Recent advances in fast-scan cyclic voltammetry

Accurate and stable chronic in vivo voltammetry enabled by a replaceable subcutaneous reference electrode

In vivo sensing of neurotransmitters has provided valuable insight into both healthy and diseased brain. However, chronically implanted Ag/AgCl reference electrodes suffer from degradationgradation, resulting in errors in the potential at the working electrode. Here, we report a simple, effective way to protect in vivo sensing measurements from reference polarization with a replaceable subcutaneously implanted reference. We compared a brain-implanted reference and a subcutaneous reference and observed no difference in impedance or dopamine redox peak separation in an acute preparation. Chronically, peak background potential and dopamine oxidation potential shifts were eliminated for three weeks. Scanning electron microscopy shows changes in surface morphology and composition of chronically implanted Ag/AgCl electrodes, and postmortem histology reveals extensive cell death and gliosis in the surrounding tissue. As accurate reference potentials are critical to in vivo electrochemistry applications, this simple technique can improve a wide and diverse assortment of in vivo preparations.

Fast-Scanning Potential-Gated Organic Electrochemical Transistors for Highly Sensitive Sensing of Dopamine in Living Rat Brain

Developing techniques for the highly sensitive assay of neurotransmitters is essential for understanding physiological and pathological processes. Here, we demonstrate a fast-scanning potential (FSP)-gated organic electrochemical transistor (OECT): for the highly sensitive sensing of dopamine (DA) in a living rat brain. The configuration combines the selectivity of fast-scan cyclic voltammetry (FSCV) with the high sensitivity of an OECT. The combined use of FSP as a gating mode and transconductance (g_m ) as a sensing parameter further improve the sensing performance in terms of sensitivity, limit of detection, reproducibility, and stability. The FSP-OECT exhibits a sensitivity of 0.899 S M^-1 and a low limit of detection down to 5 nM and was validated for in vivo monitoring of the basal level and electrically stimulated release of DA.

Flexible Glassy Carbon Multielectrode Array for In Vivo Multisite Detection of Tonic and Phasic Dopamine Concentrations

Dopamine (DA) plays a central role in the modulation of various physiological brain functions, including learning, motivation, reward, and movement control. The DA dynamic occurs over multiple timescales, including fast phasic release, as a result of neuronal firing and slow tonic release, which regulates the phasic firing. Real-time measurements of tonic and phasic DA concentrations in the living brain can shed light on the mechanism of DA dynamics underlying behavioral and psychiatric disorders and on the action of pharmacological treatments targeting DA. Current state-of-the-art in vivo DA detection technologies are limited in either spatial or temporal resolution, channel count, longitudinal stability, and ability to measure both phasic and tonic dynamics. We present here an implantable glassy carbon (GC) multielectrode array on a SU-8 flexible substrate for integrated multichannel phasic and tonic measurements of DA concentrations. The GC MEA demonstrated in vivo multichannel fast-scan cyclic voltammetry (FSCV) detection of electrically stimulated phasic DA release simultaneously at different locations of the mouse dorsal striatum. Tonic DA measurement was enabled by coating GC electrodes with poly(3,4-ethylenedioxythiophene)/carbon nanotube (PEDOT/CNT) and using optimized square-wave voltammetry (SWV). Implanted PEDOT/CNT-coated MEAs achieved stable detection of tonic DA concentrations for up to 3 weeks in the mouse dorsal striatum. This is the first demonstration of implantable flexible MEA capable of multisite electrochemical sensing of both tonic and phasic DA dynamics in vivo with chronic stability.

Multiplexed Monitoring of Neurochemicals via Electrografting-Enabled Site-Selective Functionalization of Aptamers on Field-Effect Transistors

Neurochemical corelease has received much attention in understanding brain activity and cognition. Despite many attempts, the multiplexed monitoring of coreleased neurochemicals with spatiotemporal precision and minimal crosstalk using existing methods remains challenging. Here, we report a soft neural probe for multiplexed neurochemical monitoring via the electrografting-assisted site-selective functionalization of aptamers on graphene field-effect transistors (G-FETs). The neural probes possess excellent flexibility, ultralight mass (28 mg), and a nearly cellular-scale dimension of 50 μm × 50 μm for each G-FET. As a demonstration, we show that G-FETs with electrochemically grafted molecular linkers (-COOH or -NH₂) and specific aptamers can be used to monitor serotonin and dopamine with high sensitivity (limit of detection: 10 pM) and selectivity (dopamine sensor >22-fold over norepinephrine; serotonin sensor >17-fold over dopamine). In addition, we demonstrate the feasibility of the simultaneous monitoring of dopamine and serotonin in a single neural probe with minimal crosstalk and interferences in phosphate-buffered saline, artificial cerebrospinal fluid, and harvested mouse brain tissues. The stability studies show that multiplexed neural probes maintain the capability for simultaneously monitoring dopamine and serotonin with minimal crosstalk after incubating in rat cerebrospinal fluid for 96 h, although a reduced sensor response at high concentrations is observed. Ex vivo studies in harvested mice brains suggest potential applications in monitoring the evoked release of dopamine and serotonin. The developed multiplexed detection methodology can also be adapted for monitoring other neurochemicals, such as metabolites and neuropeptides, by simply replacing the aptamers functionalized on the G-FETs.

Letting the little light of mind shine: Advances and future directions in neurochemical detection

Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.

Applying a Fast-Scan Cyclic Voltammetry to Explore Dopamine Dynamics in Animal Models of Neuropsychiatric Disorders

Progress in the development of technologies for the real-time monitoring of neurotransmitter dynamics has provided researchers with effective tools for the exploration of etiology and molecular mechanisms of neuropsychiatric disorders. One of these powerful tools is fast-scan cyclic voltammetry (FSCV), a technique which has progressively been used in animal models of diverse pathological conditions associated with alterations in dopamine transmission. Indeed, for several decades FSCV studies have provided substantial insights into our understanding of the role of abnormal dopaminergic transmission in pathogenetic mechanisms of drug and alcohol addiction, Parkinson’s disease, schizophrenia, etc. Here we review the applications of FSCV to research neuropsychiatric disorders with particular attention to recent technological advances.

Pushing the frontiers: tools for monitoring neurotransmitters and neuromodulators

Neurotransmitters and neuromodulators have a wide range of key roles throughout the nervous system. However, their dynamics in both health and disease have been challenging to assess, owing to the lack of in vivo tools to track them with high spatiotemporal resolution. Thus, developing a platform that enables minimally invasive, large-scale and long-term monitoring of neurotransmitters and neuromodulators with high sensitivity, high molecular specificity and high spatiotemporal resolution has been essential. Here, we review the methods available for monitoring the dynamics of neurotransmitters and neuromodulators. Following a brief summary of non-genetically encoded methods, we focus on recent developments in genetically encoded fluorescent indicators, highlighting how these novel indicators have facilitated advances in our understanding of the functional roles of neurotransmitters and neuromodulators in the nervous system. These studies present a promising outlook for the future development and use of tools to monitor neurotransmitters and neuromodulators.

Pannexin1 channels regulate mechanically stimulated but not spontaneous adenosine release

Fast-scan cyclic voltammetry (FSCV) is a rapid technique to measure neuromodulators, and using FSCV, two modes of rapid adenosine have been discovered. Spontaneous transients occur randomly in the brain, while mechanical stimulation also causes a rapid adenosine event. Pannexin1 channels are membrane channels that transport ions, including ATP, out of the cell where it is rapidly broken down into adenosine. Pannexin 1 channels (Panx1) have a flickering mode of rapid opening and are also mechanically stimulated. Here, we test the extent to which pannexin channels, specifically pannexin1 (Panx1) channels, are responsible for rapid adenosine events. Spontaneous adenosine release or mechanosensitive adenosine release were measured using fast-scan cyclic voltammetry in hippocampal (CA1) brain slices. In global Panx1KO mice, there is no significant difference in the frequency or concentration of spontaneous adenosine release, indicating Panx1 is not a release mechanism for spontaneous adenosine. Spontaneous adenosine frequency decreased slightly after administration of a large (100 µM) dose of carbenoxolone, a nonspecific inhibitor of many pannexin and connexin channels, suggesting other hemichannels only play a small role at most. For mechanically stimulated adenosine release, the concentration of each adenosine event significantly decreased 30% in Panx1KO mice and the frequency of stimulations that evoked adenosine also decreased. The response was similar in WT mice with carbenoxolone. Thus, Panx1 is a release mechanism for mechanically stimulated adenosine release, but not the only mechanism. These results demonstrate that pannexin channels differentially regulate rapid adenosine release and could be targeted to differentially affect mechanically stimulated adenosine due to brain damage.

Graphene-Fiber Microelectrodes for Ultrasensitive Neurochemical Detection

Here, we have synthesized and characterized graphene-fiber microelectrodes (GFME's) for subsecond detection of neurochemicals with fast-scan cyclic voltammetry (FSCV) for the first time. GFME's exhibited extraordinary properties including faster electron transfer kinetics, significantly improved sensitivity, and ease of tunability that we anticipate will have major impacts on neurochemical detection for years to come. GF's have been used in the literature for various applications; however, scaling their size down to microelectrodes and implementing them as neurochemical microsensors is significantly less developed. The GF's developed in this paper were on average 20-30 μm in diameter and both graphene oxide (GO) and reduced graphene oxide (rGO) fibers were characterized with FSCV. Neat GF's were synthesized using a one-step dimension-confined hydrothermal strategy. FSCV detection has traditionally used carbon-fiber microelectrodes (CFME's) and more recently carbon nanotube fiber electrodes; however, uniform functionalization and direct control of the 3D surface structure of these materials remain limited. The expansion to GFME's will certainly open new avenues for fine-tuning the electrode surface for specific electrochemical detection. When comparing to traditional CFME's, our GFME's exhibited significant increases in electron transfer, redox cycling, fouling resistance, higher sensitivity, and frequency independent behavior which demonstrates their incredible utility as biological sensors.

Atomistic Simulations of Dopamine Diffusion Dynamics on a Pristine Graphene Surface

Carbon microelectrodes enable in vivo detection of neurotransmitters, and new electrodes aim to optimize the carbon surface. However, atomistic detail on the diffusion and orientation of neurotransmitters near these surfaces is lacking. Here, we employ molecular dynamics simulations to investigate the surface diffusion of dopamine (DA), its oxidation product dopamine-o-quinone (DOQ), and their protonated forms on the pristine basal plane of flat graphene. We find that all DA species rapidly adsorb to the surface and remain adsorbed, even without a holding potential or graphene surface defects. We also find that the diffusivities of the adsorbed and the fully solvated DA are similar and that the protonated species diffuse more slowly on the surface than their corresponding neutral forms, while the oxidized species diffuse more rapidly. Structurally, we find that the underlying graphene lattice has little influence over the molecular adsorbate's lateral position, and the vertical placement of the amine group on dopamine is highly dependent upon its charge. Finally, we find that solvation has a large effect on surface diffusivities. These first results from molecular dynamics simulations of dopamine at the aqueous-graphene interface show that dopamine diffuses rapidly on the surface, even without an applied potential, and provide a basis for future simulations of neurotransmitter structure and dynamics on advanced carbon materials electrodes.

Dual-Channel Electrochemical Measurements Reveal Rapid Adenosine is Localized in Brain Slices

Rapid adenosine signaling has been detected spontaneously or after mechanical stimulation in the brain, providing rapid neuromodulation in a local area. To measure rapid adenosine signaling, a single carbon-fiber microelectrode has traditionally been used, which limits spatial resolution and an understanding of regional coordination. In this study, we utilized dual-channel fast-scan cyclic voltammetry to measure the spontaneous or mechanically stimulated adenosine release at two electrodes placed at different spacings in hippocampal CA1 mouse brain slices. For mechanically stimulated adenosine release, adenosine can be detected up to 150 μm away from where it was stimulated, although the signal is smaller and delayed. While spontaneous adenosine transients were detected at both electrodes, only 10 percent of the events were detected concurrently, and that number was similar at 50 and 200 μm electrode spacings. Thus, most adenosine transients were not caused by the widespread coordination of release. There was no evidence of diffusion of spontaneous transients to a second electrode 50-200 μm away. This study shows that spontaneous adenosine events are very localized and thus provide only local neuromodulation. Injury, such as mechanical stimulation, allows adenosine to diffuse farther, but the neuroprotective effects are still regional. These results provide a better understanding of the spatial and temporal profiles of adenosine available to act at receptors, which is crucial for future studies that design neuroprotective treatments based on rapid adenosine signaling.

In vitro electrochemical measurement of serotonin release in the human jejunum mucosa using a diamond microelectrode

We report herein on the use of a boron-doped diamond microelectrode (DME) to record oxidation currents in vitro associated with the release of serotonin from enterochromaffin cells in the epithelium of the human intestinal mucosa.

Enhanced Dopamine Sensitivity Using Steered Fast-Scan Cyclic Voltammetry

Fast-scan cyclic voltammetry (FSCV) is a technique for measuring phasic release of neurotransmitters with millisecond temporal resolution. The current data are captured by carbon fiber microelectrodes, and non-Faradaic current is subtracted from the background current to extract the Faradaic redox current through a background subtraction algorithm. FSCV is able to measure neurotransmitter concentrations in vivo down to the nanomolar scale, making it a very robust and useful technique for probing neurotransmitter release dynamics and communication across neural networks. In this study, we describe a technique that can further lower the limit of detection of FSCV. By taking advantage of a "waveform steering" technique and by amplifying only the oxidation peak of dopamine to reduce noise fluctuations, we demonstrate the ability to measure dopamine concentrations down to 0.17 nM. Waveform steering is a technique to dynamically alter the input waveform to ensure that the background current remains stable over time. Specifically, the region of the input waveform in the vicinity of the dopamine oxidation potential (∼0.6 V) is kept flat. Thus, amplification of the input waveform will amplify only the Faradaic current, lowering the existing limit of detection for dopamine from 5.48 to 0.17 nM, a 32-fold reduction, and for serotonin, it lowers the limit of detection from 57.3 to 1.46 nM, a 39-fold reduction compared to conventional FSCV. Finally, the applicability of steered FSCV to in vivo dopamine detection was also demonstrated in this study. In conclusion, steered FSCV might be used as a neurochemical monitoring tool for enhancing detection sensitivity.

Feasibility of Applying Fourier Transform Electrochemical Impedance Spectroscopy in Fast Cyclic Square Wave Voltammetry for the In Vivo Measurement of Neurotransmitters

We previously reported on the use of fast cyclic square wave voltammetry (FCSWV) as a new voltammetric technique. Fourier transform electrochemical impedance spectroscopy (FTEIS) has recently been utilized to provide information that enables a detailed analytical description of an electrified interface. In this study, we report on attempts to combine FTEIS with FCSWV (FTEIS-FCSWV) and demonstrate the feasibility of FTEIS-FCSWV in the in vivo detection of neurotransmitters, thus giving a new type of electrochemical impedance information such as biofouling on the electrode surface. From FTEIS-FCSWV, three new equivalent circuit element voltammograms, consisting of charge-transfer resistance (R_ct), solution-resistance (R_s), and double-layer capacitance (C_dl) voltammograms were constructed and investigated in the phasic changes in dopamine (DA) concentrations. As a result, all R_ct, R_s, and C_dl voltammograms showed different DA redox patterns and linear trends for the DA concentration (R² > 0.99). Furthermore, the R_ct voltammogram in FTEIS-FCSWV showed lower limit of detection (21.6 ± 15.8 nM) than FSCV (35.8 ± 17.4 nM). FTEIS-FCSWV also showed significantly lower prediction errors than FSCV in selectivity evaluations of unknown mixtures of catecholamines. Finally, C_dl from FTEIS-FCSWV showed a significant relationship with fouling effect on the electrode surface by showing decreased DA sensitivity in both flow injection analysis experiment (r = 0.986) and in vivo experiments. Overall, this study demonstrates the feasibility of FTEIS-FCSWV, which could offer a new type of neurochemical spectroscopic information concerning electrochemical monitoring of neurotransmitters in the brain, and the ability to estimate the degree of sensitivity loss caused by biofouling on the electrode surface.

Spontaneous Adenosine and Dopamine Cotransmission in the Caudate-Putamen Is Regulated by Adenosine Receptors

Transient changes in adenosine and dopamine have been measured in vivo, but no studies have examined if these transient changes occur simultaneously. In this study, we characterized spontaneous adenosine and dopamine transients in anesthetized mice, examining coincident release in the caudate-putamen for the first time. We found that in C57B mice, most of the dopamine transients (77%) were coincident with adenosine, but fewer adenosine transients (12%) were coincident with a dopamine transient. On average, the dopamine transient started 200 ms before its coincident adenosine transient, so they occurred concurrently. There was a positive correlation (r = 0.7292) of adenosine and dopamine concentrations during coincident release. ATP is quickly broken down to adenosine in the extracellular space, and the coincident events may be due to corelease, where dopaminergic vesicles are packaged with ATP, or cotransmission, where ATP is packaged in different vesicles released simultaneously with dopamine. The high frequency of adenosine transients compared to that of dopamine transients suggests that adenosine is also released from nondopaminergic vesicles. We investigated how A₁ and A_2A adenosine receptors regulate adenosine and dopamine transients using A₁ and A_2AKO mice. In A₁KO mice, the frequency of adenosine and dopamine transients increased, while in A_2AKO mice, the frequency of adenosine alone increased. Adenosine receptors modulate coincident transients and could be drug targets to modulate both dopamine and adenosine release. Many spontaneous dopamine transients have coincident adenosine release, and regulating adenosine and dopamine cotransmission could be important for designing treatments for dopamine diseases, such as Parkinson's or addiction.

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

Many voltammetry methods have been developed to monitor brain extracellular dopamine levels. Fewer approaches have been successful in detecting serotonin in vivo. No voltammetric techniques are currently available to monitor both neurotransmitters simultaneously across timescales, even though they play integrated roles in modulating behavior. We provide proof-of-concept for rapid pulse voltammetry coupled with partial least squares regression (RPV-PLSR), an approach adapted from multi-electrode systems (i.e., electronic tongues) used to identify multiple components in complex environments. We exploited small differences in analyte redox profiles to select pulse steps for RPV waveforms. Using an intentionally designed pulse strategy combined with custom instrumentation and analysis software, we monitored basal and stimulated levels of dopamine and serotonin. In addition to faradaic currents, capacitive currents were important factors in analyte identification arguing against background subtraction. Compared to fast-scan cyclic voltammetry-principal components regression (FSCV-PCR), RPV-PLSR better differentiated and quantified basal and stimulated dopamine and serotonin associated with striatal recording electrode position, optical stimulation frequency, and serotonin reuptake inhibition. The RPV-PLSR approach can be generalized to other electrochemically active neurotransmitters and provides a feedback pipeline for future optimization of multi-analyte, fit-for-purpose waveforms and machine learning approaches to data analysis.

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.

Biocompatible reference electrodes to enhance chronic electrochemical signal fidelity in vivo

In vivo electrochemistry is a vital tool of neuroscience that allows for the detection, identification, and quantification of neurotransmitters, their metabolites, and other important analytes. One important goal of in vivo electrochemistry is a better understanding of progressive neurological disorders (e.g., Parkinson's disease). A complete understanding of such disorders can only be achieved through a combination of acute (i.e., minutes to hours) and chronic (i.e., days or longer) experimentation. Chronic studies are more challenging because they require prolonged implantation of electrodes, which elicits an immune response, leading to glial encapsulation of the electrodes and altered electrode performance (i.e., biofouling). Biofouling leads to increased electrode impedance and reference electrode polarization, both of which diminish the selectivity and sensitivity of in vivo electrochemical measurements. The increased impedance factor has been successfully mitigated previously with the use of a counter electrode, but the challenge of reference electrode polarization remains. The commonly used Ag/AgCl reference electrode lacks the long-term potential stability in vivo required for chronic measurements. In addition, the cytotoxicity of Ag/AgCl adversely affects animal experimentation and prohibits implantation in humans, hindering translational research progress. Thus, a move toward biocompatible reference electrodes with superior chronic potential stability is necessary. Two qualifying materials, iridium oxide and boron-doped diamond, are introduced and discussed in terms of their electrochemical properties, biocompatibilities, fabrication methods, and applications. In vivo electrochemistry continues to advance toward more chronic experimentation in both animal models and humans, necessitating the utilization of biocompatible reference electrodes that should provide superior potential stability and allow for unprecedented chronic signal fidelity when used with a counter electrode for impedance mitigation.

Carbon-based neural electrodes: promises and challenges

Neural electrodes are primary functional elements of neuroelectronic devices designed to record neural activity based on electrochemical signals. These electrodes may also be utilized for electrically stimulating the neural cells, such that their response can be simultaneously recorded. In addition to being medically safe, the electrode material should be electrically conductive and electrochemically stable under harsh biological environments. Mechanical flexibility and conformability, resistance to crack formation and compatibility with common microfabrication techniques are equally desirable properties. Traditionally, (noble) metals have been the preferred for neural electrode applications due to their proven biosafety and a relatively high electrical conductivity. Carbon is a recent addition to this list, which is far superior in terms of its electrochemical stability and corrosion resistance. Carbon has also enabled 3D electrode fabrication as opposed to the thin-film based 2D structures. One of carbon's peculiar aspects is its availability in a wide range of allotropes with specialized properties that render it highly versatile. These variations, however, also make it difficult to understand carbon itself as a unique material, and thus, each allotrope is often regarded independently. Some carbon types have already shown promising results in bioelectronic medicine, while many others remain potential candidates. In this topical review, we first provide a broad overview of the neuroelectronic devices and the basic requirements of an electrode material. We subsequently discuss the carbon family of materials and their properties that are useful in neural applications. Examples of devices fabricated using bulk and nano carbon materials are reviewed and critically compared. We then summarize the challenges, future prospects and next-generation carbon technology that can be helpful in the field of neural sciences. The article aims at providing a common platform to neuroscientists, electrochemists, biologists, microsystems engineers and carbon scientists to enable active and comprehensive efforts directed towards carbon-based neuroelectronic device fabrication.

Novel, User-Friendly Experimental and Analysis Strategies for Fast Voltammetry: 1. The Analysis Kid for FSCV

Fast-scan cyclic voltammetry (FSCV) at carbon fiber microelectrodes measures low concentrations of analytes in biological systems. There are ongoing efforts to simplify FSCV analysis, and several custom platforms are available for filtering and multimodal analysis of FSCV signals, but there is no single, easily accessible platform that has the capacity for all of these features. Here we present The Analysis Kid: currently, the only free, open-source cloud application that does not require a specialized runtime environment and is easily accessible via common browsers. We show that a user-friendly interface can analyze multiplatform file formats to provide multimodal visualization of FSCV color plots with digital background subtraction. We highlight key features that allow interactive calibration and semiautomatic parametric analysis via peak finding algorithms to automatically detect the maximum amplitude, area under the curve, and clearance rate of the signal. Finally, The Analysis Kid enables semiautomatic fitting of data with Michaelis-Menten kinetics with single or dual reuptake models. The Analysis Kid can be freely accessed at http://analysis-kid.hashemilab.com/. The web application code is found, under an MIT license, at https://github.com/sermeor/The-Analysis-Kid.

Electrochemical treatment in KOH renews and activates carbon fiber microelectrode surfaces

Carbon fiber microelectrodes (CFMEs) are the standard electrodes for fast-scan cyclic voltammetry (FSCV) detection of neurotransmitters. CFMEs are generally used untreated but the surface can be activated with different treatments to improve electrochemical performance. In this work, we explored electrochemical treatments to clean and activate the CFME surface. We used different solution conditions for electrochemical treatment and found that electrochemical pretreatment in KOH outperforms treatment in KCl, H₂O₂, or HCl by accelerating the surface renewal process. The etching rate of carbon with electrochemical treatment in KOH is 37 nm/min, which is 10 times faster than that in the other solutions. Electrochemical treatment in KOH for several minutes regenerates a new carbon surface, which introduces more oxygen functional groups beneficial for adsorption and electron transfer. The KOH-treated CFMEs improved the limit of detection (LOD) to 9 ± 2 nM from 14 ± 4 nM for untreated CFMEs, and they successfully detected stimulated dopamine release in rat brain slices, demonstrating that they are stable and sensitive enough to use in biological systems. Electrochemical treatment in KOH completely restores the electrode sensitivity after biofouling. The proposed electrochemical treatment is simple and fast and can be applied prior to using CFMEs or after use to restore the surface. Thus, the method has potential to be a standard step to clean the carbon surface, or restore the sensitivity of electrodes from biofouling.

3D fuzzy graphene microelectrode array for dopamine sensing at sub-cellular spatial resolution

The development of a high sensitivity real-time sensor for multi-site detection of dopamine (DA) with high spatial and temporal resolution is of fundamental importance to study the complex spatial and temporal pattern of DA dynamics in the brain, thus improving the understanding and treatments of neurological and neuropsychiatric disorders. In response to this need, here we present high surface area out-of-plane grown three-dimensional (3D) fuzzy graphene (3DFG) microelectrode arrays (MEAs) for highly selective, sensitive, and stable DA electrochemical sensing. 3DFG microelectrodes present a remarkable sensitivity to DA (2.12 ± 0.05 nA/nM, with LOD of 364.44 ± 8.65 pM), the highest reported for nanocarbon MEAs using Fast Scan Cyclic Voltammetry (FSCV). The high surface area of 3DFG allows for miniaturization of electrode down to 2 × 2 μm², without compromising the electrochemical performance. Moreover, 3DFG MEAs are electrochemically stable under 7.2 million scans of continuous FSCV cycling, present exceptional selectivity over the most common interferents in vitro with minimum fouling by electrochemical byproducts and can discriminate DA and serotonin (5-HT) in response to the injection of their 50:50 mixture. These results highlight the potential of 3DFG MEAs as a promising platform for FSCV based multi-site detection of DA with high sensitivity, selectivity, and spatial resolution.

Unlocking the full potential of voltammetric data analysis: A novel peak recognition approach for (bio)analytical applications

Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.

A fast scan cyclic voltammetric digital circuit with precise ohmic drop compensation by online measuring solution resistance and its biosensing application

In this work, a novel fast scan digital circuit for voltammetric analysis with precious ohmic drop compensation is developed, which is achieved through online measuring solution resistance first and then proportionally feedbacking the output signal to potentiostat's in-phase input through a potentiometer. It mainly consists of a solution resistance measurement module based on AD5933 chip, an ohmic drop automatic compensation module and a STM32F103ZET6 microcontroller. The performance of the circuit is checked successively using pure resistances, RC dummy cells, RC dummy cells incorporating a pseudo-faradaic component, and the ferrocene redox system. Results show that, precise ohmic drop compensation can be realized online and automatically, affording fast scan cyclic voltammetric (FSCV) analysis for theoretical electrochemical cells at 2000 V/s and that for practical electrochemical system using conventional electrodes at 1600 V/s. Based on this circuit, a very simple DNA biosensor for ultrasensitive detection of mercuric ions was explored. Benefitting from the high sensitivity brought by the high scan rate, the limit of quantitation (LOQ) can reach 1 pmol/L, demonstrating the application potential of FSCV in the field of ultrasensitive electrochemical detection.

An FSCV Deep Neural Network: Development, Pruning, and Acceleration on an FPGA

Fast-scan cyclic voltammetry (FSCV) is an electrochemical technique for measuring rapid changes in the extracellular concentration of neurotransmitters within the brain. Due to its fast scan rate and large output-data size, the current analysis of the FSCV data is often conducted on a computer external to the FSCV device. Moreover, the analysis is semi-automated and requires a good understanding of the characteristics of the underlying chemistry to interpret, making it unsuitable for real-time implementation on low-resource FSCV devices. This paper presents a hardware-software co-design approach for the analysis of FSCV data. Firstly, a deep neural network (DNN) is developed to predict the concentration of a dopamine solution and identify the data recording electrode. Secondly, the DNN is pruned to decrease its computation complexity, and a custom overlay is developed to implement the pruned DNN on a low-resource FPGA-based platform. The pruned DNN attains a recognition accuracy of 97.2% with a compression ratio of 3.18. When the DNN overlay is implemented on a PYNQ-Z2 platform, it achieves the execution time of 13 ms and power consumption of 1.479 W on the entire PYNQ-Z2 board. This study demonstrates the possibility of operating the DNN for FSCV data analysis on portable FPGA-based platforms.

Electrochemical Biosensing of Dopamine Neurotransmitter: A Review

Neurotransmitters are biochemical molecules that transmit a signal from a neuron across the synapse to a target cell, thus being essential to the function of the central and peripheral nervous system. Dopamine is one of the most important catecholamine neurotransmitters since it is involved in many functions of the human central nervous system, including motor control, reward, or reinforcement. It is of utmost importance to quantify the amount of dopamine since abnormal levels can cause a variety of medical and behavioral problems. For instance, Parkinson's disease is partially caused by the death of dopamine-secreting neurons. To date, various methods have been developed to measure dopamine levels, and electrochemical biosensing seems to be the most viable due to its robustness, selectivity, sensitivity, and the possibility to achieve real-time measurements. Even if the electrochemical detection is not facile due to the presence of electroactive interfering species with similar redox potentials in real biological samples, numerous strategies have been employed to resolve this issue. The objective of this paper is to review the materials (metals and metal oxides, carbon materials, polymers) that are frequently used for the electrochemical biosensing of dopamine and point out their respective advantages and drawbacks. Different types of dopamine biosensors, including (micro)electrodes, biosensing platforms, or field-effect transistors, are also described.

Resource-constrained FPGA/DNN co-design

Deep neural networks (DNNs) have demonstrated super performance in most learning tasks. However, a DNN typically contains a large number of parameters and operations, requiring a high-end processing platform for high-speed execution. To address this challenge, hardware-and-software co-design strategies, which involve joint DNN optimization and hardware implementation, can be applied. These strategies reduce the parameters and operations of the DNN, and fit it into a low-resource processing platform. In this paper, a DNN model is used for the analysis of the data captured using an electrochemical method to determine the concentration of a neurotransmitter and the recoding electrode. Next, a DNN miniaturization algorithm is introduced, involving combined pruning and compression, to reduce the DNN resource utilization. Here, the DNN is transformed to have sparse parameters by pruning a percentage of its weights. The Lempel-Ziv-Welch algorithm is then applied to compress the sparse DNN. Next, a DNN overlay is developed, combining the decompression of the DNN parameters and DNN inference, to allow the execution of the DNN on a FPGA on the PYNQ-Z2 board. This approach helps avoid the need for inclusion of a complex quantization algorithm. It compresses the DNN by a factor of 6.18, leading to about 50% reduction in the resource utilization on the FPGA.

Improving serotonin fast-scan cyclic voltammetry detection: new waveforms to reduce electrode fouling

Serotonin is a neuromodulator implicated in depression that is often measured in real-time by fast-scan cyclic voltammetry (FSCV). A specialized "Jackson" waveform (JW, 0.2, 1.0 V, -0.1 V, 0.2 V, 1000 V s-1) was developed to reduce serotonin fouling, but the 1.0 V switching potential limits sensitivity and electrodes still foul. The goal of this study was to test the effects of extending the FSCV switching potential to increase serotonin sensitivity and decrease fouling. We compared the Jackson waveform, the dopamine waveform (DA, -0.4 V, 1.3 V, 400 V s-1), and two new waveforms: the extended serotonin waveform (ESW, 0.2, 1.3, -0.1, 0.2, 1000 V s-1) and extended hold serotonin waveform (EHSW, 0.2, 1.3 (hold 1 ms), -0.1, 0.2, 400 V s-1). The EHSW was the most sensitive (LOD = 0.6 nM), and the JW the least sensitive (LOD = 2.4 nM). With the Jackson waveform, electrode fouling was significant with repeated injections of serotonin or exposure to its metabolite, 5-hydroxyindoleacetic acid (5-HIAA). Using the extended waveforms, electrodes fouled 50% less than with the Jackson waveform for both analytes. No electrode fouling was observed with the dopamine waveform because of the negative holding potential. The Jackson waveform was the most selective for serotonin over dopamine (800×), and the ESW was also highly selective. All waveforms were useful for measuring serotonin with optogenetic stimulation in Drosophila larvae. These results provide new FSCV waveforms to measure dynamic serotonin changes with different experimental requirements, like high sensitivity (EHSW), high selectivity (ESW, JW), or eliminating electrode fouling (DA).

Review-Recent Advances in FSCV Detection of Neurochemicals via Waveform and Carbon Microelectrode Modification

Fast scan cyclic voltammetry (FSCV) is an analytical technique that was first developed over 30 years ago. Since then, it has been extensively used to detect dopamine using carbon fiber microelectrodes (CFMEs). More recently, electrode modifications and waveform refinement have enabled the detection of a wider variety of neurochemicals including nucleosides such as adenosine and guanosine, neurotransmitter metabolites of dopamine, and neuropeptides such as enkephalin. These alterations have facilitated the selectivity of certain biomolecules over others to enhance the measurement of the analyte of interest while excluding interferants. In this review, we detail these modifications and how specializing CFME sensors allows neuro-analytical researchers to develop tools to understand the neurochemistry of the brain in disease states and provide groundwork for translational work in clinical settings.

Aptamer Conformational Change Enables Serotonin Biosensing with Nanopipettes

We report artificial nanopores in the form of quartz nanopipettes with ca. 10 nm orifices functionalized with molecular recognition elements termed aptamers that reversibly recognize serotonin with high specificity and selectivity. Nanoscale confinement of ion fluxes, analyte-specific aptamer conformational changes, and related surface charge variations enable serotonin sensing. We demonstrate detection of physiologically relevant serotonin amounts in complex environments such as neurobasal media, in which neurons are cultured in vitro. In addition to sensing in physiologically relevant matrices with high sensitivity (picomolar detection limits), we interrogate the detection mechanism via complementary techniques such as quartz crystal microbalance with dissipation monitoring and electrochemical impedance spectroscopy. Moreover, we provide a novel theoretical model for structure-switching aptamer-modified nanopipette systems that supports experimental findings. Validation of specific and selective small-molecule detection, in parallel with mechanistic investigations, demonstrates the potential of conformationally changing aptamer-modified nanopipettes as rapid, label-free, and translatable nanotools for diverse biological systems.

Influence of Geometry on Thin Layer and Diffusion Processes at Carbon Electrodes

The geometric structure of carbon electrodes affects their electrochemical behavior, and large-scale surface roughness leads to thin layer electrochemistry when analyte is trapped in pores. However, the current response is always a mixture of both thin layer and diffusion processes. Here, we systematically explore the effects of thin layer electrochemistry and diffusion at carbon fiber (CF), carbon nanospike (CNS), and carbon nanotube yarn (CNTY) electrodes. The cyclic voltammetry (CV) response to the surface-insensitive redox couple Ru(NH₃)₆^3+/2+ is tested, so the geometric structure is the only factor. At CFs, the reaction is diffusion-controlled because the surface is smooth. CNTY electrodes have gaps between nanotubes that are about 10 μm deep, comparable with the diffusion layer thickness. CNTY electrodes show clear thin layer behavior due to trapping effects, with more symmetrical peaks and ΔE_p closer to zero. CNS electrodes have submicrometer scale roughness, so their CV shape is mostly due to diffusion, not thin layer effects. However, even the 10% contribution of thin layer behavior reduces the peak separation by 30 mV, indicating ΔE_p is influenced not only by electron transfer kinetics but also by surface geometry. A new simulation model is developed to quantitate the thin layer and diffusion contributions that explains the CV shape and peak separation for CNS and CNTY electrodes, providing insight on the impact of scan rate and surface structure size. Thus, this study provides key understanding of thin layer and diffusion processes at different surface structures and will enable rational design of electrodes with thin layer electrochemistry.

A fast, high-affinity fluorescent serotonin biosensor engineered from a tick lipocalin

Serotonin (5-HT) is an important signaling monoamine and neurotransmitter. We report structure-guided engineering of a green fluorescent, genetically encoded serotonin sensor (G-GESS) from a 5-HT-binding lipocalin in the soft tick Argas monolakensis. G-GESS shows fast response kinetics and high affinity, specificity, brightness and photostability. We used G-GESS to image 5-HT dynamics in cultured cells, brain slices and behaving mice.

Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry. One significant advantage this technique offers in terms of data analysis is that Fourier transformation provides access to the higher order harmonics that are almost devoid of background current. Perspectives on the technical advances needed to develop intelligent data analysis strategies and make them generally available to users of voltammetry are provided.

Next-Generation Diamond Electrodes for Neurochemical Sensing: Challenges and Opportunities

Carbon-based electrodes combined with fast-scan cyclic voltammetry (FSCV) enable neurochemical sensing with high spatiotemporal resolution and sensitivity. While their attractive electrochemical and conductive properties have established a long history of use in the detection of neurotransmitters both in vitro and in vivo, carbon fiber microelectrodes (CFMEs) also have limitations in their fabrication, flexibility, and chronic stability. Diamond is a form of carbon with a more rigid bonding structure (sp3-hybridized) which can become conductive when boron-doped. Boron-doped diamond (BDD) is characterized by an extremely wide potential window, low background current, and good biocompatibility. Additionally, methods for processing and patterning diamond allow for high-throughput batch fabrication and customization of electrode arrays with unique architectures. While tradeoffs in sensitivity can undermine the advantages of BDD as a neurochemical sensor, there are numerous untapped opportunities to further improve performance, including anodic pretreatment, or optimization of the FSCV waveform, instrumentation, sp2/sp3 character, doping, surface characteristics, and signal processing. Here, we review the state-of-the-art in diamond electrodes for neurochemical sensing and discuss potential opportunities for future advancements of the technology. We highlight our team's progress with the development of an all-diamond fiber ultramicroelectrode as a novel approach to advance the performance and applications of diamond-based neurochemical sensors.

Distinct roles for dopamine clearance mechanisms in regulating behavioral flexibility

Dopamine plays a crucial role in adaptive behavior, and dysfunctional dopamine is implicated in multiple psychiatric conditions characterized by inflexible or inconsistent choices. However, the precise relationship between dopamine and flexible decision making remains unclear. One reason is that, while many studies have focused on the activity of dopamine neurons, efficient dopamine signaling also relies on clearance mechanisms, notably the dopamine transporter (DAT), which predominates in striatum, and catechol-O-methyltransferase (COMT), which predominates in cortex. The exact locus, extent, and timescale of the effects of DAT and COMT are uncertain. Moreover, there is limited data on how acute disruption of either mechanism affects flexible decision making strategies mediated by cortico-striatal networks. To address these issues, we combined pharmacological modulation of DAT and COMT with electrochemistry and behavior in mice. DAT blockade, but not COMT inhibition, regulated sub-second dopamine release in the nucleus accumbens core, but surprisingly neither clearance mechanism affected evoked release in prelimbic cortex. This was not due to a lack of sensitivity, as both amphetamine and atomoxetine changed the kinetics of sub-second release. In a multi-step decision making task where mice had to respond to reversals in either reward probabilities or the choice sequence to reach the goal, DAT blockade selectively impaired, and COMT inhibition improved, performance after reward reversals, but neither manipulation affected the adaptation of choices after action-state transition reversals. Together, our data suggest that DAT and COMT shape specific aspects of behavioral flexibility by regulating different aspects of the kinetics of striatal and cortical dopamine, respectively.

Imaging Neurotransmitter and Neuromodulator Dynamics In Vivo with Genetically Encoded Indicators

The actions of neuromodulation are thought to mediate the ability of the mammalian brain to dynamically adjust its functional state in response to changes in the environment. Altered neurotransmitter (NT) and neuromodulator (NM) signaling is central to the pathogenesis or treatment of many human neurological and psychiatric disorders, including Parkinson's disease, schizophrenia, depression, and addiction. To reveal the precise mechanisms by which these neurochemicals regulate healthy and diseased neural circuitry, one needs to measure their spatiotemporal dynamics in the living brain with great precision. Here, we discuss recent development, optimization, and applications of optical approaches to measure the spatial and temporal profiles of NT and NM release in the brain using genetically encoded sensors for in vivo studies.

Real-Time Fast Scan Cyclic Voltammetry Detection and Quantification of Exogenously Administered Melatonin in Mice Brain

Melatonin (MT) has been recently considered an excellent candidate for the treatment of sleep disorders, neural injuries, and neurological diseases. To better investigate the actions of MT in various brain functions, real-time detection of MT concentrations in specific brain regions is much desired. Previously, we have demonstrated detection of exogenously administered MT in anesthetized mouse brain using square wave voltammetry (SWV). Here, for the first time, we show successful detection of exogenous MT in the brain using fast scan cyclic voltammetry (FSCV) on electrochemically pre-activated carbon fiber microelectrodes (CFEs). In vitro evaluation showed the highest sensitivity (28.1 nA/μM) and lowest detection limit (20.2 ± 4.8 nM) ever reported for MT detection at carbon surface. Additionally, an extensive CFE stability and fouling assessment demonstrated that a prolonged CFE pre-conditioning stabilizes the background, in vitro and in vivo, and provides consistent CFE sensitivity over time even in the presence of a high MT concentration. Finally, the stable in vivo background, with minimized CFE fouling, allows us to achieve a drift-free FSCV detection of exogenous administered MT in mouse brain over a period of 3 min, which is significantly longer than the duration limit (usually < 90 s) for traditional in vivo FSCV acquisition. The MT concentration and dynamics measured by FSCV are in good agreement with SWV, while microdialysis further validated the concentration range. These results demonstrated reliable MT detection using FSCV that has the potential to monitor MT in the brain over long periods of time.

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.

Real-Time Measurement of Stimulated Dopamine Release in Compartments of the Adult Drosophila melanogaster Mushroom Body

Drosophila melanogaster, a fruit fly, is an exquisite model organism to understand neurotransmission. Dopaminergic signaling in the Drosophila mushroom body (MB) is involved in olfactory learning and memory, with different compartments controlling aversive learning (heel) vs. appetitive learning (medial tip). Here, the goal was to develop techniques to measure endogenous dopamine in compartments of the MB for the first time. We compared three stimulation methods: acetylcholine (natural stimulus), P2X₂ (chemogenetics), and CsChrimson (optogenetics). Evoked dopamine release was measured with fast-scan cyclic voltammetry in isolated adult Drosophila brains. Acetylcholine stimulated the largest dopamine release (0.40 μM) followed by P2X₂ (0.14 μM) and CsChrimson (0.07 μM). With the larger acetylcholine and P2X₂ stimulations, there were no regional or sex differences in dopamine release. However, with CsChrimson, dopamine release was significantly higher in the heel than the medial tip, and females had more dopamine than males. Michaelis-Menten modeling of the single-light pulse revealed no significant regional differences in K_m, but the heel had a significantly lower V_max (0.12 μM/s vs. 0.19 μM/s) and higher dopamine release (0.05 μM vs. 0.03 μM). Optogenetic experiments are challenging because CsChrimson is also sensitive to blue light used to activate green fluorescent protein, and thus, light exposure during brain dissection must be minimized. These experiments expand the toolkit for measuring endogenous dopamine release in Drosophila, introducing chemogenetic and optogenetic experiments for the first time. With a variety of stimulations, different experiments will help improve our understanding of neurochemical signaling in Drosophila.

Evaluation of electrochemical methods for tonic dopamine detection in vivo

Dysfunction in dopaminergic neuronal systems underlie a number of neurologic and psychiatric disorders such as Parkinson's disease, drug addiction, and schizophrenia. Dopamine systems communicate via two mechanisms, a fast "phasic" release (sub-second to second) that is related to salient stimuli and a slower "tonic" release (minutes to hours) that regulates receptor tone. Alterations in tonic levels are thought to be more critically important in enabling normal motor, cognitive, and motivational functions, and dysregulation in tonic dopamine levels are associated with neuropsychiatric disorders. Therefore, development of neurochemical recording techniques that enable rapid, selective, and quantitative measurements of changes in tonic extracellular levels are essential in determining the role of dopamine in both normal and disease states. Here, we review state-of-the-art advanced analytical techniques for in vivo detection of tonic levels, with special focus on electrochemical techniques for detection in humans.

Phthalocyanine Modified Electrodes in Electrochemical Analysis

Phthalocyanines are aromatic or macrocyclic organic compounds and attract great attention due to their numerous properties. They have many high-tech applications in different areas of the industry such as dyestuffs, thermal printing screens, photovoltaic solar cells, membrane catalytic reactors, semiconductor materials and gas sensors. In the last decade, electrochemical sensor studies have accelerated with the catalytic lighting. It plays a dominant role in the development and implementation of new generation sensors. The aim of this study is to review the electrochemical methods based on electrode modification with phthalocyanines and to shed light on new application areas of phthalocyanines. The focal point was based on the sensor applications of phthalocyanines in the determination of drugs, pesticides, organic materials and metals etc. by electrochemical methods. Experimental conditions and some validation parameters of the sensor applications such as metal phthalocyanine types, indicator electrodes, selectivity, working ranges, detection limits, and analytical applications were discussed. Consequently, this is the first review dealing with the applications of phthalocyanines in electrochemical sensors for the sensitive determination of analytes in a variety of matrices.

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.

Galvanic Redox Potentiometry Based Microelectrode Array for Synchronous Ascorbate and Single-Unit Recordings in Rat Brain

Neuronal communication relies on cooperation between the chemical and electrical patterns of neurons. Thus, techniques for illustrating the linkage of the neurochemical events and action potentials with high temporal and spatial resolution is imperative to gain a comprehensive understanding of the intricacies of brain function. Herein, we integrate galvanic redox potentiometry (GRP) and electrophysiological recording onto a 16-site Au microelectrode array (MEA), one of which is for indicating the ascorbate concentration while the others for single-unit activity assessment. The electrochemical probing site was modified with single-walled carbon nanotubes to promote electron-transfer kinetics of ascorbate at low overpotential so as to enlarge the driving force for the spontaneous ascorbate/O₂ cell reaction. The resulting GRP-based MEA outputs open-circuit potential that is in a linear relationship with the logarithmic ascorbate concentration and exhibits high selectivity against a set of coexisting electroactive species. Furthermore, no reciprocal interference between the two recording systems is observed during concurrent GRP sensing of ascorbate and single-unit recording in a rat brain. In vivo feasibility of the GRP-based MEA is demonstrated by synchronous real-time measurement of ascorbate release and electrical activity from multiple neuronal populations during spreading depression. Our GRP-based MEA sensor creates new opportunities to realize high-throughput screening or mapping of neurochemical patterns in a larger dimension and correlate them to neuron functions across a spatial scale.

Optimization of graphene oxide-modified carbon-fiber microelectrode for dopamine detection

Graphene oxide (GO) is a carbon-based material that is easily obtained from graphite or graphite oxide. GO has been used broadly for electrochemistry applications and our hypothesis is that GO coating a carbon-fiber microelectrode (CFME) will increase the sensitivity for dopamine by providing more adsorption sites due to the enhancement of oxygen functional groups. Here, we compared drop casting, dip coating, and electrodeposition methods to directly coat commercial GO on CFME surfaces. Dip coating did not result in much GO coating and drop casting resulted in large agglomerations that produced noisy signals and slow rise times. Electrodeposition method with cyclic voltammetry increase the current for dopamine and this method was the most reproducible and had the least noise compared to the other two coating methods. The optimized method used a triangular waveform scanned from -1.2 V to 1.5 V at 100 mV/s for 5 cycles in 0.2 mg/mL GO in water. With fast-scan cyclic voltammetry (FSCV), the optimized GO/CFME enhanced the dopamine oxidation peak two-fold. The sensitivity of the modified electrode is 41±2 nA/μM with a linear range from 25 nM to 1 μM, and a limit of detection of 11 nM. The optimized electrodes were used to detect electrically-stimulated dopamine in brain slices to demonstrate their performance in tissue. Thus, GO can be used to enhance the sensitivity of electrodes for dopamine and improve biological measurements.

CD73 or CD39 Deletion Reveals Different Mechanisms of Formation for Spontaneous and Mechanically Stimulated Adenosine and Sex Specific Compensations in ATP Degradation

Adenosine is important for local neuromodulation, and rapid adenosine signaling can occur spontaneously or after mechanical stimulation, but little is known about how adenosine is formed in the extracellular space for those stimulations. Here, we studied mechanically stimulated and spontaneous adenosine to determine if rapid adenosine is formed by extracellular breakdown of adenosine triphosphate (ATP) using mice globally deficient in extracellular breakdown enzymes, either CD39 (nucleoside triphosphate diphosphohydrolase 1, NTPDase1) or CD73 (ecto-5'-nucleotidase). CD39 knockout (KO) mice have a lower frequency of spontaneous adenosine events than wild-type (WT, C57BL/6). Surprisingly, CD73KO mice demonstrate sex differences in spontaneous adenosine; males maintain similar event frequencies as WT, but females have significantly fewer events and lower concentrations. Examining the mRNA expression of other enzymes that metabolize ATP revealed tissue nonspecific alkaline phosphatase (TNAP) was upregulated in male CD73KO mice, but not secreted prostatic acid phosphatase (PAP) or transmembrane PAP. Thus, TNAP upregulation compensates for CD73 loss in males but not in females. These sex differences highlight that spontaneous adenosine is formed by metabolism of extracellular ATP by many enzymes. For mechanically stimulated adenosine, CD39KO or CD73KO did not change stimulation frequency, concentration, or t_1/2. Thus, the mechanism of formation for mechanically stimulated adenosine is likely direct release of adenosine, different than spontaneous adenosine. Understanding these different mechanisms of rapid adenosine formation will help to develop pharmacological treatments that differentially target modes of rapid adenosine signaling, and all treatments should be studied in both sexes, given possible differences in extracellular ATP degradation.

High Young's modulus carbon fibers are fouling resistant with fast-scan cyclic voltammetry

High modulus carbon-fibers exhibit similar resistance to chemical fouling as carbon nanotube based materials with fast-scan cyclic voltammetry.