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Fast Reconfigurable Electrode Array Based on Titanium Oxide for Localized Stimulation of Cultured Neural Network

Title
Fast Reconfigurable Electrode Array Based on Titanium Oxide for Localized Stimulation of Cultured Neural Network
Authors
Xu J.Shirinkami H.Hwang S.Jeong H.S.Kim G.Jun S.B.Chun H.
Ewha Authors
전상범
SCOPUS Author ID
전상범scopus
Issue Date
2023
Journal Title
ACS Applied Materials and Interfaces
ISSN
1944-8244JCR Link
Citation
ACS Applied Materials and Interfaces vol. 15, no. 15, pp. 19092 - 19101
Keywords
Light-addressable electrodeneural interfacephotoconductivityreconfigurable electrode arrayTiO<sub>2</sub>film
Publisher
American Chemical Society
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Planar microelectrode arrays have become standard tools for in vitro neural-network analysis. However, these predefined micropatterned devices lack adaptability to target-specific cells within a cultured network. Herein, we fabricated a reconfigurable TiO2 electrode array with an anatase-brookite bicrystalline polymorphous mesoporous layer. Because of its selective absorption of ultraviolet (UV) light and corresponding photoconductivity, TiO2 electrode array was identified as a promising tool for high-resolution light-addressing. The TiO2 film was used as a semitransparent semiconductor with a high Roff/Ron ratio of 105 and a fast response time of 400 ms. In addition, the effect of UV radiation on the resistance of the TiO2 film over 30 d in an aqueous environment was analyzed, with the film exhibiting high stability. An arbitrary UV pattern was applied to a reconfigurable TiO2 electrode using a digital micromirror device (DMD), affording highly localized neural stimulation at the single-cell level. The reconfigurable TiO2 electrode with a patterned indium tin oxide (ITO) substrate enabled the independent connection of up to 60 points with external stimulators and signal recorders. We believe this technique would be helpful for electrophysiological research requiring the analysis of cell and neural-network features using a highly localized neural interface. © 2023 American Chemical Society
DOI
10.1021/acsami.2c21649
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공과대학 > 전자전기공학전공 > Journal papers
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