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Soft Electronics for Health Monitoring Assisted by Machine Learning 被引量:4
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作者 Yancong Qiao Jinan Luo +11 位作者 Tianrui Cui Haidong Liu Hao Tang Yingfen Zeng Chang Liu Yuanfang Li jinming jian Jingzhi Wu He Tian Yi Yang Tian-Ling Ren jianhua Zhou 《Nano-Micro Letters》 SCIE EI CAS CSCD 2023年第5期83-168,共86页
Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of ... Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed. 展开更多
关键词 Soft electronics Machine learning algorithm Physiological signal monitoring Soft materials
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Black phosphorus junctions and their electrical and optoelectronic applications 被引量:5
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作者 Ningqin Deng He Tian +5 位作者 jian Zhang jinming jian Fan Wu Yang Shen Yi Yang Tian-Ling Ren 《Journal of Semiconductors》 EI CAS CSCD 2021年第8期19-31,共13页
Black phosphorus(BP),an emerging two-dimensional material,is considered a promising candidate for next-generation electronic and optoelectronic devices due to in-plane anisotropy,high mobility,and direct bandgap.Howev... Black phosphorus(BP),an emerging two-dimensional material,is considered a promising candidate for next-generation electronic and optoelectronic devices due to in-plane anisotropy,high mobility,and direct bandgap.However,BP devices face challenges due to their limited stability,photo-response speed,and detection range.To enhance BP with powerful electrical and optical performance,the BP heterostructures can be created.In this review,the state-of-the-art heterostructures and their electrical and optoelectronic applications based on black phosphorus are discussed.Five parts introduce the performance of BP-based devices,including black phosphorus sandwich structure by hBN with better stability and higher mobility,black phosphorus homojunction by dual-gate structure for optical applications,black phosphorus heterojunction with other 2D materials for faster photo-detection,black phosphorus heterojunction integration with 3 D bulk material,and BP via Asdoping tunable bandgap enabling photo-detection up to 8.2μm.Finally,we discuss the challenges and prospects for BP electrical and optical devices and applications. 展开更多
关键词 black phosphorus PHOTODETECTOR heterostructure HOMOJUNCTION 2D material
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