计算机科学
云计算
可穿戴计算机
物联网
蓝牙
产品(数学)
功能(生物学)
数据科学
纳米技术
无线
电信
万维网
嵌入式系统
进化生物学
生物
操作系统
数学
材料科学
几何学
作者
Michael Mayer,Antje J. Baeumner
出处
期刊:Chemical Reviews
[American Chemical Society]
日期:2019-05-09
卷期号:119 (13): 7996-8027
被引量:235
标识
DOI:10.1021/acs.chemrev.8b00719
摘要
The Internet of Things (IoT) is a megatrend that cuts across all scientific and engineering disciplines and establishes an integrating technical evolution to improve production efficiencies and daily human life. Linked machines and sensors use decision-making routines to work toward a common product or solution. Expanding this technical revolution into the value chain of complex areas such as agriculture, food production, and healthcare requires the implementation and connection of sophisticated (bio)analytical methods. Today, wearable sensors, monitors, and point-of-care diagnostic tests are part of our daily lives and improve patients’ medical progression or athletes’ monitoring capabilities that are already beyond imagination. Also, early contributions toward sensor networks and finally the IT revolution with wireless data collection and transmission via Bluetooth or smartphones have set the foundation to connect remote sensors and distributed analytical chemical services with centralized laboratories, cloud storage, and cloud computing. Here, we critically review those biosensor and chemosensor technologies and concepts used in an IoT setting or considered IoT-ready that were published in the period 2013–2018, while also pointing to those foundational concepts and ideas that arose over the last two decades. We focus on these sensors due to their unique ability to be remotely stationed and that easily function in networks and have made the greatest progress toward IoT integration. Finally, we highlight requirements and existing and future challenges and provide possible solutions important toward the vision of a seamless integration into a global analytical concept, which includes many more analytical techniques than sensors and includes foremost next-generation sequencing and separation principles coupled with MS detection.
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