인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In the domains of wearable electronics, robotics, and the Internet of Things, there is a demand for devices with low power consumption and the capability of multiplex sensing, memory, and learning. Triboelectric nanogenerators (TENGs) offer remarkable versatility in this regard, particularly when integrated with synaptic transistors that mimic biological synapses. However, conventional TENGs, generating only two spikes per cycle, have limitations when used in synaptic devices requiring repetitive high-frequency gating signals to perform various synaptic plasticity functions. Herein, a multi-layered micropatterned TENG (M-TENG) consisting of a polydimethylsiloxane (PDMS) film and a composite film that includes 1H,1H,2H,2H-perfluorooctyltrichlorosilane/BaTiO<sub>3</sub> /PDMS are proposed. The M-TENG generates multiple spikes from a single touch by utilizing separate triboelectric charges at the multiple friction layers, along with a contact/separation delay achieved by distinct spacers between layers. This configuration allows the maximum triboelectric output charge of M-TENG to reach up to 7.52 nC, compared to 3.69 nC for a single-layered TENG. Furthermore, by integrating M-TENGs with an organic electrochemical transistor, the spike number multiplication property of M-TENGs is leveraged to demonstrate an artificial synaptic device with low energy consumption. As a proof-of-concept application, a robotic hand is operated through continuous memory training under repeated stimulations, successfully emulating long-term plasticity.
#Triboelectric effect
#Materials science
#Polydimethylsiloxane
#Transistor
#Wearable technology
#Synaptic weight
#Nanotechnology
#Electronics
#Synapse
#Optoelectronics
#Computer science
#Electrical engineering
#Voltage
#Wearable computer
#Artificial neural network
#Artificial intelligence
#Embedded system
#Engineering
#Neuroscience
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