Field-effect-transistor based biosensors: a review of their use in environmental monitoring applications

被引:4
作者
Elli, Giulia [1 ]
Ciocca, Manuela [1 ]
Lugli, Paolo [1 ]
Petti, Luisa [1 ]
机构
[1] Free Univ Bolzano Bozen, Fac Sci & Technol, I-39100 Bolzano, Italy
来源
2021 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY (IEEE METROAGRIFOR 2021) | 2021年
关键词
IMMUNOSENSOR; PESTICIDES;
D O I
10.1109/MetroAgriFor52389.2021.9628685
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Biosensors are analytical devices used to detect the presence of a specific bio-chemical substance (called analyte), through the combination of a biorecognition element with a physico-chemical transducer. Biosensors using field-effect transistor (FET) transducing platforms (called Bio-FETs) exploit the variations of the current between a source and a drain terminal upon specific (physical or chemical) interactions in presence of the analyte of interest. While Bio-FETs have mainly been used in various fields of application such as medical diagnostics, biomolecule detection (like glucose) and food safety, environmental monitoring is still a quite unexplored area. In particular for agricultural applications, it is extremely important to develop real-time, portable, and low-cost monitoring devices for various types of pesticides, especially considering that nowadays many of these are considered environmental contaminants and are being banned. Only recently some studies have reported the development of Bio-FETs for the detection of a particular type of pesticides: herbicides. In this review, we revise the use of Bio-FETs for environmental monitoring applications. First we highlight the working principles of Bio-FETs, presenting the most commonly used material and the possible device configurations. Then we present the most relevant studies employing Bio-FETs for environmental monitoring. Finally, we show a quite new approach for Bio-FET, which is the coupling of biosensors with artificial intelligence and machine learning algorithms, to analyse complex data.
引用
收藏
页码:102 / 107
页数:6
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