Micromachines (Feb 2024)

Batch Fabrication of Microelectrode Arrays with Glassy Carbon Microelectrodes and Interconnections for Neurochemical Sensing: Promises and Challenges

  • Emma-Bernadette A. Faul,
  • Austin M. Broussard,
  • Daniel R. Rivera,
  • May Yoon Pwint,
  • Bingchen Wu,
  • Qun Cao,
  • Davis Bailey,
  • X. Tracy Cui,
  • Elisa Castagnola

DOI
https://doi.org/10.3390/mi15020277
Journal volume & issue
Vol. 15, no. 2
p. 277

Abstract

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Flexible multielectrode arrays with glassy carbon (GC) electrodes and metal interconnection (hybrid MEAs) have shown promising performance in multi-channel neurochemical sensing. A primary challenge faced by hybrid MEAs fabrication is the adhesion of the metal traces with the GC electrodes, as prolonged electrical and mechanical stimulation can lead to adhesion failure. Previous devices with GC electrodes and interconnects made of a homogeneous material (all GC) demonstrated exceptional electrochemical stability but required miniaturization for enhanced tissue integration and chronic electrochemical sensing. In this study, we used two different methods for the fabrication of all GC-MEAs on thin flexible substrates with miniaturized features. The first method, like that previously reported, involves a double pattern-transfer photolithographic process, including transfer-bonding on temporary polymeric support. The second method requires a double-etching process, which uses a 2 µm-thick low stress silicon nitride coating of the Si wafer as the bottom insulator layer for the MEAs, bypassing the pattern-transfer and demonstrating a novel technique with potential advantages. We confirmed the feasibility of the two fabrication processes by verifying the practical conductivity of 3 µm-wide 2 µm-thick GC traces, the GC microelectrode functionality, and their sensing capability for the detection of serotonin using fast scan cyclic voltammetry. Through the exchange and discussion of insights regarding the strengths and limitations of these microfabrication methods, our goal is to propel the advancement of GC-based MEAs for the next generation of neural interface devices.

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