Conductive microwire-based biochemical sensors for real-time neurological monitoring

Abstract

Disclosed herein are microelectrodes and related fabrication processes that address various shortcomings of current microelectrode designs. The disclosed microelectrodes have a smaller form factor than current microelectrode devices and can be fabricated of flexible materials, thereby improving ease of insertion and reducing the risk of tissue damage. In addition, the sensing interface of the disclosed microelectrodes can be configured for detecting designated biomarkers (e.g., neurotransmitters, ions, hormones, etc.).

Description

CONDUCTIVE MICROWIRE-BASED BIOCHEMICAL SENSORS FOR REAL-TIME NEUROLOGICAL MONITORINGSTATEMENT REGARDING FEDERALLY SPONSORED RESEARCHOR DEVELOPMENT

[0001] This invention was made with government support under grant no. ECCS2223387 awarded by the National Science Foundation. The government has certain rights in the invention.CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to, and the benefit of, U.S. Provisional Application No. 63 / 535,687, filed on August 31, 2023, titled “CONDUCTIVE MICROWIRE-BASED BIOCHEMICAL SENSORS FOR REAL-TIME NEUROLOGICAL MONITORING,” which is incorporated by reference herein in its entirety.BACKGROUND

[0003] Microelectrodes are devices that contain multiple conductive electrodes that transmit an interfacial response (e.g., electrical or electrochemical) to a recording interface.Microelectrodes are utilized in a variety of fields, including neural science and medical prosthetics, for monitoring localized physiological information. Current fabrication methods for microelectrodes are subject to high production costs and low yield rates, resulting in suboptimal cost efficiency. In addition, the relatively large size and rigid structure of current microelectrodes impose significant challenges for insertion, e.g., in a patient, often resulting in inadvertent tissue damage during application.

[0004] The activities of the central nervous system (CNS) involve an interplay of biochemical and electrophysiological processes that operate both spatially and temporally. Consequently, neurotransmitter monitoring plays a critical role in our understanding of the CNS, influencing everything from basic muscle contractions to complex cognitive functions like mood regulationfl -3], However, the field faces challenges due to the limitations inherent in current monitoring methods, which are inadequate for capturing the spatiotemporally resolved dynamics of neuronal chemicals within living organisms. Conventional microdialysis probes, while useful for sampling extracellular fluid, often fail to capture rapid neurotransmitter fluctuations,typically using 10-minute collection periods that are inadequate for tracking fast-changing neurochemical events [4], Furthermore, the considerable physical size of these probes, commonly between 200-500 pm [5], not only limits the spatial resolution but also results in extensive damage to brain tissue. This increased invasiveness can confound experimental results and is particularly problematic in smaller brain regions. Fluorescent probes offer insights into the spatial -temporal distribution of biomarkers. However, a limitation is that they require either prelabeled molecules through genetic encoding [6, 7] or the precise introduction of exogenously labeled fluorescent molecules to specific locations of interest [8, 9],

[0005] To address these issues, electrochemical sensors offer versatile solutions for in vivo, continuous monitoring of neuronal chemicals with spatiotemporal precision [10-15], Nevertheless, challenges are with designing unique form factors with geometries and mechanics that align with those of targeted bio tissues for minimally invasive implantation and potential long-term use. While silicon-based sensors [13-16] and carbon fiber microelectrodefl 7- 19] offer finer spatiotemporal resolution, their rigidity can lead to high invasiveness, risking damage and subsequent inflammatory responses in the delicate neural tissue as well as sensor performance degradation over time

[0020] , Recent advances have seen a notable shift towards the use of microfabricated, miniaturized flexible electrodes[21-29]. These innovations offer less invasive methods with potentially higher spatial resolution. A key advantage of these electrodes is their ability to conform to the soft and contoured surfaces of biological tissues for more precise measurements and to reduce tissue damage during implantation. Notable among these are metalized microelectrodes [22-26] and field-effect transistor (FET) sensors [27-29] on polymer substrates, which have demonstrated a reduction in overall cross-sectional area. Despite the success, the fabrication of these devices often involves complex, resource-intensive techniques such as photolithography and thin-film deposition, typically requiring cleanroom environments. The intricate processes necessary for miniaturization and precision, while crucial for the functionality of the sensors, present considerable challenges in terms of yield rate, design adaptability, and scalability, potentially limiting their practical application. This manufacturing complexity can be a barrier to their widespread adoption, particularly in clinical settings where reliability, stability, and cost-effectiveness are highly desirable.SUMMARY

[0006] Disclosed herein are microelectrodes and related fabrication processes that address various shortcomings of current microelectrode designs. The disclosed microelectrodes have asmaller form factor than current microelectrode devices and can be fabricated of flexible materials, thereby improving ease of insertion and reducing the risk of tissue damage. In addition, the sensing interface of the disclosed microelectrodes can be configured for detecting designated biomarkers (e.g., neurotransmitters, ions, hormones, etc.).

[0007] In some aspects, a biochemical sensor is disclosed. The biochemical sensor includes a reference electrode including: (i) a first conductive microwire coated in an insulating material, wherein a distal portion of the first conductive microwire is exposed, (ii) a porous conductor layer formed on the exposed distal portion of the first conductive microwire, and (iii) a catalyst layer formed on the porous conductor layer; and a working electrode including: (i) a second conductive microwire coated in the insulating material, wherein a distal portion of the second conductive microwire is exposed, (ii) a porous conductor layer formed on the exposed distal portion of the second conductive microwire, (iii) a mediator layer formed on the porous conductor layer, and (iv) a catalyst layer formed on the mediator layer.

[0008] In some aspects, the porous conductor layer of each of the reference and working electrodes is formed by electrodeposition.

[0009] In some aspects, the catalyst layer of each of the reference and working electrodes is a biorecognition material.

[0010] In some aspects, the biorecognition material includes a solid electrolyte. In some aspects, the biorecognition material includes an in silico maturation (ISM) or aptamers.

[0011] In some aspects, the first and second conductive microwires include gold or platinum.

[0012] In some aspects, the biochemical sensor further includes an electrical interface to facilitate an electrical connection to a remote device, wherein the reference and working electrodes are electrically coupled the electrical interface.

[0013] In some aspects, the first and second conductive microwires are 25 pm in diameter.

[0014] In some aspects, the insulating material is one of a polymer or a ceramic. In some aspects, the insulating material is isonel.

[0015] In some aspects, a method of fabricating a biochemical sensor is disclosed. The method includes providing a first conductive microwire and a second conductive microwire, wherein each of the first and second conductive microwires are coated in an insulating material; removing the insulating material form a distal portion of each of the first and second conductive microwires to form an exposed distal portion of each of the first and second conductive microwires; forming a porous conductor on the exposed distal portion of each of the first andsecond conductive microwires; and forming a catalyst layer of a biorecognition material on the porous conductor of each of the first and second conductive microwires.

[0016] In some aspects, the porous conductor is formed by electrodeposition.

[0017] In some aspects, the catalyst layer is formed by dip-coating.

[0018] In some aspects, the biorecognition material is a solid electrolyte. In some aspects, the biorecognition material an in silico maturation (ISM) or aptamers.

[0019] In some aspects, the first and second conductive microwires are formed of gold or platinum.

[0020] In some aspects, the method further includes electrically connecting the first and second conductive microwires to an electrical interface to facilitate an electrical connection to a remote device.

[0021] In some aspects, the first and second conductive microwires are 25 pm in diameter.

[0022] In some aspects, the insulating material is one of a polymer or a ceramic. In some aspects, the insulating material is isonel.BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Fig. 1 shows a diagram of a microelectrode.

[0024] Fig. 2 shows a diagram of a microelectrode.

[0025] Fig. 3 shows a diagram of the working principles of a microelectrode.

[0026] Fig. 4 shows a diagram of the working principles of a microelectrode.

[0027] Figs. 5A-5E show the design, fabrication procedures, and working principle of the microelectrodes. (Fig. 5A) Photograph of a microwire held with tweezers. (Fig. 5B) Schematic of the two-step microelectrode fabrication: cutting a commercially available insulated metal wire to achieve a 25 pm diameter electrode followed by surface functionalization. (Fig. 5C) An illustration showing the sensor poised for implantation into the mouse brain. The anode interfaces with the target location for signal capture through enzymatic oxidation, whereas the cathode is located within a stable environment to serve as a reference point for background stability through reduction. (Fig. 5D) Photographs demonstrating the mechanical flexibility and robustness of NTs, with examples including integration into a fabric, bending into various shapes, insertion into a hydrogel simulating brain tissue, and conforming around a rod. (Fig. 5E) Conceptual illustration depicting the monitoring of glutamate release during synaptic transmission with the enzyme-based anode (left) and schematic representations of the interface structure and working principle for the anode (center) and cathode (right).

[0028] Figs. 6A-6L show functional characterization of the microelectrodes as glutamate sensors. (Figs. 6A-6D) SEM images of the sensor during various development stages. (Figs. 6A- 6B) Bare electrode with a cross-section of 25 pm diameter. (Fig. 6C) The finalized cathode (post-PtBlack; PtC coating). (Fig. 6D) The finalized anode (post-PtBlack; TTF; GluOx coating). (Fig. 6E) Impedance plot from Electrochemical Impedance Spectroscopy (EIS) comparing before / after PtBlack deposition. (Fig. 6F) The dynamic response curve for glutamate changes (0 to 1400 pM; 200 pM / step) in PBS at room temperature. (Fig. 6G) The calibration curve of the glutamate response (linear sensitivity of 43.35 mV / mM). (Fig. 6H) Reversibility test with incremental glutamate concentration (200 pM / step) in PBS over three cycles. (Fig. 61) Interference test results before / after polydopamine (pDA) treatment with the introduction of sequential analytes (Glu, urea, dopamine (DA), serotonin (5-HT)) concentration (200 pM / step). (Fig. 6J) Sensor calibration response before / after pDA treatment. (Fig. 6K) Stability test of glutamate response over five days in PBS at room temperature. (Fig. 6L) Comparative glutamate response before / after sensor insertion into a brain-simulating hydrogel.

[0029] Figs. 7A-7B show PtBlack electroplating via cyclic voltammetry. (Fig. 7A) Setup for PtBlack electroplating using CV method with a scan rate of 0.1 V / s between -0.4 ~ 0.1 V vs AgCl and repeated five cycles. (Fig. 7B) Resulting CV curve over 5 cycles of scanning.

[0030] Fig. 8 shows protocol for fabricating glutamate-sensitive sensor. Schematic details the steps for fabricating electrodes for glutamate detection, which involves electroplating with PtBlack and sequential dip-coating in solutions of GluOx-based and PtC -based cocktails for the anode and cathode, respectively.

[0031] Fig. 9 shows reversibility assessment for glutamate sensor where the calibration curve was derived from the test results.

[0032] Figs. 10A-10B show setup of mechanical stability assessment of the glutamate sensing interface using agarose hydrogel. (Fig. 10A) The experimental flow of the assessment. (Fig. 10B) Images detailing the sensor setup before insertion and its condition during insertion into the Agarose hydrogel.

[0033] Figs. 11 A-l IB show raw data of the interference in lx PBS at room temperature before and after treatment. (Fig. 11 A) Shows the sensor's response to a 200 pM difference in several compounds (glutamate, urea, serotonin, dopamine, ascorbic acid, glucose) introduced at 100s intervals. (Fig. 1 IB) Sensor behavior after overnight equilibration in BSA and dopamine at 4°C.

[0034] Figs. 12A-12K show ex vivo assessment of glutamate release using the microelectrodes in acute brain slices of mice. (Fig. 12A) Photograph of NTs, showcasing theintegration of anode and cathode wires with a PCB and wire holder. (Fig. 12B) Schematic of the glutamate release from the presynaptic terminal upon electrical stimulus followed by the diffusion into the extracellular space for detection using NTs. (Figs. 12C-12D) Microscopy of the experimental setup. (Fig. 12C) The overview of the experiment setup, where the anode of the sensor and stimulation probe are in contact with the hippocampal circuit. The mouse brain slice is maintained within an oxygenated aCSF. (Fig. 12D) Microscopic image of the anode inserted in the hippocampal CAI region with the cathode placed in the adjacent aCSF solution. (Fig. 12E) Sensor response to single electrical stimulus (amplitude: ! mA; duration: 0.5 ms), comparing the effects of functionalized and non-functionalized sensors (n=3). (Fig. 12F) Box plot of the sensor mean responses from (Fig. 12E), which compares the results of the two conditions. (Fig. 12G) STFT analysis of the sensor response to a single electrical stimulus. (Fig. 12H) Sensor responses to varied intensity of electrical stimuli (amplitude: 0.5 mA, 1 mA, 2 mA; duration: 0.5 ms). (Fig. 121) Sensor readout during sequential electrical stimuli, which consists of 5 pulses (amplitude: 1 mA; duration: 0.5 ms) with an interval between each pulse train of 20 s. (Fig. 12J) Sensor responses to varied inter-pulse intervals ranging from 0.1 s to 40 s between two pulses. Each pulse has amplitude: 1 mA and duration: 0.5 ms (Fig. 12K) Calibration graph derived from (Fig. 12J), plotting the maximum calibrated glutamate concentration against interpulse intervals.

[0035] Figs. 13A-13K show in vivo response to whisker stimulation in mice (Fig. 13 A) Schematic of NTs implantation in the mouse model for real-time glutamate detection. The anode implants to the barrel cortex, while the cathode stays within the adjacent silane solution. (Fig. 13B) Schematics and photographs represent the experimental setup. A servomotor brushes the whiskers with programmed parameters (five times per trail at a frequency of 1 Hz; with a 150 s delay between each trail) (Fig. 13C) Flowchart of the data processing. Digital filters separate electrophysiological signals (HPF; fc= 2 Hz) and glutamate signals (LPF; fc= 0.2 Hz) from the mixed readout. (Figs. 13D-13E) Real-time sensor readouts with whisker stimulation. Black arrows mark the stimulation starting point. (Fig. 13E) Zoom-in view of signal separation into high-frequency activity (electrophysiology) and low-frequency signals (glutamate event). Black arrows mark the starting point of stimulation. (Figs. 13F-13G) Real-time sensor readout without stimulation. The signal reflects the neural spontaneous event. (Fig. 13G) Zoom-in view of signal separation into high-frequency activity (electrophysiology) and low-frequency signals (glutamate event). (Fig. 13H) Analytical methods for two testing scenarios (w / and w / o stimulation). Time-locked window (30 s) synchronizes the stimulation event at 5 s. Fixed-interval windows (30 s) are randomly selected throughout the trail without stimulation. (Fig.131) Time-locked responses to whisker stimulation in 5 mice, with 3 trials per mouse. Each grey curve represents the glutamate response from a single trial. The red line and shaded area represent the average response and its standard error, respectively. (Fig. 13 J) Fixed-interval responses to spontaneous events in 5 mice, with 3 trials per mouse. Each grey curve represents the glutamate response from a single trial. The blue line and shaded area indicate the average response and its standard error, respectively. (Fig. 13K) Statistical analysis compares the glutamate response between whisker stimulation and spontaneous recording.

[0036] Figs. 14A-14E show detailed procedure of sensor implantation surgery. (Fig. 14A) Revealing the skull via micro-scissor application. (Fig. 14B) Executing craniotomy at predefined coordinates using a precision drill. (Fig. 14C) Conducting durectomy to access cortical surface. (Figs. 14D-14E) Positioning and inserting the electrode into the L4 layer of the cortex, targeting 1mm anterior to Bregma and 3mm lateral to Lambda, to a precise depth of 800pm.

[0037] Figs. 15A-15C show whisker stimulator assembly and operation. (Fig. 15A) The assembled whisker stimulator is mounted on a stereotactic frame and ready for use. (Fig. 15B) Operational view of the stimulator, with the stimulation bar positioned to engage the whiskers. (Fig. 15C) Schematic representation of the stimulator's components, with part counts specified for assembly.

[0038] Figs. 16A-16C show analysis of neural response to whisker stimulation. (Fig. 16A) Bandpass-filtered signal highlighting breathing-induced artifacts within the frequency range of 0.2 Hz to 2 Hz. (Fig. 16B) Close-up view of the electrophysiological response to whisker stimulation, delineated by peaks indicated with blue arrows. (Fig. 16C) Waveform depicting the averaged neuronal firing pattern in response to repeated stimuli.

[0039] Fig. 17 shows glutamate response profiles in control and stimulated conditions. (Left) Control recordings display the baseline glutamate response in N=5 mice over 3 trials, showing only spontaneous events. (Right) Recordings during whisker stimulation superimpose stimulation-induced responses on spontaneous events, with a dashed line indicating the onset of stimulation.

[0040] Fig. 18 shows glutamate response profiles in whisker removal group. Recordings during whisker stimulation on mice without whisker. Dash line indicating the onset of stimulation.

[0041] Fig. 19 shows an FEA model. The two-dimensional finite element model with cohesive elements along the crack axis with a height of 3000 gm and a width of 1500 gm for soft brain tissue, a height of 2000 gm, and a tip angle of 50 degrees for stiff needles.

[0042] Figs. 20A-20H show mechanical interactions during neural probe penetration into brain tissue. (Fig. 20A) Distribution of Mises stress at the onset of tissue fracture, comparing the stress profiles for needles with diameters of 25 pm and 50 pm at penetration depths of 46 gm and 78 gm, respectively. (Fig. 20B) Energy absorption profile during stiff needle penetration into soft brain tissue. (Fig. 20C) Dependency of reaction force (Fr) on penetration depth for needles of varying diameters (5 gm, 15 gm, 25 gm, 35 gm, 50 gm). (Fig. 20D) The reaction force F0 of the needle at initial tissue fracture changes with needle diameters during penetration from 0 mm to 200 mm. (Fig. 20E) Illustration of soft needle penetration dynamics where Fr <Fcr or Fr>Fcr, leading to potential buckling during penetration. (Fig. 20F) Diameter-dependent critical buckling force (Fcr) for soft needles (diameter: 5 gm, 15 gm, 25 gm, 35 gm, 50 gm) with a fixed length factor (g) of 0.7. The needle length exposed outside the tissue diminishes with increasing penetration depth. (Fig. 20G) Contrast between the critical buckling force for soft needles and the reaction force during stiff needle penetration for a 25 gm diameter needle. (Fig. 20H) Correlation between critical length (Lcr) required to prevent buckling in soft needles and needle diameter.

[0043] Figs. 21 A-21B show mises stress distribution. (Fig. 21 A) Penetration depth at 500 gm. (Fig. 2 IB) Penetration depth at 1000 gm, comparing needle diameters of 25 gm and 50 gm.

[0044] Fig. 22 shows critical force for soft needles. Relationship between needle diameter (5 gm, 15 gm, 25 gm, 35 gm, 50 gm) and the critical force required to prevent buckling, with a length factor (g) of 0.5. length of the needle exposed outside the tissue decreases as penetration depth increases, affecting the critical buckling force.

[0045] Fig. 23 shows fluorescent staining of brain tissue post-neural probe insertion. Representative fluorescence images of lateral striatal slices following immunohistochemical staining (from left to right): cell nuclei are identified by DAPI (blue), neuronal nuclei by NeuN (red), apoptotic cells by activated Caspase-3 (grayscale), and a merged image combining all markers. Each panel includes a 100 pm scale bar for reference.

[0046] Fig. 24 shows an example ex vivo experimental setup.

[0047] Fig. 25 shows the results of the example ex vivo experiment.

[0048] Fig. 26 shows an example in vivo experimental setup.

[0049] Fig. 27 shows the results of the example in vivo experiment.DETAILED DESCRIPTION

[0050] Disclosed herein are microelectrodes and related fabrication processes that address various shortcomings of current microelectrode designs. For example, the disclosed fabrication process is much more cost effective than current microfabrication techniques. The disclosed microelectrodes have a smaller form factor than current microelectrode devices and can be fabricated of flexible materials, thereby improving ease of insertion and reducing the risk of tissue damage. In addition, the sensing interface of the disclosed microelectrodes can be configured for detecting designated biomarkers (e.g., neurotransmitters, ions, hormones, etc.).

[0051] Described herein is a miniaturized microsensor, characterized by its simplicity in fabrication, exceptional sensitivity, and reliability. The disclosed microsensor is capable of recording real-time neurological responses. Due to its flexible and miniaturized design, the disclosed microsensor can be seamlessly integrated with existing prosthetic devices, enhancing their utility and functionality. The disclosed microsensor and related fabrication methods could also be utilized for brain-machine interfaces, as their design minimizes brain damage during insertion, ensuring safer application. Furthermore, the disclosed microsensor holds considerable promise in clinical settings, where it can be used to monitor patients with neurological disorders or to diagnose these conditions with greater accuracy. Notably, the disclosed microsensor can be configured to detect various different biomarkers, further expanding its applicability across a wide range of clinical and research scenarios.Microelectrode and Related Fabrication Processes

[0052] Shown in Fig. l is a microelectrode configured for use in a biochemical sensor, according to some implementations. The microelectrode utilizes a conductive microwire with an insulating sheath or coating. Generally, the conductive microwire is highly conductive, flexible, and biocompatible; thus, the conductive microwire may be formed of a material having these properties. In some implementations, the conductive microwire is platinum (Pt) but other conductive materials such as gold (Au) or organic conductors are contemplated herein. Other suitable materials may include metals and alloys (e.g., Ag, Cu, Ni, Al, W, bronze, brass, steel, etc.), conductive polymers (e.g., Polypyrrole, Polyaniline, PEDOT:PSS, etc.), carbon-based materials (e.g., Carbon Nanotubes, Graphene, Graphite, etc.), conductive ceramics (e.g., Yttria- stabilized zirconia, Zinc oxide, etc.), semiconductors (e.g., Si, Ge, GaAs, etc.), liquid metals (e.g., Gallium, Gallium alloys, etc.), and / or composite materials (e.g., metal nanoparticles in polymer matrix, etc.).

[0053] In some implementations, the conductive microwire is 25 pm in diameter although the present disclosure is not intended to be limiting in this regard. For example, the conductive microwire is generally less than 100 pm in diameter; in other words, ranging from about 0.1 pm to 100 pm. As shown, the insulator that coats the conductive microwire is polymer, ceramic, or the like. More generally, as with the material of the conductive microwire, the insulator is flexible, biocompatible, and thermally stable. For example, the insulating material that coats the conductive microwire may be a polymer or plastic (e.g., PVC, PE, PTFE, Polyimide, Epoxy resins, Silicone, PP, etc.), a ceramic (e.g., Alumina, Silica, etc.), a natural material (e.g., Rubber, Enamel, etc.), a composite material (e.g., ceramic particles in polymer matrix, etc.), and / or glass (e.g., thin glass coatings, etc.). In some implementations, the microelectrode is fabricated, in part, by removing a portion of the insulation that coats the microwire, thereby revealing a conductive portion of the microwire. The resulting exposed cross-section can form the core of the microelectrode. In one specific implementation, the microelectrode is a 25pm isonel-coated Pt microwire.

[0054] After fabricating or otherwise obtaining a conductive microwire, as described above, a porous conductor is formed on an exposed tip portion of the conductive microwire, enhancing electron transfer capabilities significantly. A porous conductor is a material that, while being electrically conductive, has a high volume of void spaces (pores) within its structure. This porosity allows for a greater surface area exposed to its surrounding environment compared to a non-porous conductor. For electrochemical sensors, this is beneficial as it facilitates more significant interaction between the conductor and the analyte, improving the sensitivity and response time of the sensor. In some implementations, the porous conductor is formed by electrodeposition, for example when a current is applied at the exposed wire tips. Other suitable techniques for forming the porous conductor include, but are not limited to anodization, chemical etching, template-assisted synthesis, sputters, thermal decomposition, physical or mechanical methods, laser ablation, or hydrothermal or solvothermal synthesis.

[0055] The techniques are known in the art and are characterized herein. Anodization is an electrochemical process where a metal (e.g., aluminum or titanium) is oxidized to produce a porous oxide layer on its surface. The oxide layer can subsequently be made conductive by various means. Chemical etching is carried out using a reactive chemical solution to selectively remove parts of a material to create pores. Template-assisted synthesis is carried out using a template, often a nano- or micrometer-scale structure, to grow the conductor. Once the growth is complete, the template is removed, leaving a porous structure. Sputtering is a process whereatoms are ejected from a source material (the target) and deposit as a thin film on a substrate. By modifying the sputtering conditions, porous films can be created. Thermal Decomposition occurs when some precursors, when decomposed thermally, leave a porous structure. This is often used with metal-organic frameworks (MOFs) or other precursor materials. Physical or mechanical methods include techniques like bead milling or sandblasting that can create porous surfaces, although the control over pore size and distribution may be limited. Laser ablation is carried out using lasers to remove material and create a porous structure. Hydrothermal or solvothermal synthesis includes when conductive materials can be synthesized in a water-based (hydrothermal) or organic solvent-based (solvothermal) solution under elevated temperatures and pressures to achieve porous structures.

[0056] Subsequently, a biorecognition material, or biocatalyst, is applied to the porous conductor on the tip of the microwire to establish a sensing interface. Generally, the biorecognition material(s) may be selected based on their specificity to interact with the target biomarker. Example biorecognition materials include, but are not limited to, enzymes (e.g., Glucose oxidase (GOx), Glutamate oxidase, GABase, Cholesterol oxidase, Urease, etc.), oligonucleotide probes (i.e., specific short DNA or RNA sequences), antibodies (e.g., anti-PSA (for prostate-specific antigen), anti -Hb Ale (for glycosylated hemoglobin), etc.), ionophores (e.g., Valinomycin (for potassium ions), ETH 1001 (for calcium ions), etc.), Molecularly Imprinted Polymers (MIPs) designed for specific small molecules or ions, and / or pH-sensitive polymers (e.g., Poly(acrylic acid), Poly(methacrylic acid), etc.)

[0057] In some implementations, the microwire is dip-coated with a biorecognition material. Other suitable techniques for applying the biorecognition material include, but are not limited to spin coating, layer-by-layer assembly, electropolymerization, self-assembled monolayers, drop casting, spray coating, inkjet printing, microcontact printing, covalent linking, physical adsorption, electrochemical reduction, photoimmobilization

[0058] The techniques are known in the art and are characterized herein. In spin coating, the electrode is placed on a spinner, and the biorecognition material is dropped onto it. As the spinner rotates, a thin, uniform layer forms on the electrode. Layer-by-Layer Assembly (LbL) involves the alternate deposition of positively and negatively charged layers, allowing for controlled buildup and incorporation of biomolecules. Electropolymerization pertains to certain monomers that can be polymerized directly onto an electrode's surface using an applied electrical potential. This can trap or incorporate biorecognition elements. Self-assembled monolayers (SAMs) are often used with thiol groups on gold electrodes. The method to makeSAMs involves molecules spontaneously organizing themselves into a monolayer on a substrate. Drop casting involves a droplet of the biorecognition material solution that is placed onto the electrode and allowed to evaporate, leaving a film behind. Spray coating includes when the biorecognition material is sprayed onto the electrode, forming a thin layer. Inkjet printing is especially relevant for array electrodes where specific biomarkers are needed in defined locations. Microcontact printing is when a stamp is used to transfer the biorecognition material onto the electrode surface in a patterned manner. Covalent linking is when some electrode surfaces are be modified to contain reactive groups that can then be used to covalently attach the biorecognition molecules. Physical adsorption may include simply incubating the electrode in a solution of the biorecognition material can sometimes be enough, relying on van der Waals forces, hydrophobic interactions, or other non-covalent interactions. Electrochemical reduction may include when some materials are deposited onto an electrode surface via their electrochemical reduction. Photoimmobilization may include when some biorecognition materials are attached to surfaces using specific wavelengths of light.

[0059] The resultant potential gradient is directly proportional to the concentration of the sensed molecule, facilitating sensitive and efficient detection methods. As shown in Fig. 2, two or more microwires may be used together in a system to form a biochemical sensor. For example, one microwire can be configured as an anode and another as a cathode. In some implementations, the microwire is (or multiple microwires are) electrically connected to an interface (e.g., a circuit board) and / or a remote device (e.g., a microcontroller, a computer, etc.). For example, in Fig. 2, two microwires are shown to be electrically connected to a printed circuit board (PCB) that facilitates a connection to a remote processing device.

[0060] As shown in Fig. 3, in some configurations of a biochemical sensor that includes two microwires - one configured as an anode and another configured as a cathode - each microwire may be dip-coated with different biorecognition materials and / or may include different layers of materials at a tip of the respective wire. For example, the microwire configured as a cathode may have a porous conductor and a catalyst layered on a tip (e.g., an exposed end) of the microwire. Meanwhile, the microwire configured as an anode may have a porous conductor, a mediator, and a catalyst layered on the tip (e.g., an exposed end) of the microwire.

[0061] The “mediator” discussed above and shown in Fig. 3 is utilized to facilitate electron transfer between the enzyme (or other biocatalyst) and the electrode surface. Generally, the mediator is a redox-active compound that can shuttle electrons between the catalyst and the electrode. The mediator acts as bridges allowing electron transfer in systems where the catalystcannot directly exchange electrons with the electrode. In some implementations, the mediator is tetrathiafulvalene (TTF). Other suitable redox mediators include ferrocene and its derivatives; methylene blue; quinones (e.g., anthraquinone, benzoquinone); ruthenium complexes; Prussian blue; cobaltocenium / cobaltocene; thionine; ferrocyanide / ferricyanide; N,N,N',N'-tetramethyl-p- phenylenediamine (TMPD); and 2,6-Dichlorophenolindophenol (DCIP).

[0062] In some implementations, mediators can be immobilized on the electrode by being: (i) adsorbed onto the electrode surface from a solution; (ii) immobilized within a polymer matrix that is coated on the electrode surface; or (iii) covalently attached to the electrode surface or the catalyst; however, other suitable techniques are contemplated herein. The mediator may be integrated with a catalyst by: (i) mixed-matrix configuration, in which the mediator and the catalyst can be combined within a single layer, where both are embedded in a polymer matrix, allowing close proximity for effective electron transfer; or (ii) layered configuration, where the mediator can be coated directly onto the electrode, followed by deposition of the catalyst layer. With respect to (ii), alternatively or additionally, the catalyst layer can be put down first, followed by the mediator layer. Other suitable techniques for integrating the mediator and catalyst are contemplated herein.

[0063] The catalysis at the anode triggers the chemical reaction of the sensing molecule, thus generating electrons. An electron mediator acts to enable smooth transfer of electrons between the sensing molecule and the gold wire. The porous conductor layer magnifies the overall surface area of the interface. This process culminates in the generation of a current, creating a potential gradient across the load resistor. This gradient is proportional to the concentration of the sensing molecule.

[0064] Referring now to Fig. 4, the reference electrode maintains a stable surface potential by standardizing the ionic strength. In some implementations, the reference electrode includes a microwire, an insulator coating the microwire, a porous coating, and a solid electrolyte covering the tip of the electrode. The working electrodes are strategically coated with ISM or aptamers. Their surface charges fluctuate in accordance with the concentration of target markers, which includes ions, neurotransmitters, pH levels, etc.EXAMPLESEXAMPLE 1:

[0065] Disclosed herein is a sensing paradigm that aims to reconcile the need for flexibility with ease of fabrication and functional robustness. This study introduces a wire-typeneurotransmitter sensing interface (diameter: 25 pm), referred to as “NeuroThreads” (NTs), created through straightforward engineering of commercially available Isonel-insulated platinum microwires. The resulting device is not only minimally invasive but also potentially capable of detecting multiple bio-signals through proper engineering of the sensing interface. To showcase the efficacy of NTs, this study presents a demonstration of an enzyme-functionalized two- electrode system designed for the detection of glutamate in the mouse brain. Glutamate is an important excitatory neurotransmitter in the mammalian CNS and plays an essential role in regulating neural network signaling transmission through both synaptic and extra-synaptic paths [30, 31], Specifically, glutamate modulates the propagation of electrical signals across synapses: The release of glutamate into the synaptic cleft upon the arrival of a nerve impulse at a synaptic terminal leads to its binding to receptors, such as N-methyl-D-aspartate (NMD A) and a-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMP A), on the postsynaptic neuron. These binding triggers ion channel openings and ion influx, leading to the depolarization of the postsynaptic membrane and, potentially, the initiation of an action potential [31-33], Additionally, glutamate plays a significant role in synaptic plasticity, influencing learning and memory

[0034] , Imbalances in glutamate dynamics result in cognitive and functional deficits, such as traumatic brain injuries

[0035] , seizures

[0036] , and Alzheimer’s disease

[0033] ,

[0066] This study explores design principles and evaluates the resulting system for glutamate sensing using in vitro, ex vivo, and in vivo approaches: Benchtop tests assess the response to varying glutamate concentrations within physiologically relevant concentrations, confirming its sensitivity, stability, and selectivity. Ex vivo experiments employ the hippocampal circuit in the acute mouse brain slice model to monitor real-time glutamate dynamics under electrical stimuli. As for in vivo experiments, the NTs investigate the linkage between the barrel cortex and whisker stimulation. It examines the coupling between spatially resolved neuronal firing activities and glutamate release events upon whisker stimulation. The results offer mechanistic insights into how glutamate spread in the extracellular space impacts neural network excitability both temporally and spatially. These studies not only validate the design of NTs but also highlight their potential as a tool for advancing our understanding of neurological conditions and developing new diagnostic and therapeutic strategies. Its concise fabrication process and the low infrastructure requirements make it a practical and attractive choice for researchers to integrate biosensors into their studies.

[0067] This study introduces a wire-type biochemical sensor with a micro-scale electrode capable of real-time detection of neurotransmitter dynamics. This sensor, featuring a micro-scaleelectrode, integrates key design attributes for CNS applications: flexibility for neural conformity, minimal invasiveness for reduced tissue disturbance, manufactural scalability, and versatile sensing objectives — potentially ranging from ions and various neurotransmitters to metabolites and hormones. As an example, the study customizes the NTs for glutamate detection. This design translates glutamate concentration changes into measurable electrical signals through enzymatic catalysis. It is capable of integrating within neural networks, measuring glutamate directly in the extracellular space, which is essential for monitoring and understanding neurological health.

[0068] Figs. 5A-5B illustrates the core concept of NTs: an isonel-insulated platinum microwire, precisely cut to expose its cross-sectional to form a diameter of 25 pm microelectrode. It achieves microfabrication level dimensions with a much simpler process and eliminates the needs for facilities such as a cleanroom. After the cutting step, the microelectrode undergoes a dip-coating process with an enzyme cocktail solution to provide the sensitivity and selectivity needed for neuronal chemical monitoring. Fig. 5C shows the integration approach of NTs with tissue, specifically optimized as neural interfaces in this work. The adaptability of NTs is demonstrated in Fig. 5D by integration from fabric matrices to soft hydrogel constructs. This illustrates the potential to integrate seamlessly into various form factors, which can serve independently as a neural implant or integrate with other subsystems. The NTs preserve the existing biosensing landscape while maintaining adaptability and tissue conformity.

[0069] The glutamate sensing interface in this work is based on galvanic redox potentiometry, as shown in Fig. 5E. The anodic and cathodic reactions generate electrical currents proportional to glutamate concentration. A load resistor then transforms the current flow into a voltage reading. The anodic surface (Fig. 5E, center) features a layer of glutamate oxidase (GluOx) within a BSA-Nafion matrix. The electrochemical reaction initiates the generation of electrons by catalyzing glutamate oxidation with GluOx. Subsequently, tetrathiafulvalene (TTF), an electron mediator, transports charge carriers to the underlying electrode with platinum black (PtBlack), a conductive porous material. The cathode facilitates the reduction of oxygen by a platinum-carbon (PtC) composite within the Nafion matrix on top of the electrode with PtBlack (Fig. 5E, right). The simple fabrication procedures, customizable sensing interfaces, and low infrastructure requirements make NTs an improved biosensors.

[0070] Sensing performance in response to glutamate. Scanning Electron Microscopy (SEM) images show the evolution of the surface morphology of NTs through various fabrication stages: Figs. 6A-6B highlights the bare electrode, created by cutting a 25 pm diameter insulatedmicrowire. Fig. 6C presents the modified surface structure of the cathode after PtBlack electroplating (Fig. 7) and a series of dip-coating processes with PtC solution. Fig. 3D illustrates the morphology of the anode after the functionalization process with TTF and the enzyme cocktail solution depicted in Fig. 8. Initially, the bare electrode featured a smooth and uniform surface, which shifted to a porous structure after functionalization, resulting in a higher surface area for the interaction between glutamate, TTF and GluOx. Electrochemical impedance spectroscopy (EIS) confirmed that internal series resistance (Rs) reduced from 100 MQ to 1 MQ after the PtBlack modification, which contributed to improving the charge transfer efficiency and sensitivity

[0037] ,

[0071] The following benchtop characterizations evaluate key performance metrics of the NTs, including sensitivity, stability, selectivity, and operational longevity. This characterization procedure recorded potential differences across a 250 MQ load resistor in between the cathode and anode of the sensor. The load resistor was designed to sufficiently exceed the Rs to mitigate the voltage division effect.

[0072] Figure 6F illustrates the response of the system to step-wise increments of addition of glutamate, set at 200 pM over 200 second intervals. The resulting calibration curve (Fig. 6G) demonstrates a linear response of 43.35 mV / mM, in line with the theoretical expectation that signal output is directly proportional to glutamate concentrations up to a defined saturation point (approximately 1 mM)

[0038] , The calculation of the limit of detection (LOD) utilizes the following equation (3*(system noise / slope of calibration curve)). The system noise is defined as the standard deviation of the signal level at equilibration in the first 200 seconds of the curve in Fig. 6F, which is estimated as 0.04022 mV and yields a LOD value of 2.78 pM. This LOD level stays in the range of typical ambient glutamate concentrations in the brain (approximately 0.02- 20 pM in extracellular spaces)

[0039] ,

[0073] Fig. 6H shows the reversibility of the NTs across a dynamic range of 0 to 800 pM by cycling glutamate concentrations within the spectrum. The experiment increases concentration by 200 pM four times for each cycle and repeats the cycles three times. This range encompasses the glutamate concentration not only in brain-related areas like CSF and brain extracellular fluids (1-10 pM) but also in blood (40-60 pM) and plasma (50-100 pM), opening up potential uses for NTs in other parts of the body [40-42], Data recorded in Figs. 3H and 3L capture the last 5 seconds of the signal before the next glutamate addition to allow sensor stabilization. The sensor was demonstrated to be suitable for continuous, dynamic monitoring in physiologicalcondition. Fig. 9 quantifies the result in Fig. 6H with linear regression and reveals a Pearson's correlation coefficient of 0.96764 over three cycles.

[0074] Sensitivity tests evaluated the performance of NTs in the presence of common interfering substances such as urea, dopamine (DA), and serotonin (5-HT) (Fig. 61). Each step corresponds to a 200 pM increase in analyte concentration. Previous research has demonstrated that the functionalization of polydopamine (pDA) can improve enzyme fixation through its selfpolymerization due to the presence of amine and catechol groups

[0043] , However, pDA may also increase the non-specific binding of biomolecules and reduce the selectivity of the sensor

[0044] , Previous studies have addressed this issue by incubating the sensor with BSA, which blocks the leftover binding sites of the electrode surface

[0045] , In this study, modifying the sensor with a mixture of pDA and BSA struck a balance between interface stability and resistance to nonspecific binding.

[0075] The subsequent experiment evaluates the response to glutamate and interferents before and after treatment with pDA and BSA mixture. The calibration response, as shown in Fig. 6J, illustrates a slight decrease in glutamate sensitivity from 38.25 mV / mM to 28.78 mV / mM after the modification. Concurrently, the trend after the addition of urea followed the previous baseline drift of the readout, which implies that the sensor is insensitive to urea. The upward trend could be attributed to the nonequilibrium of the interface, where previous glutamate steps diffuse slowly within the porous sensing electrode until equilibrium is reached. On the other hand, the resistance to charged analytes like serotonin (5-HT) and dopamine (DA) show significant improvement. Sensitivity to 5-HT decreased from -15 mV / mM to -3.3 mV / mM, a 71% reduction, and for DA, it decreased from -45.6 mV / mM to -13.1 mV / mM, a 62% reduction. These results demonstrate that by properly designing the passivation strategy, the sensor can reduce the interference from non-target sensing molecules.

[0076] The stability test evaluated the performance of NTs over five days in PBS at room temperature (Fig. 10). As shown in Fig. 6K, NTs exhibit an average sensitivity of 59 mV / mM with a deviation of 17.9 mV / mM in the first four days. During this period, a decrease in sensitivity was observed, with a reduction of 33.4% at 120 hours, dropping to near zero by the end of day five. It is contemplated that the decrease may be attributed to the degradation of enzymes or leaching of functional material

[0022] , This temporal evaluation provides the reference point for the potential use and calibration of the device over a long period of time.

[0077] Post-insertion performance tests evaluate the interface robustness of NTs against mechanical stress comparable to in vivo implantation, as depicted in Fig. 11. Employing anagarose hydrogel mimicking the brain tissue Young’s modulus (10 kPa

[0046] ) simulates the conditions of surgical implantation to assess sensitivity retention. The results as shown in Fig. 6L demonstrate the sensitivity retention of the initial value after insertion, suggesting the NTs can maintain integrity during in vivo applications.

[0078] Ex-vivo studies of glutamate release from acute brain slices of mice. The following section investigates neurotransmitter dynamics using the NTs on hippocampal brain slices from mice, specifically targeting the CAI region that accounts for learning and memory. Fig. 12A illustrates the integration of the cathode and anode within a wire holder on a printed circuit board (PCB) fixture. These wires were electrically connected to a 250 M resistor and measured the resulting potential correlated with the concentration of glutamate at the anode site. The experiment utilized electrical stimulation to trigger the glutamate release at the synaptic terminals (Fig. 12B). The stimulation probe targeted the Schaffer collateral pathway connecting the CA3 and CAI regions. The anode was inserted into the hippocampal slice at the CAI region, with the cathode placed in the adjacent artificial cerebrospinal fluid (aCSF) to maintain a stable environment (Fig. 12C-12D). When a stimulation activates CA3 presynaptic neurons, an action potential then propagates to the CAI region, subsequently facilitating the release of glutamate into the synaptic cleft followed by the diffusion into the extracellular space.

[0079] Figs. 12E-12F show a comparison between the performance of probes with and without functionalization (TTF and GluOx): The functionalized sensor exhibited a potential change of 6.55 +0.15 mV following a 1 mA bi-phasic stimulus, whereas the unfunctionalized probe showed a minimum amplitude of potential change of 0.57 ± 0.35 mV. The results suggest a significant difference (p<0.001) between the two groups. The voltage reading was translated into concentration based on the calibrated sensitivity, as shown in Fig. 6G, and yields a value of A151 pM, which is close to the reasonable fluctuation range of localized extracellular concentration of glutamate (1-100 pM) reported in the literature

[0047] , The temporal analysis of the sensor response with Short-Time Fourier Transform (STFT), as shown in Fig. 12G, indicates a predominance of low-frequency signals, primarily below 0.2 Hz with an amplitude of potential changes ranging from 0 - 15 mV and corresponding to A0 to 350 pM in glutamate concentration. The sensor response time, spectrum pattern, and glutamate concentration changes are similar to the reported extracellular glutamate sensor [48-50],

[0080] Additional investigations involved varying the electrical stimulus intensity from 0.5 mA to 2 mA, delivered in biphasic pulses with a width of 0.5 ms. The results, as shown in Fig. 12H, illustrate the correlation between the intensity of electrical stimulation and the sensorreadout of extracellular glutamate. The result as shown in Fig. 12H is consistent with a previous study, where electrical stimulation with higher frequency, intensity or pulse width induces a greater extracellular glutamate release

[0050] ,

[0081] The following experiments further investigated glutamate release by applying a series of stimulations with varied parameters (such as interval and the number of pulses). The first experiment applied five biphasic pulses, each with 0.5 ms pulse width and an intensity of 1 mA, with a 20 second interval between consecutive pulse trains. The glutamate level increased after each stimulus and formed a stepwise response, as shown in Fig. 121, which implies that the glutamate level builds up with every stimulus. Although the increase in glutamate levels was lower compared to Fig. 12H, these variations can be attributed to reduced dynamics of glutamate release due to prior stimulation of the brain slices or variation among different animal s / cronoplanes.

[0082] The temporal dependency of the stimulus and glutamate readout was further evaluated by applying two pulses (0.5 ms duration and 1mA intensity) with different stimulation intervals (0.1, 1, 2.5, 5, 10, 20, 40 seconds). The representative curve of the experiment is shown in Fig. 12J. The peak value of the curve, as shown in Fig. 12K, shows a trend of decreasing glutamate levels with longer inter-pulse intervals (>20 seconds). The decrease in measured glutamate concentration is attributed to diffusion into the surrounding tissue as time extends between stimuli [26, 39], Overall, the ex vivo experiments highlight NTs capability of capturing the extracellular glutamate signal within the local microdomain, which has been reported to exhibit significant fluctuations upon neuronal firing events [7, 52],

[0083] In vivo studies on triggered and spontaneous glutamate release in mice. The recording capability of NTs in live animals was studied using a model correlating whisker deflection with firing events in the barrel cortex, an area located at layer IV of the mouse primary somatosensory cortex (SI) [53-58], Such deflection activates the primary sensory neuron in the trigeminal ganglion, and this signal propagates through the principal trigeminal brainstem nucleus (Pr5) to the ventral posterior medial nucleus (VPM). Given that each VPM specifically projects to an individual barrel, the deflection triggers a whisker-specific event at SI

[0057] , Moreover, SI captures information from both contralateral and ipsilateral whisker stimulation, previous research suggested such interactions originate the discrimination of bilateral tactile stimuli as contralateral and ipsilateral stimulation creates different spike patterns

[0053] , Recent research has utilized iGluSnFR to study whisker-induced glutamate release in the barrel cortex. It was found that all whiskers deflected within 40 ms using unilateral air puffing and glutamatereadouts showed an immediate increase of glutamate fluorescence of over 10% in the large region (360x250 gm) in the barrel cortex

[0059] , Given these established models, whisker- controlled glutamate release can serve as an ideal method to measure the extracellular glutamate dynamics.

[0084] This study used an experimental setup shown in Figs. 13 A and 14. The study utilized stereotaxic surgery for sensor implantation targeting the L4 region (3 mm lateral, and 1 mm posterior to the bregma). Figure 13B and Fig. 15 illustrate the servomotor setup on a stereotactic frame, providing the sensory input. The servomotor was programmed to brush all the whiskers on the right side. Each brushing cycle lasted 100 ms and occurred five times at 1 second intervals. Because the extracellular glutamate diffusion would take a longer period compared to free space [60, 61], the sequence was paused for a 150-second interval before the next cycle, and the NTs were monitored during the entire response in the barrel cortex throughout the experiment.

[0085] Fig. 13D illustrates the real-time readout of the NT in response to whisker stimulations highlighted by black arrows. The results show that there is a high-frequency signal coupled with whisker stimulation in addition to low-frequency glutamate signal captured by the sensor, and the subsequent glutamate concentration increase. Similarly, other studies reported observing electrophysiological signals as high-frequency components (above 1 Hz) on the same electrode used for electrochemical recordings by using amperometry or galvanic redox potentiometry [62- 65], Previous studies also report the use of a digital filter to separate the recorded electrophysiological signal from the electrochemical signal.

[0086] The study utilized digital filters to extract neurochemical signals (low pass filter; fc= 0.2 Hz) and LFP (high pass filter; fc= 2 Hz) from the sampling rate of 0.4 ms. Fig. 13E illustrates the signal after digital filter separation. As shown in Figs.l3E and Fig. 16, the stimulation- induced rapid LFP, with amplitude of 0.15 mV, was aligned with previously reported sensory - evoked potential at the barrel cortex, the previously reported amplitude were approximately 0.25 mV

[0067] , In comparison, there was no such signal observed after removing the whiskers of the mouse and only providing the tactile stimulation (see Fig. 17), which rules out the possibility that the high frequency components were artifacts. The results show that the sensor can capture electrophysiology because of the similar working principle with local field potential (LFP) setup, which measures potential between the recording site and reference and utilized high input impedance. It was observed that the NTs captured additional high-frequency signal notcorrelated to whisker stimulation (see Figs. 13F and 13G). According to previous research, this phenomenon may originate from spontaneous neuron-firing episodes in the L4 region

[0068] ,

[0087] To enhance the distinction between whisker-induced events and spontaneous activity, the study employs a time-locked and fixed-interval analysis. This time-lock method analyzes trails with whisker stimulation, which aligns a window based on the stimulation onset time across multiple trials and aggregates these trials into a single average trace. By this method, any event that is not consistently correlated to the whisker stimulation, such as a spontaneous event or environmental noise, can be canceled out due to the randomness of these signal sources. As shown in Fig. 131, the method aggregated data across five mice (three trials each in Fig. 18), each trial reflects the glutamate response pattern to whisker stimulation. The trails with a 30 second window were aligned based on the stimulation onset time at 5 seconds, and the average glutamate level dynamic and its standard error are shown by the thick line and shaded area, respectively. The results show the glutamate level increased immediately after the stimulation onset and rose approximately 5 pM after 15 seconds following the stimulation. This result is consistent with the previously reported method, where whisker stimulation induces 4-6 pM glutamate releases

[0069] , And such a result was observed in both contralateral and ipsilateral stimulation (Fig. 19).

[0088] On the other hand, the fixed-interval method analyzed the trials without whisker stimulation. In this condition, all the HFC and glutamate events happened randomly, therefore, aggregation of the trails with the same window size averaged out all the fluctuation, and the remaining part served as a control group of the experiment, showing the baseline activity. As shown in Figs. 13J and 18, the method aggregates data across five mice, in which three 30 second frames are selected from each trail randomly without overlapping. The thick line and shaded area depict the average value and standard error of the trails, respectively. The results show this method averaged out the glutamate fluctuation and minimizes the impact of spontaneous events. In Fig. 13K, an analytical comparison between with and without whisker stimulation reveals a significant increase in glutamate levels with whisker stimulation, marked by a p-value of 0.04, highlighting the stimulation induced glutamate activity beyond the baseline.

[0089] Mechanical behavior and brain damage analysis. The insertion of neural probes into the fragile neural tissue presents formidable challenges, including the risk of tissue damage and inflammatory reactions. Meanwhile, flexible probes are highly susceptible to buckling which leads to the failure of the probe implantation into the brain. The main objective of this section isto study the mechanical behaviors of flexible probes inserted into brain tissue, including the effect of needle diameter on the reaction force of the probe and the damage to brain tissue during penetration. The goal was to guide the development of probe designs that significantly reduce the likelihood of brain injury and prevent the buckle of soft probes.

[0090] The simulation of the probe insertion process employed commercial finite element software ABAQUS to analyze the mechanics of a simplified rigid body probe penetrating brain tissue. The sharp-tipped probe initiated a planar mode-I crack at the entry point, which expands as the probe advances [70-72], Computational limitations necessitated the use of a two- dimensional plane strain finite element model (Fig. 19), as a comprehensive three-dimensional model would demand excessive computational resources [72, 73],

[0091] Initial penetration by probes with diameters of 25 pm and 50 pm deformed the tissue and initiated cracks, as illustrated in Fig. 20A. Fig. 20B illustrates the energy response of the probe-brain tissue system during the insertion of a 25 pm diameter probe into depths up to 200 pm. This response was categorized into three stages [72, 74], In stage I, the conversion of external work primarily resulted in the accumulation of strain energy. During this stage, the contact area between the probe and brain tissue was relatively small, resulting in minimal friction loss compared to the strain energy. The reaction force of the probe increased with the deformation of brain tissue. In stage II, as the reaction force of the probe reached its maximum, the brain tissue starts to rupture, and the reaction force rapidly decreased. This stage was characterized by the initial increase of fracture energy from a value of 0 and a decrease in strain energy due to tissue relaxation. In stage III, the crack began to propagate, leading to an increase in the contact area between the probe and the tissue. Consequently, the friction force acting on the probe also increased, which corresponds to the rising reaction force as shown in Fig 20C. In this stage, both strain energy, fracture energy, and friction loss tended to increase.

[0092] Fig. 20C displays the reaction force (F-) of probes with varying diameters against penetration depth ranging up to 500 pm, which indicates that larger diameter probes experienced greater reaction force during penetration. Additionally, Fig. 20D illustrates the reaction force of the needle at initial tissue fracture (Fo), highlighting that a thicker probe resulted in higher reaction forces. The tissue’s response also varied with probe diameter. Fig. 20A and Fig. 21 present the Mises stress distribution in the tissue at initial punch on, depths of 500 pm, and 1000 pm. A probe with a diameter of 50 pm demonstrated a high-stress region larger than that of a probe with a diameter of 25 pm.

[0093] Addressing the flexible nature of the wire-type sensor, it was crucial to consider how mechanical behavior under force deviated from rigid-body assumptions, particularly in scenarios where the reaction force during penetration leads to buckling. When the reaction force of the probe during penetration is greater than its buckling critical force, the probe buckles resulting in failure of penetration as shown in Fig. 20E. The critical buckling load increased with penetration depth for probes with length factors of 0.5 and 0.7 (Figs. 20F and 22), due to the shorter needle length exposed to the tissue as the penetration depth increased, which increased the critical load for buckling.

[0094] For the probe with a diameter of 25 pm, the critical load during penetration (p=0.7) was significantly higher than the reaction force required for tissue penetration in Fig. 20C, enabling it to function as a rigid probe (Fig. 20G). The longer the length of the probe, the lower the critical load for buckling. The critical length (Lcr) of a flexible probe is defined by the length with equal maximum Frand Fcr. When the penetration depth ranges from 0 to 200 pm (as shown in Fig. 20D), the probe must have a length below the critical length to prevent buckle during penetration. Figure 20H provides a correlation of the critical length of the probe with the probe’s diameters. For example, for a probe with a diameter of 25 microns with an insertion depth ranging from 0 to 200 pm, the maximum reaction force required was 4.39 pN. To ensure a smooth penetration into the tissue without causing probe bucking, its length should not be less than 17 cm.

[0095] The following immunohistochemical study evaluates the effects of a 25 pm diameter probe insertion on brain tissue integrity in C57BL / 6J mice. A stereotaxic frame guided the insertion under anesthesia, with the probe carefully advancing at a rate of 100 pm / min to a depth of 800 pm and remained in place for three hours before slowly removing. Subsequent saline perfusion and dissection of the brains allowed for immunohistochemical processing.

[0096] As shown in Fig. 7, DAPI and NeuN staining revealed the maintenance of cell nuclei and neuronal structures, while minimal Caspase-3 activation indicated a low apoptotic response post-insertion. The observed tissue damage, with a diameter of 45 pm, was comparable to that reported for other brain implants [75-77], yet larger than the actual probe size, resulting from insertion conditions such as insertion rate

[0078] or insertion angle. This issue points to the potential for enhanced accuracy using advanced neural probe guides or other navigational methods for neural implants [79, 80],

[0097] Materials and Methods

[0098] Materials. Isonel-insulated platinum wire (diameter 25 pm; A-M system, USA), Tetrathiafulvalene (TTF; Sigma Aldrich USA), L-Glutamate Oxidase (25U; Cosmo, USA), Nafion (5 wt%; Sigma Aldrich, USA), Platinum on carbon (Pt / C, 10%; Sigma-Aldrich, MO, USA), Silwet L-77 (PlantMedia, USA), Chloroplatinic acid (PtCleEk; Sigma, USA), Lead acetate (Pb(C 214302)2; Sigma, USA), 250 MOhm resistor (5% 1 / 8W 0805; Stackpole Electronics Inc).

[0099] Methods: Fabrication of Wire-type Microelectrode. The fabrication of wire-type microelectrodes started with applying 100 grams of tension by tweezers to straighten the isonel- insulated wires. Next, two 25 mm wire segments were inserted into a 30 AWG needle holder and exposed 10 mm of wire beyond the needle tip to yield a stable handling system. The wire was trimmed with a razor blade to create a clean cross-section at each end to form the microelectrodes. The wire was submerged with its holder in isopropanol alcohol and sonicated for 5 minutes to create a clean interface and complete the microelectrode fabrication.

[0100] Electrodeposition ofPtBlack on Microelectrodes . As shown in Fig. 8, PtBlack was electrodeposited onto the microelectrodes to enhance the active surface area of the sensing interface. Cyclic voltammetry (CV) was utilized within a voltage range of -0.4 to 0.1 V at a scan rate of 0.1 V / s to conduct the electrodeposition. The electrodeposition solution was prepared with 3 percent by weight chloroplatinic acid (PtCleEk) and 0.1 percent by weight lead acetate (Pb(C 214302)2) in deionized water. The electrodeposition setup was established by immersing the electrodes in the electrodeposition solution and connecting each electrode individually to the electrochemical station. Electrodeposition was performed sequentially on each electrode to ensure a uniform layer of PtBlack. The electrodes were rinsed with deionized water and air-dried to prepare the electrodes for the next steps.

[0101] Functionalization of Microelectrode: Enzyme-based Glutamate Sensing Interface . The functionalization of the enzyme-based glutamate sensing interface included preparing both an anode and a cathode. The cathode was fabricated by dip-coating through a Pt / C solution, which contained 10 percent by weight platinum on carbon in 2 percent by weight of Nafion solution in ethanol. The microelectrodes were immersed with PtBlack in this Pt / C mixture and then airdried. The dip-coating procedure was repeated five times to ensure adequate coverage and uniformity.

[0102] The anode was dip-coated in ultrasonically homogenized TTF solution, which included 0.1 M in a mixture of acetone and ethanol at a ratio of 1 :9 v / v. The electrodes were air-driedand the process was repeated five times to ensure a consistent coating. Next, the electrodes were drip-coated once with a Silwet solution, which included 0.5% concentration in de-ionized water to improve the wettability of the TTF-coated microelectrodes. The enzyme cocktail for the anode was prepared by mixing glutamate oxidase (50 U / ml), bovine serum albumin (BSA, 5 mg / ml), and a diluted Nafion solution (0.5 wt% in PBS). For the cocktail preparation, BSA (20 mg) was dissolved in PBS (875 pl), glutaraldehyde (0.5 wt% in 125 pl PBS) was added, and then the mixture was incubated at room temperature for 30 minute. Next, Nafion was diluted to 1 percent by weight in PBS and glutamate oxidase was dispersed at 200 U / ml in PBS. The cocktail was mixed to a volume ratio of 1 : 1 :2 of glutamate oxidase solution, BSA solution, and Nafion solution, respectively. The TTF-coated microelectrodes were dip-coated in the enzyme cocktail and air-dried. The dip-coating process in the enzyme cocktail was repeated ten times to ensure a thorough and uniform coating on the electrode surfaces.

[0103] Polydopamine Treatment. The anode was immersed in 0.1 M dopamine solution with 0.25 percent by weight of BSA in PBS after coating then the immersed anode was left in refrigerated conditions overnight to initiate the oxidation of the dopamine in the solution and the subsequent deposition of a polydopamine layer on the anode. This step reduced electrostatic adsorption and enhanced the selectivity of the sensor. After treatment, the sensor was rinsed with deionized water, air-dried, and stored in a freezer for preservation.

[0104] Characterization: Electrical and Electrochemical Characterization. The impedance of the microelectrodes was quantified utilizing an electrochemical (EC) station in Electrochemical Impedance Spectroscopy (EIS) mode. In this setup, a microelectrode functioned as the working electrode (WE), alongside an Ag / AgCl reference electrode (RE) and a Pt wire counter electrode (CE). The electrochemical impedance at the interface was measured using a redox couple of K4Fe(CN)e / K3Fe(CN)6 (1 : 1 ratio) at a concentration of 2 mM in a phosphate-buffered saline (PBS) buffer. The parameters for these measurements included a frequency range from 0.1 Hz to 50 kHz and a sine-wave amplitude of 20 mV.

[0105] The glutamate sensitivity of the sensor was calibrated using the EC station in an opencircuit potentiometry mode. The sensor was connected to a 250 M resistor to ensure it was at least ten-fold the series resistance of the interface and then linked to the EC station, with the anode connected to the RE and the cathode to the WE. The calibration was conducted in a 10 ml solution of PBS at room temperature, which was pre-oxygenated with air, ensuring optimal conditions for the enzyme. Concentrated glutamate (0.2 M) was incrementally added in 5 plvolumes to the PBS, resulting in stepwise increases in glutamate concentration by 200 pM. The measurement sequence ranged from 0 to 1000 pM.

[0106] Interferences Characterization. The selectivity of the sensor was assessed using methods similar to those employed for sensitivity calibration. First, concentrated solutions of various analytes (Glutamate, Urea, Serotonin, and Dopamine) at a concentration of 0.2 M in PBS were prepared. The EC station was set to open-circuit potential mode, then the sensor was connected via a 250 Mohm resistor to the EC station, with the anode connected to the RE and the cathode to the WE. A consistent environment was ensured by conducting the calibration procedure in a 10 ml solution of PBS at room temperature, pre-oxygenated with air. Each concentrated analyte (0.2 M) was added sequentially in 10 pl increments to the PBS, which resulted in stepwise increases in analyte concentration up to 200 pM.

[0107] Lifetime Characterization, the lifetime of the sensor while stored in PBS at room temperature was evaluated by monitoring the sensitivity change over time. The sensitivity of the sensor was measured using a method consistent with those employed in previous characterizations.

[0108] In these tests, subjecting the sensor to two incremental glutamate concentration changes, each by 200 pM, in PBS at room temperature was the procedure. Repeating this procedure periodically tracked any changes in the glutamate detection sensitivity. The evaluation aimed to quantify its long-term performance and stability under room temperature storing conditions.

[0109] Mechanical Stability Characterization. The electrical properties of the sensor were monitored during mechanical testing by connecting the sensor to an EC station in chronopotentiometry mode. A current of 100 pA was applied to the wire sensor and the voltage was recorded continuously to assess changes in the conductivity of the wire to determine the mechanical stability under cyclic bending conditions.

[0110] The interfacial stability of the sensor was evaluated by simulation of insertion of the sensor into brain tissue using an agarose hydrogel. The hydrogel included with 0.6 wt% agarose in PBS to replicate the mechanical properties of brain tissue, specifically the Young’s modulus. The interfacial stability under sensor insertion was determined by conducting tests on the sensor both before and following its insertion into the hydrogel and comparing the resulting change. These evaluations utilized the calibration methods described in the electrochemical characterization section.[OHl] Ex-vivo Experiment: Brain Slice Preparation. First, acute brain slices were prepared from both male and female mice (C57BL / 6J mice, 2-3 months old). Anesthesia was induced with isoflurane, and its effectiveness was confirmed by testing for the absence of toe pinch and corneal reflex responses to ensure the mice were adequately anesthetized. Next, the mice were decapitated, and the brains were promptly removed and placed into an ice-cold dissecting solution. The dissecting solution composition included: 87 mM NaCl, 2.5 mM KC1, 1.25 mM NaEEPCh, 7 mM MgCh, 0.5 mM CaCh, 25 mM NaHCCh, 25 mM glucose, and 75 mM sucrose, oxygenated with a 95% 02 / 5% CO2 gas mix. A Leica VT1200 vibratome was used to section 300 pm thick coronal brain slices. The slices were incubated for 25 minutes at 35°C in the dissecting solution and subsequently stored at room temperature in aCSF to prepare them for the next step. The aCSF contained 124 mM NaCl, 3 mM KC1, 1.25 mM NaH2PO4, 2 mM MgCh, 2 mM CaCh, 26 mM NaHCOs, and 10 mM glucose, and oxygenated with gas mixture (95% O2 and 5% CO2).

[0112] Electrical Stimulation and Sensor Measurement. To begin the ex vivo experiments, the microelectrode was positioned to interface with the hippocampal CAI region. The anode was carefully inserted into the hippocampal tissue, and the cathode was placed within the adjacent artificial aCSF. The aCSF was oxygenated with a gas mixture (95% O2 and 5% CO2) to ensure the proper environment. The Schaffer collateral fibers were stimulated using a tungsten concentric bipolar electrode (World Precision Instruments, #TM33CCINS), which was connected to an isolated stimulator (Model 2100, A-M Systems). The response was systematically optimized by investigating various stimulation parameters, including pulse intensities, numbers, and intervals. The stimuli were administered using a bi-phasic mode with a fixed duration of 0.5 ms for each phase.

[0113] The measurement was started by electrically connecting the glutamate sensor to a surface mount 250 MOhms resistor. The voltage across this resistor was recorded using an EC station (PalmSense 4) with Bluetooth data transmission, operating in open-circuit potential mode with a sample rate of 0.4 ms. The entire experiment was conducted within a Faraday cage to ensure accuracy and minimize environmental interference.

[0114] Animal Study: Surgery for Implantation of Sensor into the Mice Brain. The study utilized both male and female C57BL / 6J mice, aged 7 to 8 weeks. The mice were anesthetized with isoflurane at a concentration of 3% for induction, which was maintained at 2% for the duration of the procedure to ensure proper anesthesia. Each mouse was securely positioned on a stereotaxic frame (RWD) to prepare them for surgery. To start the surgery, a midline incisionwas made along the scalp to expose the skull. A reference mark was placed 3 mm lateral (right side) and 1 mm posterior to the bregma to guide the subsequent craniotomy.

[0115] Based on this reference point, a craniotomy was performed using a hand-held cranial drill (RWD), creating a cranial window approximately 2 x 2 mm in size. The bone flap was carefully lifted and removed using either a 20-gauge needle or fine forceps to expose the brain. The exposed brain area was gently washed with a silane solution to control any minor bleeding. A 30-gauge needle tip was bent to create a small hook, which was used to gently engage and delicately remove the dura mater from the craniotomy site and carefully avoid direct contact with the brain tissue beneath.

[0116] The anode was implanted within the right-side barrel cortex region targeting the L4 layer. The anode of the sensor was precisely lowered into the cortex at a controlled pace of 100 pm / min to a depth of 800 pm. The cathode was simultaneously positioned adjacent to the silane to complete the setup. After the placement, the anesthesia level was reduced to 0.75% isoflurane for at least one hour to allow the mouse to stabilize. The insertion site was kept hydrated with a silane solution throughout the experiment to ensure tissue integrity.

[0117] Whisker Stimulation Procedure and Sensor Measurement. The whisker stimulation procedure was initiated following the surgical implantation of the neural sensor in mice, which involved mounting a custom-designed whisker stimulator onto the stereotaxic frame. The stimulator arm was aligned with the whisker pad to ensure comprehensive stimulation of the entire right side.

[0118] The stimulation parameters were controlled using a microcontroller (Arduino Mega) with a servo motor providing the necessary controlled movement. The stimulator arm was programmed to move the servo motor between 0 to 33 degrees within 0.1 seconds. The stimulation was repeated five times at 1 -second intervals with 150-second rest period between each set of five stimuli.

[0119] The glutamate sensor was electrically mounted to a surface mount 250 MOhms resistor for monitoring during the whisker stimulation, the voltage across the resistor was monitored and recorded continuously using a PalmSense 4 EC station operated in open-circuit potential mode and data was transmitted via Bluetooth with a sample rate of 0.4 ms. The entire experimental setup was enclosed within a Faraday cage to ensure the precision of the measurements and to mitigate any environmental electromagnetic interference. The timing of the whisker stimulation and the sensor signal recording were synchronized to allow for accurate correlation of the stimuli with the sensor response.

[0120] Statistical Analysis. Statistical analysis of the experimental data was performed using Matlab and Origin Pro software. A dual filtering approach was applied, which consisted of a low-pass filter with a cutoff frequency of 0.2 Hz and a high-pass filter with a cutoff frequency of 2 Hz, to separate the electrophysiological and glutamate electrochemical signals from the mixed sensor signal. Following the filtering process, short-time Fourier transforms were executed in Origin Pro to analyze changes in frequency and power spectra over time.

[0121] Random 30-second windows were selected from the filtered data using Matlab with no overlap between these selections to prepare the spontaneous event assessment. The baselines of these segments were standardized to zero, which involved averaging the first 5 seconds of data points in each window. Replicating this process created a comprehensive dataset for subsequent analysis.

[0122] Thirty-second windows were selected for the data segments in the context of whisker stimulation events, with the initial 5 seconds preceding the stimulation. A consistent dataset for analysis was established by standardizing baselines of these windows to zero based on the average of the first 5 seconds of data points.

[0123] FEA Simulation of Probe Penetration into Tissue. The relationship between the force and displacement during the penetration of probes with varying diameters, as well as the distribution of von Mises stress within the tissue were studied using FEA commercial software ABAQUS. The simulation model was formed by setting the dimensions of the tissue to a height of 3000 pm and a width of 1500 pm and utilizing a rigid probe with a height of 2000 pm and a tip angle of 50 degrees. The simulations was performed using explicit dynamics with a total computation time of 50 seconds. The interaction parameters were defined assuming the contact between the needle and skin is a hard contact and a friction coefficient of 0.5. The lateral and bottom surfaces of the tissue were fixed, and a constant velocity of 20 pm / s was applied to the reference point, as illustrated in Fig 19.

[0124] The mesh cohesive zones were setup by discretizing the tissue using three-node linear plane strain triangular elements and introducing a layer of zero-thickness cohesive elements into the tissue as shown by the yellow line in Fig 8. Four-node two-dimensional cohesive elements were used for the meshed cohesive zones. The tissue was modeled as an elastic material by assigning material parameters with an elastic modulus £tissue= 20 kPa and a Poisson's ratio vtissue=0.475. The cohesive material properties were set to an interfacial strength of 2 kPa and an interfacial energy of 4.7 J / mm2

[0081] ,

[0125] Buckling of a Flexible Probe. The buckling load of the probe was expressed as:

[0126] where E is the elastic modulus of the flexible probe, I is the moment of inertia of the probe cross-section, I is the length of the probe exposed outside the tissue, and p is the effective length factor. The total length of the probe was 2 mm. The elastic modulus of the probe was Eprobe = 169 GPa, and the Poisson’s ratio was yProbe=0.3. The constraints on the probe were between hinge constraints (p=0.7) and fixed constraints (p=0.5). It is noted that for needles of the same length, their critical load at an effective length factor of 0.7 was lower than their critical load at an effective length factor of 0.5. The probe was considered to be resistant to buckling if the reaction force of the probe during penetration was lower than the critical load of the probe at a length factor of 0.7.

[0127] In conclusion, this study introduced NTs, a low-cost and minimally invasive neural sensing platform, capable of monitoring neurotransmitter dynamics in complex biological environments. The microwire-made platform eliminates the need for microfabrication and other resource-intensive techniques. Functionalizing the platform with GluOx enabled a sensitivity of 43.35 mV / mM and selectivity to glutamate within a detectable range of 0 - 1000 pM. The sensor remained functional for over 120 hours in PBS at room temperature. The NTs displayed consistent performance when inserting into the brain-mimicking hydrogel, subjected to 10,000 cyclic bindings, and during repeated cycles of glutamate concentration changes. Ex vivo experiments with mouse hippocampal slices illustrated the capability of the sensor on capturing real-time glutamate dynamics and observing low-frequency signals below 0.2 Hz linked to glutamate release triggered by electrical stimulation. Notably, the in vivo tests in mouse models demonstrated the effectiveness of NTs in CNS, observing glutamate dynamics and electrophysiology in the barrel cortex in response to whisker stimulation. The stimulation induced a direct relationship of 5 pM glutamate concentration increase over the spontaneous event at the barrel cortex.

[0128] It is contemplated that the system can be utilized in monitoring traumatic brain injury (TBI) patients, where the sensing wire is implanted through the skull to monitor glutamate levels less invasively and continuously in the cerebrospinal fluid (CSF). Additionally, the detection targets can expand to include dopamine, a pleasure neurotransmitter associated with Parkinson’s disease and schizophrenia, and serotonin, a mood neurotransmitter related to depression, anxiety, and aggression. The system can also expand the latest brain-machine interfaces(BMIs) with biochemical sensing capability. For example, Neuronlink has utilized robotic arms to insert microwire BMIs to decode electrophysiology. By implementing the study system design with this kind of BMI system, researchers can gain insights into neuropsychiatric disorders and cognitive functions across brain regions. Overall, the system provides a versatile building block that allows both research and clinical application to build miniaturized sensing system with different form factors.EXAMPLE 2:

[0129] To demonstrate the disclosed biochemical sensor, ex vivo and in vivo experiments were conducted on a mouse model. Fig. 24 shows an experimental set for glutamate detection in the mouse model. In this configuration, the anode composition includes glutamate oxidase, serving as the anode catalyst, alongside Nafion, BSA, and glutaraldehyde which collectively act as the immobilizer for the oxidase. The addition of TTF, an electron mediator, ensures specific interaction with glutamate, generating an electrical current. The cathode is constructed from PtC, which acts as the catalyst, and Nafion, functioning as the immobilizer, ensuring stability and performance. Electrodeposition is used to create a porous material layer, with platinum black (PtBlack) being the preferred substance due to its high conductivity and surface area.

[0130] As demonstrated in Fig. 25, the disclosed biochemical sensor successfully detected the release of glutamate, demonstrating its practical application and robust functionality. This experimental outcome provides crucial insights into neuronal communication mechanisms, further extending understanding of various disease states.

[0131] Fig. 26 shows an experimental setup for in vivo glutamate detection. In this example, the disclosed biochemical sensor has been strategically inserted into the L4 cortical region, specifically at a coordinate 1 mm anterior to the Bregma, ensuring optimal positioning for data capture. As demonstrated in Fig. 27, the disclosed biochemical sensor demonstrates the ability to record neurotransmitter responses with high temporal resolution, highlighting its accuracy and efficacy in real-time data capture.Configuration of Certain Implementations

[0132] The construction and arrangement of the systems and methods as shown in the various implementations are illustrative only. Although only a few implementations have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes, and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position ofelements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative implementations. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the implementations without departing from the scope of the present disclosure.

[0133] Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also, two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.

[0134] It is to be understood that the methods and systems are not limited to specific synthetic methods, specific components, or to particular compositions. It is also to be understood that the terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting.

[0135] As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, another implementation includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another implementation. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0136] “Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.

[0137] Throughout the description and claims of this specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other additives, components, integers or steps.“Exemplary” means “an example of’ and is not intended to convey an indication of a preferred or ideal implementation. “Such as” is not used in a restrictive sense, but for explanatory purposes.

[0138] Disclosed are components that can be used to perform the disclosed methods and systems. These and other components are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these components are disclosed that while specific reference of each various individual and collective combinations and permutation of these may not be explicitly disclosed, each is specifically contemplated and described herein, for all methods and systems. This applies to all aspects of this application including, but not limited to, steps in disclosed methods. Thus, if there are a variety of additional steps that can be performed it is understood that each of these additional steps can be performed with any specific implementation or combination of implementations of the disclosed methods.REFERENCES:1. Wu, Z., D. Lin, and Y. Li, Pushing the frontiers: tools for monitoring neurotransmitters and neuromodulators. 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Claims

WHAT IS CLAIMED IS:

1. A biochemical sensor comprising: a reference electrode comprising: (i) a first conductive microwire coated in an insulating material, wherein a distal portion of the first conductive microwire is exposed, (ii) a porous conductor layer formed on the exposed distal portion of the first conductive microwire, and (iii) a catalyst layer formed on the porous conductor layer; and a working electrode comprising: (i) a second conductive microwire coated in the insulating material, wherein a distal portion of the second conductive microwire is exposed, (ii) a porous conductor layer formed on the exposed distal portion of the second conductive microwire, (iii) a mediator layer formed on the porous conductor layer, and (iv) a catalyst layer formed on the mediator layer.

2. The biochemical sensor of claim 1, wherein the porous conductor layer of each of the reference and working electrodes is formed by electrodeposition.

3. The biochemical sensor of claim 1, wherein the catalyst layer of each of the reference and working electrodes is a biorecognition material.

4. The biochemical sensor of claim 3, wherein the biorecognition material comprises a solid electrolyte.

5. The biochemical sensor of claim 3, wherein the biorecognition material comprises an in silico maturation (ISM) or aptamers.

6. The biochemical sensor of claim 1, wherein the first and second conductive microwires comprise gold or platinum.

7. The biochemical sensor of claim 1, further comprising an electrical interface to facilitate an electrical connection to a remote device, wherein the reference and working electrodes are electrically coupled the electrical interface.

8. The biochemical sensor of claim 1, wherein the first and second conductive microwires are 25 pm in diameter.

9. The biochemical sensor of claim 1, wherein the insulating material is one of a polymer or a ceramic.

10. The biochemical sensor of claim 1, wherein the insulating material is isonel.

11. A method of fabricating a biochemical sensor, the method comprising: providing a first conductive microwire and a second conductive microwire, wherein each of the first and second conductive microwires are coated in an insulating material; removing the insulating material form a distal portion of each of the first and second conductive microwires to form an exposed distal portion of each of the first and second conductive microwires; forming a porous conductor on the exposed distal portion of each of the first and second conductive microwires; and forming a catalyst layer of a biorecognition material on the porous conductor of each of the first and second conductive microwires.

12. The method of claim 11, wherein the porous conductor is formed by electrodeposition.

13. The method of claim 11, wherein the catalyst layer is formed by dip-coating.

14. The method of claim 11, wherein the biorecognition material is a solid electrolyte.

15. The method of claim 11, wherein the biorecognition material an in silico maturation(ISM) or aptamers.

16. The method of claim 11, wherein the first and second conductive microwires are formed of gold or platinum.

17. The method of claim 11, further comprising electrically connecting the first and second conductive microwires to an electrical interface to facilitate an electrical connection to a remote device.

18. The method of claim 11, wherein the first and second conductive microwires are 25 pm in diameter.

19. The method of claim 11, wherein the insulating material is one of a polymer or a ceramic.

20. The method of claim 11, wherein the insulating material is isonel.

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