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Titlebook: Materials for Chemical Sensing; Thiago Regis Longo Cesar Paix?o,Subrayal Medapati Book 2017 Springer International Publishing AG 2017 Bio

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發(fā)表于 2025-3-21 19:51:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Materials for Chemical Sensing
編輯Thiago Regis Longo Cesar Paix?o,Subrayal Medapati
視頻videohttp://file.papertrans.cn/626/625835/625835.mp4
概述Describes new materials used for development of analytical devices.Details development of new analytical techniques that contribute to the design of new chemical sensors.Examines wearable materials us
圖書封面Titlebook: Materials for Chemical Sensing;  Thiago Regis Longo Cesar Paix?o,Subrayal Medapati  Book 2017 Springer International Publishing AG 2017 Bio
描述This book covers new materials used as analytical devices for increasing the interactions between the development of new analytical devices and materials science. The authors describe how different types of materials such as polymers, self-assembled layers, phthalocyanines, and nanomaterials can further enhance sensitivity and promote selectivity between analytes for different applications. They explain how continuing research and discussion into materials science for chemical sensing is stimulating the search for different strategies and technologies that extract information for these chemical sensors in order to obtain a chemical fingerprint of samples.
出版日期Book 2017
關(guān)鍵詞Bioanalytical Devices; Biomimetic Sensors; Chemometrics and Sensors; Implantable Sensors; Nanomaterials;
版次1
doihttps://doi.org/10.1007/978-3-319-47835-7
isbn_softcover978-3-319-83835-9
isbn_ebook978-3-319-47835-7
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Nipapan Ruecha,Kentaro Yamada,Koji Suzuki,Daniel Citteriondle different services into different set-chains. These set-chains share data with each other through an elaborate access control model. Experimental results show that the proposed model fits the characteristics of the IoT data management, guaranteeing the privacy and security requirements with bet
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Subrayal Medapati Reddyich may open the way to a range of exploits. These JavaScript codes are implicitly loaded by 92.3% of Android apps. Using VirusTotal, we classify 1.18% of third-party resources as suspicious. Our observations raise concerns for how apps are currently developed, and suggest that more rigorous vetting
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Roger Jagdish Narayan,Nishant Vermatity extraction, relation extraction, and rule extraction. It then conducts an intra-entity and inter-entity consistency checking over the generated knowledge graph. We implement RFCKG and apply it to the main RFC?(RFC7252) of CoAP, one of the most extensively used messaging protocols in IoT. Our ev
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Celina Massumi Miyazaki,Anerise de Barros,Daniela Branco Tavares Mascagni,Juliana Santos Gra?a,Paulal time. In this paper, a simple LEO satellite simulation platform is built to generate traffic adversarial samples using four classical adversarial sample generation methods, and a two-classification deep learning model is trained to determine the effectiveness of the attack and defence. Experiments
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Debdyuti Mukherjee,Revanasiddappa Manjunatha,Srinivasan Sampath,Asim Kumar Rayrecent representative work published in the dominant time to investigate the emerging research. Also, we summarize the research methodology commonly adopted in this fast-growing area. In consonance with the phases of the research methodology, each paper that discovers IoT vulnerabilities is comprehe
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