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March 10, 2026Advanced Intelligent Systems0 citationsOpen Access

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

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TSTheodoros SerghiouRMRogério Miranda MoraisDVDouglas Henrique Vieira

Key Points

  • The aim is to develop biodegradable and bioinspired synaptic transistors for use in artificial intelligence vision systems.
  • Fabricated synaptic phototransistor based on electrolyte-gated field-effect transistor architecture.
  • Used eco-friendly materials including poly(butylene adipate-co-terephthalate)/poly(lactic acid) substrate.
  • Implemented reduced graphene oxide electrodes and ZnO active layer for UV light sensing.
  • Evaluated neuromorphic behaviors including plasticity at low energy consumption.
  • The device exhibits short- and long-term plasticity alongside light-response plasticity.
  • Stable performance observed under varying UV intensities and exposure durations.
  • Tunable memory achieved through scan rate and sweep range modulation.
  • Proposed a learning model for simulating UV damage, indicating potential health-monitoring applications.

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.

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Cite This Study

Serghiou et al. (2026) studied this question.

synapsesocial.com/papers/69af950a70916d39fea4c38ehttps://doi.org/10.1002/aisy.202501338
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