Lizhu Li, Yuxiao Zhang, Yunfei Gao, Yuqi Wang, Shirong Wang, Xue Gao, Ke Chen, Lan Yin, Xing Sheng, Dezhong Yao
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引用次数: 0
Abstract
Precise control of light-induced electrical signals at the biotic-abiotic interface remains a central challenge in advancing next-generation bioelectronic systems. In particular, achieving bidirectional signal modulation is essential for effective neural interface applications. Here, a spatially resolved, bidirectional photoelectric response at the silicon (Si) membrane-solution interface, induced by laser illumination is presented. Notably, a clear reversal in signal polarity between the illuminated regions (bright zones) and adjacent non-illuminated areas (dark zones) is observed. This signal orientation can be dynamically tuned by adjusting the light spot position and tailoring interfacial properties. To understand the underlying mechanism, the author systematically examined how various experimental parameters influence photoelectric behavior. These include the choice of adhesive, substrate conductivity (conductive vs insulating), boundary conditions (fixed vs free edges), and membrane geometry (e.g., grids and rectangles). These results reveal a cooperative effect between intrinsic charge conservation in the Si membrane and capacitive coupling at the interface. Moreover, in vivo studies show that integrating a conductive substrate beneath the Si membrane significantly enhances the modulation of sciatic nerve activity. Together, these findings define a new framework for light-responsive bioelectronic interfaces and point toward their broad utility in bioelectronic and neuromodulation applications.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.