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Review on Improvement,Modeling,and application of Ionic Polymer Metal Composite Artificial Muscle

Journal of bionic Engineering 2022年第2期19卷 279-298页

作者： Qingsong He Guoxiao Yin David Vokoun Qi Shen Ji Lu Xiaofang Liu Xianrui Xu Min Yu Zhendong Dai Jiangsu Provincial Key Laboratory of bionic Functional Materials Institute of Bio-Inspired Structure and Surface EngineeringCollege of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016China Institute of Physics of the Academy of Sciences of the Czech Republic Prague 18221Czech Republic Active Materials and Smart Living Laboratory Department of Mechanical EngineeringUniversity of Nevada Las VegasLas Vegas 89154-4027USA

Recently,researchers have concentrated on studying ionic polymer metal composite(IPMC)artificial muscle,which has numerous advantages including a relatively large strain under low input voltage,flexibility,high response,low noise,light weight,and high driving energy *** paper reports recent developments in IPMC artificial muscle,including improvement methods,modeling,and *** types of IPMCs are described,along with various methods for overcoming some shortcomings,including improvement of Nafion matrix membranes,surface preparation of Nafion membranes,the choice of high-performing electrodes,and new electro-active polymers for enhancing the properties of *** models are also reviewed,providing theoretical guidance for studying the performance and applications of *** applications such as bio-inspired robots,opto-mechatronic systems,and medical engineering are discussed.

关键词： Ionic polymer metal composite Artificial muscle Improvement Model bio-inspired application

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Nanoscale resistive switching devices for memory and computing applications

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Nano Research 2020年第5期13卷 1228-1243页

作者： Seung Hwan Lee Xiaojian Zhu Wei D.Lu Department of Electrical Engineering and Computer Science The University of MichiganAnn ArborMichigan 48109USA

With the slowing down of the Moore’s law and fundamental limitations due to the von-Neumann bottleneck,continued improvements in computing hardware performance become increasingly more *** switching(RS)devices are being extensively studied as promising candidates for next generation memory and computing applications due to their fast switching speed,excellent endurance and retention,and scaling and three-dimensional(3D)stacking *** particular,RS devices offer the potential to natively emulate the functions and structures of synapses and neurons,allowing them to efficiently implement neural networks(NNs)and other in-memory computing systems for data intensive applications such as machine learning *** this review,we will examine the mechanisms of RS effects and discuss recent progresses in the application of RS devices for memory,deep learning accelerator,and more faithful brain-inspired computing *** and possible solutions at the device,algorithm,and system levels will also be discussed.

关键词： resistive switching oxygen vacancy metal cation memory application in-memory computing bio-inspired application

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