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基于光谱的生物传感器中的生物识别技术用于-水和水污染物分析。

Bio-Recognition in Spectroscopy-Based Biosensors for -Water and Waterborne Contamination Analysis.

机构信息

Institute for Microelectronics and Microsystems (IMM), CNR, Via Monteroni, 73100 Lecce, Italy.

Mathematics and Physics "E. De Giorgi" Department, University of Salento, Via Monteroni, 73100 Lecce, Italy.

出版信息

Biosensors (Basel). 2019 Jul 30;9(3):96. doi: 10.3390/bios9030096.

Abstract

Microsystems and biomolecules integration as well multiplexing determinations are key aspects of sensing devices in the field of heavy metal contamination monitoring. The present review collects the most relevant information about optical biosensors development in the last decade. Focus is put on analytical characteristics and applications that are dependent on: (i) Signal transduction method (luminescence, colorimetry, evanescent wave (EW), surface-enhanced Raman spectroscopy (SERS), Förster resonance energy transfer (FRET), surface plasmon resonance (SPR)); (ii) biorecognition molecules employed (proteins, nucleic acids, aptamers, and enzymes). The biosensing systems applied (or applicable) to water and milk samples will be considered for a comparative analysis, with an emphasis on water as the primary source of possible contamination along the food chain.

摘要

微系统与生物分子的集成以及多重检测是重金属污染监测领域中传感设备的关键方面。本综述收集了过去十年中关于光学生物传感器发展的最相关信息。重点介绍了依赖以下因素的分析特性和应用:(i)信号转导方法(发光、比色法、消逝波(EW)、表面增强拉曼光谱(SERS)、Förster 共振能量转移(FRET)、表面等离子体共振(SPR));(ii)所采用的生物识别分子(蛋白质、核酸、适体和酶)。将考虑应用(或可应用)于水样和奶样的生物传感系统进行比较分析,重点关注水作为食物链中可能污染的主要来源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da40/6784378/c7e6500af22b/biosensors-09-00096-g001.jpg

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