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Recent developments in self-powered smart chemical sensors for wearable electronics

Nano Research 2021年第11期14卷 3669-3689页

作者： Aaryashree Surjit Sahoo Pravin Walke Saroj Kumar Nayak Chandra Sekhar Rout dattatray j.late Shibaura Institute of Technology Koto-CityTokyo135-8548Japan School of Basic Sciences Indian Institute of Technology BhubaneswarJatniOdisha752050India National Centre for Nanosciences and Nanotechnology University of MumbaiMumbaiMaharashtra400098India Centre for Nano and Material Sciences Jain UniversityRamanagaramBangalore562112India Centre for Nanoscience&Nanotechnology Amity University MaharashtraMumbai-Pune ExpresswayBhatanPost—SomathnePanvelMaharashtra410206India

The next generation of electronics technology is purely going to be based on wearable sensing systems. Wearable electronic sensors that can operate in a continuous and sustainable manner without the need of an external power sources, are essential for portable and mobile electronic applications. In this review article, the recent progress and advantages of wearable self-powered smart chemical sensors systems for wearable electronics are presented. An overview of various modes of energy conversion and storage technologies for self-powered devices is provided. Self-powered chemical sensors (SPCS) systems with integrated energy units are then discussed, separated as solar cell-based SPCS, triboelectric nano-generators based SPCS, piezoelectric nano-generators based SPCS, energy storage device based SPCS, and thermal energy-based SPCS. Finally, the outlook on future prospects of wearable chemical sensors in self-powered sensing systems is addressed.

关键词： chemical sensors self-powered wearable electronics smart sensors

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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CdS nanocrystals as fluorescent probe for detection of dolasetron mesylate in aqueous solution:Application to biomedical analysis

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journal of Pharmaceutical Analysis 2016年第6期6卷 410-416页

作者： Samadhan P.Pawar Laxman S.Walekar Uttam R.Kondekar dattatray B.Gunjal Anil H.Gore Prashant V.Anbhule Shivajirao R.Patil Govind B.Kolekar Fluorescence Spectroscopy Research Laboratory Department of ChemistryShivaji University

A simple and straightforward method for the determination of dolasetron mesylate(DM) in aqueous solution was developed based on the fluorescence quenching of 3-Mercaptopropionic acid(MPA) capped Cd S quantum dots(QDs).The structure,morphology,and optical properties of synthesized QDs were characterized by using UV-Vis absorption spectroscopy,fluorescence spectroscopy,transmission electron microscopy(TEM) and dynamic light scattering(DLS) *** the optimum conditions,the MPA-Cd S QDs fluorescence probe offered good sensitivity and selectivity for detecting *** probe provided a highly specific selectivity and a linear detection of DM in the range of 2–40 μg/m L with detection limit(LOD) 1.512 μg/m *** common excipients did not interfere in the proposed *** fluorescence quenching mechanism of Cd S QDs is also *** developed sensor was applied to the quantification of DM in urine and human serum sample with satisfactory results.

关键词： Dolasetron mesylate CdS quantum dots Fluorescence quenching Nonradiative recombination

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Influence of Thickness on the Photosensing Properties of Chemically Synthesized Copper Sulfide Thin Films

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World journal of Condensed Matter Physics 2015年第1期5卷 1-9页

作者： Abhiman dattatray Dhondge Sunil Rameshgir Gosavi Narayani Madhukar Gosavi Chatur Pundlik Sawant Amar Maruti Patil Abhijeet Ravsaheb Shelke Nishad Gopal Deshpande C. H. C. Arts S. G. P. Commerce and B. B. J. P. Science College Taloda India Government College of Engineering Jalgaon India G. T. Patil College Nandurbar India q-SpinTech & Nanomaterials Laboratory Department of Physics Shivaji University Kolhapur India

We report here the influence of thickness on the photosensing properties of copper sulfide (CuS) thin films. The CuS films were deposited onto glass substrate by using a simple and cost effective chemical bath deposition method. The changes in film thickness as a function of time were monitored. The films were characterized using X-ray diffraction technique (XRD), field emission scanning electron microscopy (FE-SEM), optical measurement techniques and electrical measurement. X-ray diffraction results indicate that all the CuS thin films have an orthorhombic (covellite) structure with preferential orientation along (113) direction. The intensity of the diffraction peaks increases as thickness of the film increases. Uniform deposition having nanocrystalline granular morphology distributed over the entire glass substrate was observed through FE-SEM studies. The crystalline and surface properties of the CuS thin films improved with increase in the film thickness. Transmittance (except for 210 nm thick CuS film) together with band gap values was found to decrease with increase in thickness. I-V measurements under dark and illumination condition show that the CuS thin films give a good photoresponse.

关键词： Copper Sulfide (CuS) Chemical Bath Deposition (CBD) Thin Film X-Ray Diffraction (XRD) Field Emission Scanning Electron Microscopy (FESEM) Photosensitivity

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