Hybrid organic-inorganic electrolytes via SiO 2 -doping: enhanced stability and ionic conductivity for neuromorphic EGTs

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Authors: Bo Sun, Yifu Fu, Mohd F. Z. Kadir, Siti Nabila Aidit, Shixun Zheng, Quanjin Ma, Feiyang Li, Zhaoji Zong, Sharifah Sharifah Faradila Wan Muhamad Hatta
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References: 55

Abstract

Abstract Hybrid organic–inorganic electrolytes represent a promising class of materials for sustainable and energy-efficient electronics, yet strategies to simultaneously improve their electrochemical robustness and functional device performance remain limited. Here, a chitosan-silica hybrid electrolyte was developed by incorporating silica nanoparticles (SiO 2 NPs, 0–2 wt%) into a chitosan matrix, and its structural, electrochemical, and dielectric properties were systematically investigated. At an optimized SiO 2 NPs content of 1.5 wt%, the hybrid electrolyte achieved an ionic conductivity of 1.83 × 10 −5 S cm −1 and a high ion transference number ( t ion = 0.909), while the swelling rate was suppressed by 160% and the breakdown voltage was enhanced to 2.76 ± 0.013 V. These improvements were attributed to increased segmental mobility and Lewis acid–base interactions that promoted ion dissociation and stabilized electric double-layer formation. To demonstrate functional relevance, the chitosan-silica hybrid electrolyte was integrated into an electrolyte-gated transistor (EGT) with zinc oxide nanoparticles (ZnO NPs) channel, yielding reproducible synaptic neuromorphic characteristics, including excitatory post-synaptic current (EPSC) of 399.32 ± 42.36 nA and a paired-pulse facilitation (PPF) index of 90.4%. Importantly, this work establishes a direct correlation between nanoscale electrolyte engineering and neuromorphic device responses, using synaptic behaviors as functional validation of electrolyte stability and ionic transport. The findings establish chitosan-silica hybrids as promising materials for next-generation neuromorphic and biocompatible electronics.

Fields of Study & Subjects

Advanced Battery Materials and Technologies Advanced Memory and Neural Computing Conducting polymers and applications

Publication details

Journal / source
Engineering Research Express
Volume
8
Issue
3
Pages
035330–035330
Publication date
1 February 2026