Bridging Local Anionic Polarity to Macroscopic State Separability: Molecular Design for Tailored Polaron Kinetics in Organic Synaptic Transistors

ABSTRACT Rational modulation over carrier dynamics in organic mixed ionic–electronic conductors is fundamental to modern neuromorphic hardware, yet molecularly regulating the relaxation kinetics of polarons to dictate high‐dimensional state space remains an elusive goal. Here, we demonstrate that the anionic polarity index (API), a quantitative metric defining localized charge density, functions as a predictive descriptor bridging microscopic polaron kinetics to macroscopic state space evolution in electrolyte‐gated organic synaptic transistors (EGOSTs). Rather than acting as passive counterions, high‐API anions concentrate their local electrostatic potential, establishing robust non‐covalent interactions with the oxidized polymer matrix. This electrostatic confinement elevates the energy barrier for anion back‐diffusion, retarding polaron relaxation kinetics without sacrificing electrochemical doping efficiency. By translating this local charge localization into macro‐scale transport control, we decouple dynamic range from fading memory, enabling precise tuning of the relaxation time constant and significantly expanding accessible reservoir state space. Consequently, the resulting physical reservoir achieves superior phase‐space separability, yielding a 91.2% classification accuracy in MNIST benchmark simulations and maintaining 89.2% accuracy under fourfold feature compression. This study establishes a generalizable molecular design strategy that quantitatively connects local electrostatic confinement to high‐dimensional state evolution in organic physical reservoirs.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-10-30
DOI
https://doi.org/10.1002/adfm.79372
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Bridging Local Anionic Polarity to Macroscopic State Separability: Molecular Design for Tailored Polaron Kinetics in Organic Synaptic Transistors

Meng Qiang Li, Eunho Lee, Donghwa Lee, Junho Sung et al.
10 links
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Bridging Local Anionic Polarity to Macroscopic State Separability: Molecular Design for Tailored Polaron Kinetics in Organic Synaptic Transistors

Meng Qiang Li, Eunho Lee, Donghwa Lee, Junho Sung, Myungjin An, Jaewon Lee, Jinglong Pang
article en

Abstract

ABSTRACT Rational modulation over carrier dynamics in organic mixed ionic–electronic conductors is fundamental to modern neuromorphic hardware, yet molecularly regulating the relaxation kinetics of polarons to dictate high‐dimensional state space remains an elusive goal. Here, we demonstrate that the anionic polarity index (API), a quantitative metric defining localized charge density, functions as a predictive descriptor bridging microscopic polaron kinetics to macroscopic state space evolution in electrolyte‐gated organic synaptic transistors (EGOSTs). Rather than acting as passive counterions, high‐API anions concentrate their local electrostatic potential, establishing robust non‐covalent interactions with the oxidized polymer matrix. This electrostatic confinement elevates the energy barrier for anion back‐diffusion, retarding polaron relaxation kinetics without sacrificing electrochemical doping efficiency. By translating this local charge localization into macro‐scale transport control, we decouple dynamic range from fading memory, enabling precise tuning of the relaxation time constant and significantly expanding accessible reservoir state space. Consequently, the resulting physical reservoir achieves superior phase‐space separability, yielding a 91.2% classification accuracy in MNIST benchmark simulations and maintaining 89.2% accuracy under fourfold feature compression. This study establishes a generalizable molecular design strategy that quantitatively connects local electrostatic confinement to high‐dimensional state evolution in organic physical reservoirs.

Advanced Functional Materials
Seoul National University of Science and Technology (KR), Chungnam National University (KR)
National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
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