Electro‐Steric Ion Confinement in Polyelectrolyte Networks for Robust Nonvolatile Artificial Synapse

ABSTRACT Organic electrochemical synaptic transistors (OESTs) have garnered considerable attention as promising neuromorphic devices owing to their low operating voltage and high ionic sensitivity. Nevertheless, realizing nonvolatile synaptic behavior in OESTs remains fundamentally challenging because ion relaxation is governed by electrolyte‐level transport and trapping rather than channel properties, thereby limiting channel‐centric design strategies. To overcome this intrinsic trade‐off, the stoichiometric composition of an ion‐gating polyelectrolyte is modulated, offering a robust alternative to complex channel‐centric modifications. This study demonstrates that a poly(styrene sulfonate) (PSS)‐dominated architecture (1PAA–3PSS) induces a distinct dual‐confinement mechanism that transforms ion relaxation kinetics from a diffusion‐dominated regime to a trap‐controlled regime. In this electrolyte architecture, synergistic steric and electrostatic confinement effectively immobilizes doped ions at the channel interface. This mechanism enables sustained electrochemical doping, as evidenced by the persistence of polaronic states for up to 1,200 s after bias removal. Consequently, the optimized OESTs exhibit outstanding nonvolatile synaptic retention, highly symmetric weight modulation, and robust switching endurance over more than 5,000 operation cycles. This work establishes a scalable, electrolyte‐centric paradigm for regulating ion transport dynamics in high‐performance neuromorphic and bioinspired electronic systems.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-05-31
DOI
https://doi.org/10.1002/adfm.76235
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Electro‐Steric Ion Confinement in Polyelectrolyte Networks for Robust Nonvolatile Artificial Synapse

Seong‐Min Bak, Jinbo Kim, Jeonghun Kim, Junho Sung et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Electro‐Steric Ion Confinement in Polyelectrolyte Networks for Robust Nonvolatile Artificial Synapse

Seong‐Min Bak, Jinbo Kim, Jeonghun Kim, Junho Sung, Myeongjin An, Eunsung Hwang, Eunho Lee, Jisoo Park, Donghwa Lee, Suhwan Seok
article en

Abstract

ABSTRACT Organic electrochemical synaptic transistors (OESTs) have garnered considerable attention as promising neuromorphic devices owing to their low operating voltage and high ionic sensitivity. Nevertheless, realizing nonvolatile synaptic behavior in OESTs remains fundamentally challenging because ion relaxation is governed by electrolyte‐level transport and trapping rather than channel properties, thereby limiting channel‐centric design strategies. To overcome this intrinsic trade‐off, the stoichiometric composition of an ion‐gating polyelectrolyte is modulated, offering a robust alternative to complex channel‐centric modifications. This study demonstrates that a poly(styrene sulfonate) (PSS)‐dominated architecture (1PAA–3PSS) induces a distinct dual‐confinement mechanism that transforms ion relaxation kinetics from a diffusion‐dominated regime to a trap‐controlled regime. In this electrolyte architecture, synergistic steric and electrostatic confinement effectively immobilizes doped ions at the channel interface. This mechanism enables sustained electrochemical doping, as evidenced by the persistence of polaronic states for up to 1,200 s after bias removal. Consequently, the optimized OESTs exhibit outstanding nonvolatile synaptic retention, highly symmetric weight modulation, and robust switching endurance over more than 5,000 operation cycles. This work establishes a scalable, electrolyte‐centric paradigm for regulating ion transport dynamics in high‐performance neuromorphic and bioinspired electronic systems.

Advanced Functional Materials
Seoul National University of Science and Technology (KR), Yonsei University (KR)
National Research Foundation of Korea
Openalex Percentile: Top 9%
Advanced Memory and Neural Computing
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