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.

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.

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 a 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 nanotubes (CNTs) 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 the 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 the chemical and surface structure affects the 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.