论文题目：Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

论文作者：Huanhuan Wei, Rongchao Shi, Lin Sun, Haiyang Yu, Jiangdong Gong,
Chao Liu, Zhipeng Xu, Yao Ni, Jialiang Xu*, Wentao Xu

发表期刊：Nature Communications, 12(1), 1068, 2021    

Abstract： 

A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic  short-term plasticity, has been proposed to mimic biological signal  transmission behavior. The impulse response of the GAS has been reduced  to several millivolts with competitive femtowatt-level consumption,  exceeding the biological level by orders of magnitude. Most importantly,  the GAS is capable of parallelly processing signals transmitted from  multiple pre-neurons and therefore realizing dynamic logic and  spatiotemporal rules. It is also found that the GAS is thermally stable  (at 353K) and environmentally stable (in a relative humidity up to 35%).  Our artificial efferent nerve, connecting the GAS with artificial  muscles, has been demonstrated to complete the information integration  of pre-neurons and the information output of motor neurons, which is  advantageous for coalescing multiple sensory feedbacks and reacting to  events. Our synaptic element has potential applications in bioinspired  peripheral nervous systems of soft electronics, neurorobotics, and  biohybrid systems of brain-computer interfaces. Constructing artificial  sensorimotor systems for robotic applications calls for development of  synaptic connections for complicated information processing. Wei et al.  propose a graphdiyne-based artificial synapse capable of parallel  processing signals and utilize it in an artificial mechanoreceptor  system.