Reticular Photoelectrochemical Transistor with Biochemical Metaplasticity

Q Qing‐Qing Wu (State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China) Z Zheng Li M Miao‐Hua Chen (State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 P. R. China) C Cheng Yuan (State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry) Y Yuan‐Cheng Zhu (State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 P. R. China) J Jing‐Juan Xu (State Key Laboratory of Analytical Chemistry For Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing China) W Wei‐Wei Zhao (State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry Nanjing University Nanjing China)

Abstract

Abstract Close imitation of synaptic metaplasticity is an important objective in the neuromorphic domain. Progress has been made in solid‐state electronics with high‐voltage dynamics, which, nevertheless, marks a significant inconsistency with the biological systems in aqueous media. Here, the concept of reticular photoelectrochemical transistor (RPECT) is proposed and devised that can realize metaplasticity with biochemical modulation. Based on the ambipolar behavior of a metal–organic framework channel gated by a photosensitive hydrogen‐bonded organic framework electrode, biochemically modulated positive/negative photoconductivity and metaplasticity with the typical features, e.g., the nonmonotonic enhanced depression effect region and the threshold sliding are achieved. Taking advantage of such unique properties, in‐sensor preprocessing and in‐memory computing are further implemented for efficient image recognition. This work realizes the aqueous metaplasticity by a new device of RPECT, which also introduces the biochemical modulation into image recognition, providing a perspective for future development of machine vision processing.

Article Details

Volume / Issue Vol. 37, Issue 37
Published September 01, 2025
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (7)

Q

Qing‐Qing Wu

State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 China

Z

Zheng Li

M

Miao‐Hua Chen

State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 P. R. China

C

Cheng Yuan

State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry

Y

Yuan‐Cheng Zhu

State Key Laboratory of Analytical Chemistry for Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing 210023 P. R. China

J

Jing‐Juan Xu

State Key Laboratory of Analytical Chemistry For Life Science School of Chemistry and Chemical Engineering Nanjing University Nanjing China

W

Wei‐Wei Zhao

State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry Nanjing University Nanjing China