Organic mixed conductors for bioinspired electronics

神经形态工程学 数码产品 纳米技术 导电体 计算机科学 材料科学 仿生学 人工神经网络 电气工程 人工智能 工程类
作者
Paschalis Gkoupidenis,Yan Zhang,Hans Kleemann,Haifeng Ling,Francesca Santoro,Simone Fabiano,Alberto Salleo,Yoeri van de Burgt
出处
期刊:Nature Reviews Materials [Nature Portfolio]
卷期号:9 (2): 134-149 被引量:166
标识
DOI:10.1038/s41578-023-00622-5
摘要

Owing to its close resemblance to biological systems and materials, soft matter has been successfully implemented in numerous bioelectronic and biosensing applications, as well as in bioinspired computing and neuromorphic electronics. Particularly, organic mixed ionic–electronic conductors possess favourable characteristics for their efficient use in organic electrochemical transistors, electrochemical memory and artificial synapses and neurons. Owing to their mixed ionic–electronic conduction, leading to high amplification, these materials are ideal for translating chemical signals, such as ions or neurotransmitters, into electrical signals, as well as for accurately controlling stable conductance states to efficiently emulate synaptic weights in artificial neural networks. Because these mixed conductors operate with ionic charges — similar to signalling in biological neuronal networks — they also exhibit ideal properties to emulate biological spiking neurons. In this Perspective, we consider the potential of soft matter, especially based on organic mixed conductors, for bioinspired systems and their possible applications. We discuss the potential that these materials have in applications in which low power, conformability and tunability are key, such as smart and adaptive biosensors, low-power in-sensor and edge computing, intelligent agents and robotics, and event-driven systems and biohybrid spiking circuits at the interface with biology. We present a comprehensive perspective of the potential of biomimetic and bioinspired electronics based on soft matter to integrate artificial intelligence into everyday life.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
张欢馨应助半夏的IQ蛋蛋采纳,获得10
刚刚
sparks发布了新的文献求助10
1秒前
1秒前
在水一方应助渴望者采纳,获得10
2秒前
FashionBoy应助桃仐采纳,获得10
3秒前
冷酷的柚子完成签到,获得积分20
5秒前
6秒前
乐观忆梅完成签到,获得积分20
6秒前
6秒前
xiaokezhang完成签到,获得积分20
7秒前
152完成签到 ,获得积分10
8秒前
11111完成签到,获得积分10
10秒前
xiaokezhang发布了新的文献求助10
10秒前
DTO完成签到,获得积分20
10秒前
摸余一直爽完成签到,获得积分10
11秒前
12秒前
小马甲应助于清绝采纳,获得10
13秒前
14秒前
团团完成签到 ,获得积分10
15秒前
张二十八发布了新的文献求助10
17秒前
17秒前
18秒前
104zw完成签到,获得积分10
18秒前
共享精神应助科研小白菜采纳,获得10
19秒前
v0id应助百里幻竹采纳,获得10
19秒前
19秒前
19秒前
Muhebbet发布了新的文献求助100
21秒前
苗佳威完成签到,获得积分10
21秒前
21秒前
单凝发布了新的文献求助10
23秒前
23秒前
yusensong1发布了新的文献求助10
24秒前
24秒前
彪yu完成签到,获得积分10
24秒前
学术laji发布了新的文献求助10
26秒前
于清绝发布了新的文献求助10
27秒前
爱笑歌曲完成签到,获得积分10
29秒前
传奇3应助风中画板采纳,获得10
29秒前
NexusExplorer应助精明纸鹤采纳,获得10
30秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior 1000
全员动态考核,锚定高质量发展:读懂同济大学教师人事改革新政的深层价值 900
Health Psychology 800
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
Römisch-Germanische Forschungen 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7595409
求助须知:如何正确求助?哪些是违规求助? 9172097
关于积分的说明 19634315
捐赠科研通 7172735
什么是DOI,文献DOI怎么找? 3267827
关于科研通互助平台的介绍 2432647
邀请新用户注册赠送积分活动 2260909