Geometrical‐ and Substrate‐Dependent Photo Response of Thin‐Film Silicon‐Based Biointerfaces

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.

Authors

Institutions

Publication Details

Journal
Small Methods
Published
2025-10-15
DOI
https://doi.org/10.1002/smtd.202501228
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Geometrical‐ and Substrate‐Dependent Photo Response of Thin‐Film Silicon‐Based Biointerfaces

Lizhu Li, Xing Sheng, Dezhong Yao, Yu‐Xiao Zhang et al.
Small Methods
Neuroscience and Neural Engineering
article

Geometrical‐ and Substrate‐Dependent Photo Response of Thin‐Film Silicon‐Based Biointerfaces

Lizhu Li, Xing Sheng, Dezhong Yao, Yu‐Xiao Zhang, Lan Yin, Yunfei Gao, Xue Gao, Yuqi Wang, Ke Chen, Shirong Wang
article en

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 MethodsVol. 10(3)
University of Electronic Science and Technology of China (CN), Chinese Institute for Brain Research (CN), China Meat Research Centre (CN), State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University (CN)
Openalex Percentile: Top 68%
Neuroscience and Neural Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.