From Memory Tracesto Surface Chemistry: DecodingREDOX Reactions

Gas and moisture sensing devices leveraging the resistive switching effect in transition metal oxide memristors promise to revolutionize next-generation, nanoscaled, cost-effective, and environmentally sustainable sensor solutions. These sensors encode readouts in resistance state changes based on gas concentration, yet their nonlinear current–voltage characteristics offer richer dynamics, capturing detailed information about REDOX reactions and surface kinetics. Traditional vertical devices fail to fully exploit this complexity. This study demonstrates planar resistive switching devices, moving beyond the Butler–Volmer model. A systematic investigation of the electrochemical processes in Na-doped ZnO with lateral planar contacts reveals intricate patterns resulting from REDOX reactions on the device surface. When combined with advanced algorithms for pattern recognition, these allow the analysis of complex switching patterns, including crossings, loop directions, and resistance values, providing unprecedented insights for next-generation complex sensors.

Publication Details

Journal
Figshare
Published
2025-02-17
DOI
https://doi.org/10.1021/acsaelm.4c01977.s001
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

From Memory Tracesto Surface Chemistry: DecodingREDOX Reactions

Figshare
Advanced Memory and Neural Computing
article

From Memory Tracesto Surface Chemistry: DecodingREDOX Reactions

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Abstract

Gas and moisture sensing devices leveraging the resistive switching effect in transition metal oxide memristors promise to revolutionize next-generation, nanoscaled, cost-effective, and environmentally sustainable sensor solutions. These sensors encode readouts in resistance state changes based on gas concentration, yet their nonlinear current–voltage characteristics offer richer dynamics, capturing detailed information about REDOX reactions and surface kinetics. Traditional vertical devices fail to fully exploit this complexity. This study demonstrates planar resistive switching devices, moving beyond the Butler–Volmer model. A systematic investigation of the electrochemical processes in Na-doped ZnO with lateral planar contacts reveals intricate patterns resulting from REDOX reactions on the device surface. When combined with advanced algorithms for pattern recognition, these allow the analysis of complex switching patterns, including crossings, loop directions, and resistance values, providing unprecedented insights for next-generation complex sensors.

Figshare
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Advanced Memory and Neural Computing
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