Abstract

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.

Highlights

  • While carbon fiber microelectrodes (CFMEs) often rely on manual assembly techniques that can be cumbersome and low-throughput, Boron-doped diamond (BDD) electrodes can be patterned in a variety of architectures and batch-fabricated

  • fast-scan cyclic voltammetry (FSCV) measurement of neurotransmitters is achieved by assessing peaks peaks in Faradaic that result from electroactive species

  • There are several challenges related to in vivo signal processing which have been established for carbon-based electrodes, and these challenges likewise will need to be considered for FSCV using diamond electrodes

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Summary

Introduction to Carbon-Based Sensors for Neurochemical Sensing

Disruption of chemical or electrical signaling in the brain underlies neurological disorders such as addiction [1,2,3], Alzheimer’s disease [4,5,6], amyotrophic lateral sclerosis [7,8,9,10,11], chronic pain [12,13,14,15], depression [16,17,18], Huntington’s disease [19,20,21], Parkinson’s disease [22,23,24], and schizophrenia [25,26,27]. CFMEs are brittle and broken during insertion into the brain Likewise, their long-term stability is compromised by dissolution of the carbon fiber electrode material that can result in significant degradation and loss of sensitivity over time. By using the BDD growth process, electrodes can be further tailored to enhance specific electrochemical properties Such modifications include adjusting the structure–function relationship of the material to enhance conductivity, decrease capacitance, and increase chemical functionalization for selectivity and sensitivity. Recent advances in electrode array technologies for voltammetric measurements include multi-barrel glass capillary arrays [115,116], patterned arrays on silicon wafers [117,118], or parylene-C insulated multichannel carbon fiber electrode arrays [85,119] While these arrays are powerful, most rely on hand fabrication processes under a microscope and are cumbersome, and slow. Open opportunities remain to improve the design and performance of carbon-based neurochemical sensors, including the development and optimization of diamond-based electrodes

Motivation for Diamond Sensors for Neurochemical Detection
Batch Fabrication and Customization
Wide Potential Window
Selectivity
Sensitivity
Detection Method
Biocompatibility
Flexibility
Opportunities for Optimization
Instrumentation
Waveform
Surface Characteristics
Methods for Improved Selectivity
Stability
Diamond Processing and Boron Doping
Reference Electrodes
Signal Processing
Findings
Concluding Remarks

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