Biodegradable and Bioinspired UV Light Recognition via Sustainable Synaptic Transistors for Artificial Intelligence Vision Systems

Presented here is a biodegradable, bioinspired synaptic phototransistor (SPT) based on an electrolyte‐gated field‐effect transistor (EGFET) architecture for sustainable artificial intelligence vision systems (AIVSs). The EGFET is designed to be zero waste and degrades to benign end products at the end‐of‐life. The device is fabricated onto a poly(butylene adipate‐ co ‐terephthalate)/poly(lactic acid) (PBAT/PLA) bioderived substrate, which uses reduced graphene oxide (rGO) electrodes, a ZnO active layer, and honey as a natural gate electrolyte, enabling simultaneous sensing and memory of UV light stimuli. Through charge trapping/detrapping and field‐effect modulation, the device exhibits key neuromorphic behaviors including short‐ and long‐term plasticity, spike‐time‐dependent plasticity (STDP), and light‐response plasticity at low operating voltages and energy consumption. The EGFET demonstrates tunable memory via scan rate and sweep range modulation and maintains stable synaptic responses under varying UV intensities and exposure durations. A learning model simulating UV‐induced skin and ocular damage is proposed, highlighting the device's potential for wearables and health‐monitoring applications. Overall, the work demonstrates the feasibility of manufacturing SPT devices based on EGFETS using eco‐friendly materials for neuromorphic electronics while minimizing the growing e‐waste problem in electronics.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2026-02-24
DOI
https://doi.org/10.1002/aisy.202501338
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Biodegradable and Bioinspired UV Light Recognition via Sustainable Synaptic Transistors for Artificial Intelligence Vision Systems

R. Parvizi, Rogério Miranda Morais, Douglas Henrique Vieira, Jeff Kettle et al.
Advanced Intelligent Systems
Advanced Memory and Neural Computing
article

Biodegradable and Bioinspired UV Light Recognition via Sustainable Synaptic Transistors for Artificial Intelligence Vision Systems

R. Parvizi, Rogério Miranda Morais, Douglas Henrique Vieira, Jeff Kettle, Theodoros Serghiou, Neri Alves
article en

Abstract

Presented here is a biodegradable, bioinspired synaptic phototransistor (SPT) based on an electrolyte‐gated field‐effect transistor (EGFET) architecture for sustainable artificial intelligence vision systems (AIVSs). The EGFET is designed to be zero waste and degrades to benign end products at the end‐of‐life. The device is fabricated onto a poly(butylene adipate‐ co ‐terephthalate)/poly(lactic acid) (PBAT/PLA) bioderived substrate, which uses reduced graphene oxide (rGO) electrodes, a ZnO active layer, and honey as a natural gate electrolyte, enabling simultaneous sensing and memory of UV light stimuli. Through charge trapping/detrapping and field‐effect modulation, the device exhibits key neuromorphic behaviors including short‐ and long‐term plasticity, spike‐time‐dependent plasticity (STDP), and light‐response plasticity at low operating voltages and energy consumption. The EGFET demonstrates tunable memory via scan rate and sweep range modulation and maintains stable synaptic responses under varying UV intensities and exposure durations. A learning model simulating UV‐induced skin and ocular damage is proposed, highlighting the device's potential for wearables and health‐monitoring applications. Overall, the work demonstrates the feasibility of manufacturing SPT devices based on EGFETS using eco‐friendly materials for neuromorphic electronics while minimizing the growing e‐waste problem in electronics.

Advanced Intelligent Systems
Universidade do Oeste Paulista (BR), Hospital Regional de Presidente Prudente (BR), University of Glasgow (GB), Universidade Estadual Paulista (Unesp) (BR)
UK Research and Innovation, Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Instituto Nacional de Ciência e Tecnologia em Eletrônica Orgânica, Engineering and Physical Sciences Research Council
Responsible consumption and production
Openalex Percentile: Top 31%
Advanced Memory and Neural Computing
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