Size-Dependent Electrochemistry of Laser-Induced Graphene Electrodes.

Laser-induced graphene (LIG) electrodes have become popular for electrochemical sensor fabrication due to their simplicity for batch production without the use of reagents. The high surface area and favorable electrocatalytic properties also enable the design of small electrochemical devices while retaining the desired electrochemical performance. In this work, we systematically investigated the effect of LIG working electrode size, from 0.8 mm to 4.0 mm diameter, on their electrochemical properties, since it has been widely assumed that the electrochemistry of LIG electrodes is independent of size above the microelectrode size regime. The background and faradaic current from cyclic voltammetry (CV) of an outer-sphere redox probe [Ru(NH3)6]3+ showed that smaller LIG electrodes had a higher electrode roughness factor and electroactive surface ratio than those of the larger electrodes. Moreover, CV of the surface-sensitive redox probes [Fe(CN)6]3- and dopamine revealed that smaller electrodes exhibited better electrocatalytic properties, with enhanced electron transfer kinetics. Scanning electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy showed that the physical and chemical surface structure were different at the electrode center versus the edges, so the electrochemical properties of the smaller electrodes were improved by having rougher surface and more density of the graphitic edge planes, and more oxide-containing groups, leading to better electrochemistry. The difference could be explained by the different photothermal reaction time from the laser scribing process that causes different stable carbon morphology to form on the polymer surface. Our results give a new insight on relationships between surface structure and electrochemistry of LIG electrodes and are useful for designing miniaturized electrochemical devices.

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry.

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.

Complex sex and estrous cycle differences in spontaneous transient adenosine.

Adenosine is a ubiquitous neuromodulator that plays a role in sleep, vasodilation, and immune response and manipulating the adenosine system could be therapeutic for Parkinson's disease or ischemic stroke. Spontaneous transient adenosine release provides rapid neuromodulation; however, little is known about the effect of sex as a biological variable on adenosine signaling and this is vital information for designing therapeutics. Here, we investigate sex differences in spontaneous, transient adenosine release using fast-scan cyclic voltammetry to measure adenosine in vivo in the hippocampus CA1, basolateral amygdala, and prefrontal cortex. The frequency and concentration of transient adenosine release were compared by sex and brain region, and in females, the stage of estrous. Females had larger concentration transients in the hippocampus (0.161 ± 0.003 µM) and the amygdala (0.182 ± 0.006 µM) than males (hippocampus: 0.134 ± 0.003, amygdala: 0.115 ± 0.002 µM), but the males had a higher frequency of events. In the prefrontal cortex, the trends were reversed. Males had higher concentrations (0.189 ± 0.003 µM) than females (0.170 ± 0.002 µM), but females had higher frequencies. Examining stages of the estrous cycle, in the hippocampus, adenosine transients are higher concentration during proestrus and diestrus. In the cortex, adenosine transients were higher in concentration during proestrus, but were lower during all other stages. Thus, sex and estrous cycle differences in spontaneous adenosine are complex, and not completely consistent from region to region. Understanding these complex differences in spontaneous adenosine between the sexes and during different stages of estrous is important for designing effective treatments manipulating adenosine as a neuromodulator.

Structural Similarity Image Analysis for Detection of Adenosine and Dopamine in Fast-Scan Cyclic Voltammetry Color Plots.

Fast-scan cyclic voltammetry (FSCV) is widely used for in vivo detection of neurotransmitters, but identifying analytes, particularly mixtures, is difficult. Data analysis has focused on identifying dopamine from cyclic voltammograms, but it would be better to analyze all the data in the three-dimensional FSCV color plot. Here, the goal was to use image analysis-based analysis of FSCV color plots for the first time, specifically the structural similarity index (SSIM), to identify rapid neurochemical events. Initially, we focused on identifying spontaneous adenosine events, as adenosine cyclic voltammograms have a primary oxidation at 1.3 V and a secondary oxidation peak that grows in over time. Using SSIM, sample FSCV color plots were compared with reference color plots, and the SSIM cutoff score was optimized to distinguish adenosine. High-pass digital filtering was also applied to remove the background drift and lower the noise, which produced a better LOD. The SSIM algorithm detected more adenosine events than a previous algorithm based on current versus time traces, with 99.5 ± 0.6% precision, 95 ± 3% recall, and 97 ± 2% F1 score (n = 15 experiments from three researchers). For selectivity, it successfully rejected signals from pH changes, histamine, and H2O2. To prove it is a broad strategy useful beyond adenosine, SSIM analysis was optimized for dopamine detection and is able to detect simultaneous events with dopamine and adenosine. Thus, SSIM is a general strategy for FSCV data analysis that uses three-dimensional data to detect multiple analytes in an efficient, automated analysis.

Recent advances in fast-scan cyclic voltammetry.

Fast-scan cyclic voltammetry (FSCV) at carbon-fiber microelectrodes (CFMEs) is a versatile electrochemical technique to probe neurochemical dynamics in vivo. Progress in FSCV methodology continues to address analytical challenges arising from biological needs to measure low concentrations of neurotransmitters at specific sites. This review summarizes recent advances in FSCV method development in three areas: (1) waveform optimization, (2) electrode development, and (3) data analysis. First, FSCV waveform parameters such as holding potential, switching potential, and scan rate have been optimized to monitor new neurochemicals. The new waveform shapes introduce better selectivity toward specific molecules such as serotonin, histamine, hydrogen peroxide, octopamine, adenosine, guanosine, and neuropeptides. Second, CFMEs have been modified with nanomaterials such as carbon nanotubes or replaced with conducting polymers to enhance sensitivity, selectivity, and antifouling properties. Different geometries can be obtained by 3D-printing, manufacturing arrays, or fabricating carbon nanopipettes. Third, data analysis is important to sort through the thousands of CVs obtained. Recent developments in data analysis include preprocessing by digital filtering, principal components analysis for distinguishing analytes, and developing automated algorithms to detect peaks. Future challenges include multisite measurements, machine learning, and integration with other techniques. Advances in FSCV will accelerate research in neurochemistry to answer new biological questions about dynamics of signaling in the brain.

Thin layer cell behavior of CNT yarn and cavity carbon nanopipette electrodes: Effect on catecholamine detection.

Carbon nanotube yarn microelectrodes (CNTYMEs) are an alternative to carbon-fiber microelectrodes (CFMEs) with interesting electrochemical properties because analyte is momentarily trapped in cavities between the CNTs. Here, we compare fast-scan cyclic voltammetry (FSCV) detection of catecholamines, including dopamine, norepinephrine, and epinephrine, at CNTYMEs, CFMEs, as well as cavity carbon nanopipette electrodes (CNPEs). At CFMEs, current decreases dramatically at high FSCV repetition frequencies. At CNTYMEs, current is almost independent of FSCV repetition frequency because the analytes are trapped in the crevices between CNTs, and thus the electrode acts like a thin-layer cell. At CFMEs, small cyclization product peaks are observed due to an intramolecular cyclization reaction to form leucocatecholamine, which is electroactive, and these peaks are largest for the secondary amine epinephrine. At CNTYMEs, more of the leucocatecholamine cyclization product is detected for all catecholamines because of the enhanced trapping effects, particularly at higher repetition rates where the reaction occurs more frequently and more product is accumulated. For epinephrine, the secondary peaks have larger currents than the primary oxidation peaks at 100 Hz, and similar trends are observed with faster scan rates and 500 Hz repetition frequencies. Finally, we examined CNPEs, which also momentarily trap neurotransmitters. Similar to CNTYMEs, at CNPEs, catecholamines have robust cyclization peaks, particularly at high repetition rates. Thus, CNTYMEs and CNPEs have thin layer cell behavior that facilitates high temporal resolution measurements, but catecholamines CVs are complicated by cyclization reactions. However, those additional peaks could be useful in discriminating the analytes, particularly epinephrine and norepinephrine.

Mechanism of Histamine Oxidation and Electropolymerization at Carbon Electrodes.

Histamine plays an important role in neuromodulation and the biological immune response. Although many electrochemical methods have been developed for histamine detection, the mechanism of its redox reaction has not been directly investigated. Here, we studied the mechanism of histamine oxidation at carbon electrodes and used that mechanistic information to design better fast-scan cyclic voltammetry (FSCV) methods for histamine. Using amperometry, cyclic voltammetry (CV), and X-ray photoelectron spectroscopy (XPS), we demonstrate that histamine oxidation requires a potential of at least +1.1 V vs Ag/AgCl. We propose that histamine undergoes one-electron oxidation on an imidazole nitrogen that produces a radical. The radical species dimerize and continue to undergo oxidation, leading to electropolymerization, which fouls the electrode. CV shows a peak at 1.3 V that is pH dependent, consistent with a one-proton, one-electron oxidation reaction. This mechanism is confirmed using 1- and 3-methylhistamine, which do not electropolymerize, compared to Nα-methylhistamine, which does. XPS also revealed a nitrogen-containing product adsorbed on the electrode surface after histamine oxidation. For FSCV detection of histamine at carbon-fiber microelectrodes, histamine oxidation was adsorption-controlled, and the anodic peak was observed at +1.2 V on the backward scan because of the rapid scan rate. However, the oxidation fouled the electrode and convoluted the FSCV temporal response; therefore, we implemented Nafion coating to alleviate the electrode fouling and preserve the time response of FSCV. Knowing the mechanism of histamine oxidation will facilitate design of better electrochemical methods for real-time monitoring of histamine.

Cavity Carbon-Nanopipette Electrodes for Dopamine Detection.

Microelectrodes are typically used for neurotransmitter detection, but nanoelectrodes are not because there is a trade-off between spatial resolution and sensitivity that is dependent on surface area. Cavity carbon-nanopipette electrodes (CNPEs), with tip diameters of a few hundred nanometers, have been developed for nanoscale electrochemistry. Here, we characterize the electrochemical performance of CNPEs with fast-scan cyclic voltammetry (FSCV) for the first time. Dopamine detection using cavity CNPEs, with a depth equivalent to a few radii, is compared with that using open-tube CNPEs, an essentially infinite geometry. Open-tube CNPEs have very slow temporal responses that change over time as the liquid rises in the CNPE. However, a cavity CNPE has a fast temporal response to a bolus of dopamine that is not different from that of a traditional carbon-fiber microelectrode. Cavity CNPEs, with tip diameters of 200-400 nm, have high currents because the small cavity traps and increases the local dopamine concentration. The trapping also leads to an FSCV frequency-independent response and the appearance of cyclization peaks that are normally observed only with large concentrations of dopamine. CNPEs have high dopamine selectivity over ascorbic acid (AA) because of the repulsion of AA by the negative electric field at the holding potential and the irreversible redox reaction. In mouse-brain slices, cavity CNPEs detected exogenously applied dopamine, showing they do not clog in tissue. Thus, cavity CNPEs are promising neurochemical sensors that provide spatial resolution on the scale of hundreds of nanometers, which is useful for small model organisms or for locations near specific cells.

3D-Printed Carbon Electrodes for Neurotransmitter Detection.

Implantable neural microsensors have significantly advanced neuroscience research, but the geometry of most probes is limited by the fabrication methods. Therefore, new methods are needed for batch-manufacturing with high reproducibility. Herein, a novel method is developed using two-photon nanolithography followed by pyrolysis for fabrication of free-standing microelectrodes with a carbon electroactive surface. 3D-printed spherical and conical electrodes were characterized with slow scan cyclic voltammetry (CV). With fast-scan CV, the electrodes showed low dopamine LODs of 11±1 nm (sphere) and 10±2 nm (cone), high sensitivity to multiple neurochemicals, and high reproducibility. Spherical microelectrodes were used to detect dopamine in a brain slice and in vivo, demonstrating they are robust enough for tissue implantation. This work is the first demonstration of 3D-printing of free-standing carbon electrodes; and the method is promising for batch fabrication of customized, implantable neural sensors.

Surface-engineered natural fibers: Emerging alternative substrates for chemical sensor applications: A review.

Natural fiber has become one of the most widely used alternative materials for chemical sensor fabrication due to its advantages, such as biocompatibility, flexibility, and self-microfluidic properties. Enhanced natural fiber surface has been used as a substrate in colorimetric and electrochemical sensors. This review focuses on improving the natural fiber properties for preparation as a substrate for chemical sensors. Various methods for natural fiber extraction are discussed and compared. Bleaching and decolorization is important for preparation of colorimetric sensors, while carbonization and nanoparticle doping are favorable for increasing their electrical conductivity for electrochemical sensor fabrication. Also, example fabrications and applications of natural fiber-based chemical sensors for chemical and biomarker detection are discussed. The selectivity of the sensors can be introduced and improved by surface modification of natural fiber, such as enzyme immobilization and biorecognition element functionalization, illustrating the adaptability of natural fiber as a smart sensing device, e.g., wearable and portable sensors. Ultimately, the high performances of natural fiber-based chemical sensors indicate the potential uses of natural fiber as a renewable and eco-friendly substrate material in the field of chemical sensors and biosensors for clinical diagnosis and environmental monitoring.

Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening.

BACKGROUND: Diabetes is a significant health threat, with its prevalence and burden increasing worldwide indicating its challenge for global healthcare management. To decrease the disease severity, the diabetic patients are recommended to regularly check their blood glucose levels. The conventional finger-pricking test possesses some drawbacks, including painfulness and infection risk. Nowadays, smartphone has become a part of our lives offering an important benefit in self-health monitoring. Thus, non-invasive wearable sweat glucose sensor connected with a smartphone readout is of interest for real-time glucose detection. RESULTS: Wearable sweat glucose sensing device is fabricated for self-monitoring of diabetes. This device is designed as a body strap consisting of a sensing strip and a portable potentiostat connected with a smartphone readout via Bluetooth. The sensing strip is modified by carbon nanotubes (CNTs)-cellulose nanofibers (CNFs), followed by electrodeposition of Prussian blue. To preserve the activity of glucose oxidase (GOx) immobilized on the modified sensing strip, chitosan is coated on the top layer of the electrode strip. Herein, machine learning is implemented to correlate between the electrochemical results and the nanomaterial content along with deposition cycle of prussian blue, which provide the highest current response signal. The optimized regression models provide an insight, establishing a robust framework for design of high-performance glucose sensor. SIGNIFICANCE: This wearable glucose sensing device connected with a smartphone readout offers a user-friendly platform for real-time sweat glucose monitoring. This device provides a linear range of 0.1-1.5 mM with a detection limit of 0.1 mM that is sufficient enough for distinguishing between normal and diabetes patient with a cut-off level of 0.3 mM. This platform might be an alternative tool for improving health management for diabetes patients.

Non-invasive wearable chemical sensors in real-life applications.

Over the past decade, non-invasive wearable chemical sensors have gained tremendous attention in the field of personal health monitoring and medical diagnosis. These sensors provide non-invasive, real-time, and continuous monitoring of targeted biomarkers with more simplicity than the conventional diagnostic approaches. This review primarily describes the substrate materials used for sensor fabrication, sample collection and handling, and analytical detection techniques that are utilized to detect biomarkers in different biofluids. Common substrates including paper, textile, and hydrogel for wearable sensor fabrication are discussed. Principles and applications of colorimetric and electrochemical detection in wearable chemical sensors are illustrated. Data transmission systems enabling wireless communication between the sensor and output devices are also discussed. Finally, examples of different designs of wearable chemical sensors including tattoos, garments, and accessories are shown. Successful development of non-invasive wearable chemical sensors will effectively help users to manage their personal health, predict the potential diseases, and eventually improve the overall quality of life.

Nanodiamond Coating Improves the Sensitivity and Antifouling Properties of Carbon Fiber Microelectrodes.

Nanodiamonds (NDs) are carbon nanomaterials with a core diamond crystalline structure and crystal defects, such as graphitic carbon and heteroatoms, on their surface. For electrochemistry, NDs are promising to increase active sites and decrease fouling, but NDs have not been studied for neurotransmitter electrochemistry. Here, we optimized ND coatings on microelectrodes and found that ND increases the sensitivity for neurotransmitters with fast-scan cyclic voltammetry detection and decreases electrochemical and biofouling. Different sizes and functionalizations of NDs were tested, and ND suspensions were drop-casted onto carbon-fiber microelectrodes (CFMEs). The 5 nm ND-H and 5 nm ND-COOH formed thick coatings, while the 15 and 60 nm ND-COOH formed more sparse coatings. With electrochemical impedance spectroscopy, 5 nm ND-H and 5 nm ND-COOH had high charge-transfer resistance, while 15 and 60 nm ND-COOH had low charge-transfer resistance. ND-COOH (15 nm) was optimal, with the best electrocatalytic properties and current for dopamine. Sensitivity was enhanced 2.1 ± 0.2 times and the limit of detection for dopamine improved to 3 ± 1 nM. ND coating increased current for other cations such as serotonin, norepinephrine, and epinephrine, but not for the anion ascorbic acid. Moreover, NDs decreased electrochemical fouling from serotonin and 5-hydroxyindoleacetic acid, and they also decreased biofouling in brain slice tissue by 50%. The current at biofouled ND-coated electrodes is similar to the signal of pristine, unfouled CFMEs. The carboxylated ND-modified CFMEs are beneficial for neurotransmitter detection because of easy fabrication, improved limit of detection, and antifouling properties.

Review: New insights into optimizing chemical and 3D surface structures of carbon electrodes for neurotransmitter detection.

The carbon-fiber microelectrode has been used for decades as a neurotransmitter sensor. Recently, new strategies have been developed for making carbon electrodes, including using carbon nanomaterials or pyrolyzing photoresist etched by nanolithography or 3D printing. This review summarizes how chemical and 3D surface structures of new carbon electrodes are optimized for neurotransmitter detection. There are effects of the chemical structure that are advantageous and nanomaterials are used ranging from carbon nanotube (CNT) to graphene to nanodiamond. Functionalization of these materials promotes surface oxide groups that adsorb dopamine and dopants introduce defect sites good for electron transfer. Polymer coatings such as poly(3,4-ethylenedioxythiophene) (PEDOT) or Nafion also enhance the selectivity, particularly for dopamine over ascorbic acid. Changing the 3D surface structure of an electrode increases current by adding more surface area. If the surface structure has roughness or pores on the micron scale, the electrode also acts as a thin layer cell, momentarily trapping the analyte for redox cycling. Vertically-aligned CNTs as well as lithographically-made or 3D printed pillar arrays act as thin layer cells, producing more reversible cyclic voltammograms. A better understanding of how chemical and surface structure affects electrochemistry enables rational design of electrodes. New carbon electrodes are being tested in vivo and strategies to reduce biofouling are being developed. Future studies should test the robustness for long term implantation, explore electrochemical properties of neurotransmitters beyond dopamine, and combine optimized chemical and physical structures for real-time monitoring of neurotransmitters.

Carbon Nanohorn-Modified Carbon Fiber Microelectrodes for Dopamine Detection.

Carbon nanohorns (CNHs), closed cone-shaped cages of sp 2-hybridized carbons, are a promising nanomaterial to improve carbon-fiber microelectrode (CFME) dues to their high specific surface area and edge planes, but few studies have tested their electrochemical properties. Here, we tested the dopamine detection at electrodeposited CNHs on CFME (CNH/CFME). The optimized concentration of CNHs in the deposition solution is 0.5 mg/mL, and the optimized electrodeposition waveform is 10 cycles of triangular waveform scanned from -1.0 V and +1.0 V at 50 mV/s. Using fast-scan cyclic voltammetry, the optimized CNH/CFME enhances dopamine peak current to 2.3 ± 0.2 times that of the CFME. To further increase the current, CNH/CFMEs were oxidized in NaOH (ox-CNH/CFME), which creates more defects and surface oxide groups to adsorb dopamine. The oxidative etching further increases the peak current to 3.5 ± 0.2 times of the CFME, and ox-CNH/CFME had a limit of detection of 6 ± 2 nM. The dopamine anodic current at ox-CNH/CFME was stable for 8 h of continuous scanning. The ox-CNH/CFME enhanced the anodic peak current for other cationic neurotransmitters including epinephrine, norepinephrine, and serotonin, but less enhancement was found for ascorbic acid, showing higher selectivity for cationic molecules. CNHs also decreased tissue biofouling at CFME. Thus, electrodeposited CNHs are a promising new method for increasing the surface area and current of CFMEs for dopamine detection.