Bioinspired Ion Doping for Threshold Control in Green Chitosan-Based Flexible Transistor Neuromorphic Devices

Electrolyte-gated transistors (EGTs) are promising candidates for flexible neuromorphic electronics featuring low-voltage operation via ion-electron coupling. However, precise threshold control remains a key challenge due to the dynamic nature of the electric double layer (EDL). This study introduces a facile doping strategy, in which Na<sup>+</sup> cations, analogous to biological neurotransmission ions, are incorporated into chitosan-based EGTs to modulate the EDL. This approach enables continuous threshold voltage tuning by controlling NaCl doping concentrations. At 0.5 wt %, a clear transition from depletion to enhancement mode is achieved with a Quasi-neutral turn-off condition, where the drain current without gate bias decreases from ∼10<sup>-4</sup> A (0 wt %) to below 10<sup>-7</sup> A (0.5 wt %) under 0.2 V drain voltage, and the energy consumption for synaptic functions is reduced by ∼200 times. The resulting devices exhibit high on/off ratios of >10<sup>3</sup>, operational stability exceeding 100 days, and excellent mechanical durability over 1000 bending cycles, making them suitable for flexible green bioelectronics. Furthermore, the devices can emulate key synaptic behaviors, including excitatory postsynaptic currents, paired-pulse facilitation, and spike-dependent plasticity, enabling neuromorphic computing with >95% accuracy in image recognition. This work establishes a scalable, biocompatible, and energy-efficient platform for threshold-controllable green EGTs in next-generation flexible neuromorphic devices.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-01-27
DOI
https://doi.org/10.1021/acsami.5c21733
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Bioinspired Ion Doping for Threshold Control in Green Chitosan-Based Flexible Transistor Neuromorphic Devices

Dewei Chu, Zhi Li, Fandi Chen, Jiyun Kim et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Bioinspired Ion Doping for Threshold Control in Green Chitosan-Based Flexible Transistor Neuromorphic Devices

Dewei Chu, Zhi Li, Fandi Chen, Jiyun Kim, Nicholas J. Ekins-Daukes, Mingrui He, Tingting Mei, Guojun He, Tianxu Huang, Zijian Feng, Chun-Ho Lin, Xinyu Ma, Shuo Zhang, Shimul Kanti Nath, Xiaojing Hao, Long Hu
article en

Abstract

Electrolyte-gated transistors (EGTs) are promising candidates for flexible neuromorphic electronics featuring low-voltage operation via ion-electron coupling. However, precise threshold control remains a key challenge due to the dynamic nature of the electric double layer (EDL). This study introduces a facile doping strategy, in which Na<sup>+</sup> cations, analogous to biological neurotransmission ions, are incorporated into chitosan-based EGTs to modulate the EDL. This approach enables continuous threshold voltage tuning by controlling NaCl doping concentrations. At 0.5 wt %, a clear transition from depletion to enhancement mode is achieved with a Quasi-neutral turn-off condition, where the drain current without gate bias decreases from ∼10<sup>-4</sup> A (0 wt %) to below 10<sup>-7</sup> A (0.5 wt %) under 0.2 V drain voltage, and the energy consumption for synaptic functions is reduced by ∼200 times. The resulting devices exhibit high on/off ratios of >10<sup>3</sup>, operational stability exceeding 100 days, and excellent mechanical durability over 1000 bending cycles, making them suitable for flexible green bioelectronics. Furthermore, the devices can emulate key synaptic behaviors, including excitatory postsynaptic currents, paired-pulse facilitation, and spike-dependent plasticity, enabling neuromorphic computing with >95% accuracy in image recognition. This work establishes a scalable, biocompatible, and energy-efficient platform for threshold-controllable green EGTs in next-generation flexible neuromorphic devices.

ACS Applied Materials & InterfacesVol. 18(5)
University of Technology Sydney (AU), UNSW Sydney (AU)
Affordable and clean energy
Openalex Percentile: Top 76%
Advanced Memory and Neural Computing
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