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Titlebook: Biomimetic Sensing; Methods and Protocol Jessica E. Fitzgerald,Hicham Fenniri Book 2019 Springer Science+Business Media, LLC, part of Sprin

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期刊全稱Biomimetic Sensing
期刊簡(jiǎn)稱Methods and Protocol
影響因子2023Jessica E. Fitzgerald,Hicham Fenniri
視頻videohttp://file.papertrans.cn/189/188216/188216.mp4
發(fā)行地址Includes cutting-edge techniques.Provides step-by-step detail essential for reproducible results.Contains key implementation advice from the experts
學(xué)科分類Methods in Molecular Biology
圖書封面Titlebook: Biomimetic Sensing; Methods and Protocol Jessica E. Fitzgerald,Hicham Fenniri Book 2019 Springer Science+Business Media, LLC, part of Sprin
影響因子This book highlights the potential of e-device technology to serve as a successful platform for multiplexed sensing, along with the methods for device fabrication, calibration, and assays in multiple applications. The subsequent sections describe e-device sensing platforms, explore their use, and outline existing limitations and future directions in device development. This work mainly focuses on optical and electrochemical methods of sensing, as these are at the forefront of e-device technology, while also addressing cutting-edge mechanochemical sensing and methods to optimize e-device data and technology via drift correction and calibration and computer modelling. Written for the highly successful .Methods in Molecular Biology. series, chapters include introductions to their respective topics, lists of the necessary materials, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.?.Authoritative and practical, .Biomimetic Sensing: Methods and Protocols. is an ideal guide for researchers working in a wide variety of fields with a desire to utilize this inexpensive and less specialized technology in their studies..
Pindex Book 2019
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https://doi.org/10.1057/9780230592841rom high-dimensional raw data and has been widely applied to computer vision, speech recognition, and natural language processing, though little has been reported in the field of olfaction. In this chapter, we describe the procedure to build a deep neural network to predict odor characteristics of chemicals from their mass spectra.
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