Unveiling the Resistive and Capacitive Switching Memory in <scp>PVA</scp> Doped With Silver Nanoparticles for Wearable and Solid‐State Memory Devices

ABSTRACT This study aims to investigate flexible polyvinyl alcohol doped with silver nanoparticles (PVA‐AgNPs) nanocomposite films as a promising candidate for both capacitive and resistive switching memory applications. Different concentrations of silver nitrate (AgNO 3 ) (0.01, 0.02, and 0.04 mol) were utilized to synthesize various concentrations of AgNPs‐doped in PVA by physical irradiation to ultraviolet (3 J/cm 2 ) for 60 min. The successful formation of a face‐centered cubic of AgNPs, which exhibited boosted crystallinity by increasing AgNPs concentrations, was substantiated by X‐ray diffraction measurements as well as Fourier transform infrared spectroscopy (FTIR). Microstructural and compositional analyses were conducted using HRTEM, FESEM, and EDX. The AgNPs concentration‐dependence of optical properties, such as transmittance, absorbance, and reflectance, optical band gap, Urbach energy, extinction coefficient, and optical conductivity, was extensively studied. The optical band gap ( E g ) reduced from 4.60 to 2.01 eV as the AgNPs concentrations were changed from 0.00 to 0.04 mol. Electrical measurements on Cu/AgNP@PVA/Cu structures demonstrated stable bipolar resistive switching, attributed to Ag + ion migration and charge trapping/de‐trapping. High AgNPs (0.4 mol) concentrations yielded enhanced switching stability, indicating strong potential for non‐volatile memory applications.

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Publication Details

Journal
Polymers for Advanced Technologies
Published
2026-01-01
DOI
https://doi.org/10.1002/pat.70488
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
21.93

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article

Unveiling the Resistive and Capacitive Switching Memory in PVA Doped With Silver Nanoparticles for Wearable and Solid‐State Memory Devices

Reda Khalil, Fatimah Mohammad H. Al Sulami, Ashok Adhikari, Talaat A. Hameed et al.
1 citations
Polymers for Advanced Technologies
Advanced Memory and Neural Computing
21.93
article

Unveiling the Resistive and Capacitive Switching Memory in PVA Doped With Silver Nanoparticles for Wearable and Solid‐State Memory Devices

Reda Khalil, Fatimah Mohammad H. Al Sulami, Ashok Adhikari, Talaat A. Hameed, Reema H. Aldahiri, Ahlam I. Al ‐Sulami
article en
1 citations

Abstract

ABSTRACT This study aims to investigate flexible polyvinyl alcohol doped with silver nanoparticles (PVA‐AgNPs) nanocomposite films as a promising candidate for both capacitive and resistive switching memory applications. Different concentrations of silver nitrate (AgNO 3 ) (0.01, 0.02, and 0.04 mol) were utilized to synthesize various concentrations of AgNPs‐doped in PVA by physical irradiation to ultraviolet (3 J/cm 2 ) for 60 min. The successful formation of a face‐centered cubic of AgNPs, which exhibited boosted crystallinity by increasing AgNPs concentrations, was substantiated by X‐ray diffraction measurements as well as Fourier transform infrared spectroscopy (FTIR). Microstructural and compositional analyses were conducted using HRTEM, FESEM, and EDX. The AgNPs concentration‐dependence of optical properties, such as transmittance, absorbance, and reflectance, optical band gap, Urbach energy, extinction coefficient, and optical conductivity, was extensively studied. The optical band gap ( E g ) reduced from 4.60 to 2.01 eV as the AgNPs concentrations were changed from 0.00 to 0.04 mol. Electrical measurements on Cu/AgNP@PVA/Cu structures demonstrated stable bipolar resistive switching, attributed to Ag + ion migration and charge trapping/de‐trapping. High AgNPs (0.4 mol) concentrations yielded enhanced switching stability, indicating strong potential for non‐volatile memory applications.

Polymers for Advanced TechnologiesVol. 37(1)
Benha University (EG), University of Jeddah (SA), National Research Centre (EG), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (MX)
Ministry of Education, India
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
21.93
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