Wet-Spun Porous Carbon Microfibers for Enhanced Electrochemical Detection

Surface-Roughened Graphene Oxide Microfibers Enhance Electrochemical Reversibility

Here, we provide an optimized method for fabricating surface-roughened graphene oxide disk microelectrodes (GFMEs) with enhanced defect density to generate a more suitable electrode surface for dopamine detection with fast-scan cyclic voltammetry (FSCV). FSCV detection, which is often influenced by adsorption-based surface interactions, is commonly impacted by the chemical and geometric structure of the electrode's surface, and graphene oxide is a tunable carbon-based nanomaterial capable of enhancing these two key characteristics. Synthesized GFMEs possess exquisite electronic and mechanical properties. We have optimized an applied inert argon (Ar) plasma treatment to increase defect density, with minimal changes in chemical functionality, for enhanced surface crevices to momentarily trap dopamine during detection. Optimal Ar plasma treatment (100 sccm, 60 s, 100 W) generates crevice depths of 33.4 ± 2.3 nm with high edge plane character enhancing dopamine interfacial interactions. Increases in GFME surface roughness improve electron transfer rates and limit diffusional rates out of the crevices to create nearly reversible dopamine electrochemical redox interactions. The utility of surface-roughened disk GFMEs provides comparable detection sensitivities to traditional cylindrical carbon fiber microelectrodes while improving temporal resolution ten-fold with amplified oxidation current due to dopamine cyclization. Overall, surface-roughened GFMEs enable improved adsorption interactions, momentary trapping, and current amplification, expanding the utility of GO microelectrodes for FSCV detection.

Waste Coffee Ground-Derived Porous Carbon for Neurochemical Detection

We present an optimized synthetic method for repurposing coffee waste to create controllable, uniform porous carbon frameworks for biosensor applications to enhance neurotransmitter detection with fast-scan cyclic voltammetry. Harnessing porous carbon structures from biowastes is a common practice for low-cost energy storage applications; however, repurposing biowastes for biosensing applications has not been explored. Waste coffee ground-derived porous carbon was synthesized by chemical activation to form multivoid, hierarchical porous carbon, and this synthesis was specifically optimized for porous uniformity and electrochemical detection. These materials, when modified on carbon-fiber microelectrodes, exhibited high surface roughness and pore distribution, which contributed to significant improvements in electrochemical reversibility and oxidative current for dopamine (3.5 ± 0.4-fold) and other neurochemicals. Capacitive current increases were small, showing evidence of small increases in electroactive surface area. Local trapping of dopamine within the pores led to improved electrochemical reversibility and frequency-independent behavior. Overall, we demonstrate an optimized biowaste-derived porous carbon synthesis for neurotransmitter detection for the first time and show material utility for viable neurotransmitter detection within a tissue matrix. This work supports the notion that controlled surface nanogeometries play a key role in electrochemical detection.

Editors' Choice-Review-The Future of Carbon-Based Neurochemical Sensing: A Critical Perspective

Carbon-based sensors have remained critical materials for electrochemical detection of neurochemicals, rooted in their inherent biocompatibility and broad potential window. Real-time monitoring using fast-scan cyclic voltammetry has resulted in the rise of minimally invasive carbon fiber microelectrodes as the material of choice for making measurements in tissue, but challenges with carbon fiber's innate properties have limited its applicability to understudied neurochemicals. Here, we provide a critical review of the state of carbon-based real-time neurochemical detection and offer insight into ways we envision addressing these limitations in the future. This piece focuses on three main hinderances of traditional carbon fiber based materials: diminished temporal resolution due to geometric properties and adsorption/desorption properties of the material, poor selectivity/specificity to most neurochemicals, and the inability to tune amorphous carbon surfaces for specific interfacial interactions. Routes to addressing these challenges could lie in methods like computational modeling of single-molecule interfacial interactions, expansion to tunable carbon-based materials, and novel approaches to synthesizing these materials. We hope this critical piece does justice to describing the novel carbon-based materials that have preceded this work, and we hope this review provides useful solutions to innovate carbon-based material development in the future for individualized neurochemical structures.