SNUEI

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

2026 · Advanced Functional Materials

저자

D.S. Lee, Byung Ik Park, Gihyeon Yim, Hyunjae Yoo, Sangjun Ma, Jundong Park, 선정윤 (Jeong‐Yun Sun)

연구주제

Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Conducting polymers and applications

NTIS 과학기술표준분류

에너지/자원만에너지·환경·원자력 확장

가장 가까운 분류(보건의료 > 의료기기 > 의료용 소재)가 에너지·환경·원자력 확장 범위 밖이다.

AI 연구 키워드·방법

이식형 생체전자소자 ionic memristor natural polymer hydrogel 리저버 컴퓨팅

키워드 (OpenAlex)

biomedical implants hyaluronic acid hydrogel chitosan hydrogel polydimethylsiloxane substrate reservoir computing synaptic plasticity biocompatibility in vivo evaluation

초록

ABSTRACT Memristors emulate the synaptic plasticity of the human brain by exhibiting conductance modulation dependent on prior stimuli, thereby enabling simultaneous information processing and storage. Ionic memristors, which utilize the migration of biological ions (e.g., K + , Cl − ), exhibit history‐dependent synaptic plasticity and offer a more biocompatible approach than conventional solid‐state electronic memristors. However, existing ionic memristors typically employ rigid substrates and biologically incompatible materials, which limit their potential for use in implantable devices. Here, we report a fully biocompatible natural polymer‐based ionic memristor, comprising a hyaluronic acid hydrogel, a chitosan hydrogel, and a polydimethylsiloxane (PDMS) substrate. Hyaluronic acid and chitosan are biodegradable natural polymers widely used in biomedical applications, while PDMS is a b…

이 논문을 지원한 국가R&D 과제 (NTIS 성과)

NTIS 성과DB(2020–24)에서 DOI 매칭 없음

인접 그래프

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교수 1

선정윤

연구주제 1

Advanced Memory and Neural Computing

키워드 2

이식형 생체전자소자 · 리저버 컴퓨팅