Humidity-responsive multistate resistive switching device for non-contact sensory neuro-electronic applications

Integrated sensory-based synaptic systems offer a promising pathway for developing future neuro-electronic technologies that emulate the human brain. In this study, we present a humidity-sensitive multistate memristive device based on porous nickel pyrophosphate (Ni 2 P 2 O 7 ), designed for touch-free sensory neuro-electronic applications. The device exhibits distinct I – V characteristics under varying relative humidity (RH) conditions, with both the half-period time ( ) and hysteresis area showing significant modulation as a function of RH. These parameters serve as key indicators for evaluating humidity-responsive neuromorphic devices. Furthermore, the device demonstrates effective synaptic modulation when stimulated by a moisturized finger placed 1 mm away, enabling non-contact operation. Density functional theory (DFT) calculations were employed to analyze the intrinsic electronic properties of Ni 2 P 2 O 7 and to investigate the changes induced upon H 2 O adsorption. These findings suggest that the proposed Au/Ni 2 P 2 O 7 /fluorine-doped tin oxide (FTO)-based humidity-sensitive resistive memory device is a strong candidate for future non-contact sensory neuro-electronic systems designed to mimic brain-like functionality. • Porous Ni 2 P 2 O 7 memristor enables humidity-sensitive multistate switching. • Analog resistive switching achieved with tunable states under humidity. • Non-contact synaptic response demonstrated using moist finger proximity. • DFT shows H 2 O adsorption narrows bandgap and improves conductivity. • SVM and MLP classify proximity and humidity states with >97% accuracy.

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Publication Details

Journal
eScience
Published
2025-12-01
DOI
https://doi.org/10.1016/j.esci.2025.100520
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

Humidity-responsive multistate resistive switching device for non-contact sensory neuro-electronic applications

Deepak P. Dubal, Rayyan Ali Shaukat, Mahesh Y. Chougale, Tukaram D. Dongale et al.
1 citations
eScience
Advanced Memory and Neural Computing
0.73
article

Humidity-responsive multistate resistive switching device for non-contact sensory neuro-electronic applications

Deepak P. Dubal, Rayyan Ali Shaukat, Mahesh Y. Chougale, Tukaram D. Dongale, Qazi Muhammad Saqib, Swapnil R. Patil, Snehal L. Patil, Sourabh B. Ghode, Jungmin Kim, Gaurav M. Lohar, Jinho Bae
article en
1 citations

Abstract

Integrated sensory-based synaptic systems offer a promising pathway for developing future neuro-electronic technologies that emulate the human brain. In this study, we present a humidity-sensitive multistate memristive device based on porous nickel pyrophosphate (Ni 2 P 2 O 7 ), designed for touch-free sensory neuro-electronic applications. The device exhibits distinct I – V characteristics under varying relative humidity (RH) conditions, with both the half-period time ( ) and hysteresis area showing significant modulation as a function of RH. These parameters serve as key indicators for evaluating humidity-responsive neuromorphic devices. Furthermore, the device demonstrates effective synaptic modulation when stimulated by a moisturized finger placed 1 mm away, enabling non-contact operation. Density functional theory (DFT) calculations were employed to analyze the intrinsic electronic properties of Ni 2 P 2 O 7 and to investigate the changes induced upon H 2 O adsorption. These findings suggest that the proposed Au/Ni 2 P 2 O 7 /fluorine-doped tin oxide (FTO)-based humidity-sensitive resistive memory device is a strong candidate for future non-contact sensory neuro-electronic systems designed to mimic brain-like functionality. • Porous Ni 2 P 2 O 7 memristor enables humidity-sensitive multistate switching. • Analog resistive switching achieved with tunable states under humidity. • Non-contact synaptic response demonstrated using moist finger proximity. • DFT shows H 2 O adsorption narrows bandgap and improves conductivity. • SVM and MLP classify proximity and humidity states with >97% accuracy.

eScience
Shivaji University (IN), Queensland University of Technology (AU), G.S. Science, Arts And Commerce College (IN), Jeju National University (KR)
Industry, innovation and infrastructure
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
0.73
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