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Gold Nanoparticle Modified Carbon Fiber Microelectrodes for Enhanced Neurochemical Detection
Published on: May 13, 2019
Advancing glassy carbon microelectrode arrays for neurochemical sensing: impact of double pyrolysis on structure and
Sarah Catherine Sellen1, Umisha Siwakoti2, Ashok Sigdel2
1Department of Electrical Engineering, Louisiana Tech University, Ruston, LA, United States.
Abstract:
Advancing neural interfaces requires implantable devices capable of long-term electrical and chemical monitoring. "All"-glassy carbon (GC) microelectrode arrays (MEAs), in which both electrodes and interconnects are formed from homogeneous GC layer, offer integrated chemical sensing and electrophysiological recording, while enhancing electrochemical durability by eliminating metal components. To guide the development of high-resolution, double-layer "all"-GC MEAs for higher-density architectures, this study systematically investigates GC as both an interconnect and neurochemical sensing material, with particular focus on the effects of double pyrolysis on structural integrity, interconnect resistance, and microelectrode performance. Sheet resistance was analyzed across films of varying thicknesses, and interconnect geometry was evaluated. Raman spectroscopy and X-ray diffraction characterized graphitization and crystallinity, while fast-scan cyclic voltammetry (FSCV) assessed dopamine and serotonin detection. A 48% reduction in the thickness of once-pyrolyzed GC corresponds to a 63% increase in its sheet resistance. A double pyrolyzed GC trace has about 50% higher sheet resistance than a single-pyrolyzed GC trace of the same thickness. Double pyrolysis caused approximately 20% shrinkage in the GC layer. Compared to Cr/Au/Pt traces, GC interconnects had higher resistance at 1-3 µm widths but approached metal-like performance at 5-10 µm. Importantly, the second pyrolysis cycle preserved structural integrity and FSCV sensitivity. These analyses advance our understanding of GC's electrical and sensing properties, providing critical insights for optimizing compact multilayer devices in next-generation "all"-GC-MEAs.

