since the system integrates pH and temperature correction algorithms, these factors will not affect the accuracy of glucose detection. The above results show that the patch system can effectively monitor sweat glucose levels under different living conditions, demonstrating its potential for application in daily activities.

[0108] Subsequently, we conducted more human experiments to further verify the potential of the patch system for personalized medical applications. We tested two healthy volunteers and compared the results with those of a commercial fingertip blood glucose meter (BGM) ( Figure 6 e,f in the figure). During the test, we recorded sweat and blood glucose concentrations every 30 minutes. The results showed that the sweat glucose concentrations of both volunteers rose and peaked 30-60 minutes after the meal, and then began to decline. Specifically, the sweat glucose concentration of subject 1 rose rapidly to the maximum value 30 minutes after the meal and returned to the baseline level within 3 hours; while the rising and falling trends of subject 2 were slower, and the concentration was still high after 3 hours. This difference suggests that the insulin level of subject 1 can effectively regulate blood sugar, while subject 2 may have insufficient insulin secretion or weakened insulin action, which may be an early sign of insulin resistance. The area under the curve (AUC) of glucose, as a sensitive predictor of diabetes and impaired glucose tolerance, has been shown to be more accurate than HbA1c and fasting blood glucose levels (Sakaguchi et al., 2016). In this study, the sweat glucose AUCs of subject 1 and subject 2 were 46.25 μM·h and 65.07 μM·h, respectively, indicating that subject 2 had a higher risk of diabetes. Before each test, we first measured fingertip blood glucose and then collected sweat through 100W cycling, which lasted about 10 minutes. According to literature reports, there is a lag of about 10 minutes between sweat glucose and blood glucose, so this test method effectively avoids the lag effect. Figure 6 The graph in Figure 4 shows that the changing trends of sweat and blood glucose concentrations are consistent, and the correlation coefficient between the two is as high as 0.97.

[0109] Furthermore, we used the patch system to evaluate the blood glucose fluctuations of volunteers throughout the day. After the subjects consumed 50g (two slices of bread), 120g (a large bowl of rice), and 100g (a bowl of rice) of carbohydrates for three meals, both sweat and blood glucose concentrations showed consistent postprandial fluctuations ( Figure 6 h in ). To further verify the accuracy of the system, we amplified all sweat glucose data by 125 times as predicted blood glucose data and performed Clarke grid analysis with BGM data ( Figure 6The results show that all data points fall into the clinically acceptable error zone A (indicating no significant impact on clinical decision-making), with an error rate of 85%; only a few data points are in the error zone B (indicating little impact on clinical decision-making), with an error rate of 15%. These data show that the designed wearable enzyme-free sweat glucose monitoring system has great application potential in diabetes management. Unlike traditional BGM that provides discrete information and may cause discomfort, this patch system can achieve continuous, accurate, and real-time glucose monitoring, showing its wide application prospects in daily diabetes monitoring.

[0110] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A sweat glucose detection device based on an enzyme-free wearable wireless patch, characterized in that: It includes a microfluidic channel, a sensor array and a signal acquisition module; the microfluidic channel is encapsulated by two upper and lower substrates, and the microfluidic channel sequentially includes an inlet, a one-way Tesla valve, a sodium hydroxide mixing chamber, a serpentine mixing chamber, a detection chamber and a storage chamber; the one-way Tesla valve is used to prevent the sample from flowing back; The sensor array is located in the detection chamber, and the sensor array includes a glucose detection sensor, a temperature sensor and a pH sensor that are independently arranged; The wiring of the sensor array is connected to the signal acquisition module; the signal acquisition module is used to collect and calculate the signal of the sensor array and perform correction to obtain the glucose concentration of the sample in the microfluidic channel.

2. The sweat glucose detection device based on the enzyme-free wearable wireless patch according to claim 1, characterized in that: The electron mediator of the glucose detection sensor is a two-dimensional nickel-based organic framework modified by gold nanoparticles.

3. The sweat glucose detection device based on the enzyme-free wearable wireless patch according to claim 1, characterized in that: The gold nanoparticle-modified two-dimensional nickel-based organic framework is prepared by the following method: first, an initial metal organic framework material composed of coordinated octahedral divalent nickel and terephthalic acid is synthesized by a solvothermal method, and then chloroauric acid is reduced in situ by sodium borohydride to deposit gold nanoparticles on the initial metal organic framework material to obtain a gold nanoparticle-modified two-dimensional nickel-based organic framework.

4. The sweat glucose detection device based on the enzyme-free wearable wireless patch according to claim 1, characterized in that: The sweat glucose detection device further comprises an absorption pad, which is located in the storage chamber; the absorption pad is used to absorb the sample after detection.

5. The sweat glucose detection device based on the enzyme-free wearable wireless patch according to claim 1, characterized in that: The glucose detection sensor is an electrochemical three-electrode system, including a working electrode, a reference electrode and a counter electrode.

6. Use of the device according to any one of claims 1 to 5, characterized in that: A sweat glucose concentration detection device for preparing an enzyme-free wearable wireless patch.

7. The use according to claim 6, characterized in that: The following steps are involved: (1) adding a sample from the inlet to the microfluidic channel, the sample passes through the Tesla valve to enter the sodium hydroxide mixing chamber in one direction, the sodium hydroxide in the sodium oxide mixing chamber is dissolved by the sample, making the sample alkaline, the alkaline sample flows into the detection chamber through the serpentine mixing chamber, contacts the sensor array, the glucose detection sensor detects the original glucose concentration of the alkaline sample, and the temperature sensor and the pH sensor respectively detect the real-time temperature and pH; The alkaline sample then flows into the storage chamber; (2) According to the current value I collected by the glucose sensor, the temperature value T collected by the temperature sensor, and the pH value collected by the pH sensor, the glucose concentration of the sample is calculated by the following formula: Wherein, C is the glucose concentration in μM; I is the current value collected by the glucose sensor in μA; T is the temperature value collected by the temperature sensor, and pH is the pH value collected by the pH sensor.

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