Ferroelectric composite-based piezoelectric energy harvester for selfpowered detection of obstructive sleep
作者机构：Department of Robotics and Mechatronics EngineeringDaegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu42988South Korea Department of Electrical EngineeringSiksha‘O’Anusandhan UniversityBhubaneswar751030India Department of Electrical EngineeringPoornima College of EngineeringJaipur303903India Department of PhysicsIndian Institute of Technology-GuwahatiAssam781039India Robotics and Mechatronics Research CenterDaegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu42988South Korea
出 版 物：《Journal of Materiomics》 (无机材料学学报（英文）)
年 卷 期：2023年第9卷第4期
页 面：609-617页
核心收录：
学科分类：07[理学] 070205[理学-凝聚态物理] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0702[理学-物理学]
主 题：Lead-free Ferroelectric Nanogenerator Sleepdisorder
摘 要：Lead-free piezoelectric ceramic is a promising material for energy harvesters,as they have superior electromechanical,ferroelectric,and piezoelectric *** addition,piezoelectric ceramics can be blended with polymer to achieve high-flexibility polymer-ceramic composites,providing mechanical robustness and *** this context,a new lead-free ferroelectric material,having the chemical formula SrTi_(2)O_(5)(STO),was synthesized using a high-temperature solid-state *** analyses of the structural,morphological,and electrical properties of the synthesized material were *** crystallizes with orthorhombic symmetry and space group of *** frequency and temperature-dependent dielectric parameters were evaluated,and impedance spectroscopy shed light on the charge *** PDMS-STO composites at different mass fraction of the STO were prepared using a solvent casting route,and a corresponding piezoelectric nanogenerator(PENG)was *** electrical output of the different PENG by varying massfractions of STO in PDMS and varying force were *** 15%(in mass)PENG device delivered the highest peak-to-peak voltage,current,and power density of 25 V,92 nA,and 0.64 mW@500 MU,*** biomechanical energy harvesting using the PENG device by daily human motions,bending of the device,and attaching the device to laboratory equipment was *** the PENG device was attached to the human throat region,and snoring signals were recorded.A classification model was designed employing the convolutional neural network(CNN)*** have been laid to differentiate between normal and abnormal snores,which could help the patient with screening and early disease detection,contributing to self-powered healthcare applications.
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