微电极
材料科学
生物相容性
佩多:嘘
多电极阵列
纳米技术
聚吡咯
导电聚合物
介电谱
聚合
聚合物
电极
掺杂剂
电化学
光电子学
化学
兴奋剂
复合材料
冶金
物理化学
作者
Mahdi Ghazal,Anna Susloparova,Camille Lefebvre,Michel Daher Mansour,Najami Ghodhbane,Alexis Melot,Corentin Scholaert,David Guérin,Sébastien Janel,Nicolas Barois,Morvane Colin,Luc Buée,Pierre Yger,Sophie Halliez,Yannick Coffinier,Sébastien Pecqueur,Fabien Alibart
标识
DOI:10.1016/j.bios.2023.115538
摘要
Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. Recently, PEDOT:PSS has been used to coat microelectrodes due to its good biocompatibility and low impedance, which enhances neural coupling. Herein, we investigate on electro-co-polymerization of EDOT with its triglymated derivative to control valence between monomer units and hydrophilic functions on a conducting polymer. Molecular packing, cation complexation, dopant stoichiometry are governed by the glycolation degree of the electro-active coating of the microelectrodes. Optimal monomer ratio allows fine-tuning the material hydrophilicity and biocompatibility without compromising the electrochemical impedance of microelectrodes nor their stability while interfaced with a neural cell culture. After incubation, sensing readout on the modified electrodes shows higher performances with respect to unmodified electropolymerized PEDOT, with higher signal-to-noise ratio (SNR) and higher spike counts on the same neural culture. Reported SNR values are superior to that of state-of-the-art PEDOT microelectrodes and close to that of state-of-the-art 3D microelectrodes, with a reduced fabrication complexity. Thanks to this versatile technique and its impact on the surface chemistry of the microelectrode, we show that electro-co-polymerization trades with many-compound properties to easily gather them into single macromolecular structures. Applied on sensor arrays, it holds great potential for the customization of neurosensors to adapt to environmental boundaries and to optimize extracted sensing features.
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