Optically Tuneable Memristors Based on Au NP–PDR1A Nanocomposites Probed by Impedance Spectroscopy

ABSTRACT Optical memristors are promising building blocks for neuromorphic vision systems because they can emulate retina‐like adaptive signal processing. However, the development of practical devices remains limited by an incomplete understanding of the coupled electrical–optical responses and the mechanisms governing switching. In this work, gold nanoparticles (Au NPs) were dispersed in poly(disperse red 1 acrylate) (PDR1A) to form a photo‐responsive composite that was used to fabricate optical memristor devices via a simple solution‐based process. The optoelectronic behavior was systematically investigated using impedance spectroscopy to probe charge‐transport dynamics and interfacial processes under operating conditions, complementing structural TEM imaging studies that reveals the underlying morphology. The devices exhibit light‐tuneable resistive–capacitive switching, with a transition from purely capacitive behavior in pristine PDR1A devices to coupled resistive–capacitive transport in Au NP:PDR1A composites. Impedance analysis indicates trap‐mediated conduction together with nanoparticle‐assisted conductive pathways that are dynamically modulated by light‐induced photomechanical expansion and contraction of the polymer matrix. Equivalent‐circuit modeling further clarifies the resistive–capacitive interplay responsible for optically controlled switching. These findings provide mechanistic insight into light‐responsive memristive behavior and establish a framework for the design of optically tuneable memristive devices for neuromorphic computing and vision applications.

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Publication Details

Journal
Advanced Materials Technologies
Published
2026-10-08
DOI
https://doi.org/10.1002/admt.71388
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Optically Tuneable Memristors Based on Au NP–PDR1A Nanocomposites Probed by Impedance Spectroscopy

Ioannis Messaris, Ronald Tetzlaff, Alina Muravitskaya, Neil Timothy Kemp et al.
Advanced Materials Technologies
Advanced Memory and Neural Computing
article

Optically Tuneable Memristors Based on Au NP–PDR1A Nanocomposites Probed by Impedance Spectroscopy

Ioannis Messaris, Ronald Tetzlaff, Alina Muravitskaya, Neil Timothy Kemp, Ayoub H. Jaafar, Ali M. Adawi, Craig Venables, Chris Carter
article en

Abstract

ABSTRACT Optical memristors are promising building blocks for neuromorphic vision systems because they can emulate retina‐like adaptive signal processing. However, the development of practical devices remains limited by an incomplete understanding of the coupled electrical–optical responses and the mechanisms governing switching. In this work, gold nanoparticles (Au NPs) were dispersed in poly(disperse red 1 acrylate) (PDR1A) to form a photo‐responsive composite that was used to fabricate optical memristor devices via a simple solution‐based process. The optoelectronic behavior was systematically investigated using impedance spectroscopy to probe charge‐transport dynamics and interfacial processes under operating conditions, complementing structural TEM imaging studies that reveals the underlying morphology. The devices exhibit light‐tuneable resistive–capacitive switching, with a transition from purely capacitive behavior in pristine PDR1A devices to coupled resistive–capacitive transport in Au NP:PDR1A composites. Impedance analysis indicates trap‐mediated conduction together with nanoparticle‐assisted conductive pathways that are dynamically modulated by light‐induced photomechanical expansion and contraction of the polymer matrix. Equivalent‐circuit modeling further clarifies the resistive–capacitive interplay responsible for optically controlled switching. These findings provide mechanistic insight into light‐responsive memristive behavior and establish a framework for the design of optically tuneable memristive devices for neuromorphic computing and vision applications.

Advanced Materials Technologies
University of Jordan (JO), University of Nottingham (GB), University of Hull (GB), Technische Universität Dresden (DE)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
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