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Enzyme Method-Based Microfluidic Chip for the Rapid Detection of Copper Ions.
Yin, Binfeng; Wan, Xinhua; Qian, Changcheng; Sohan, A S M Muhtasim Fuad; Zhou, Teng; Yue, Wenkai.
Afiliação
  • Yin B; School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
  • Wan X; School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
  • Qian C; School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
  • Sohan ASMMF; School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
  • Zhou T; Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China.
  • Yue W; School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
Micromachines (Basel) ; 12(11)2021 Nov 10.
Article em En | MEDLINE | ID: mdl-34832792
ABSTRACT
Metal ions in high concentrations can pollute the marine environment. Human activities and industrial pollution are the causes of Cu2+ contamination. Here, we report our discovery of an enzyme method-based microfluidic that can be used to rapidly detect Cu2+ in seawater. In this method, Cu2+ is reduced to Cu+ to inhibit horseradish peroxidase (HRP) activity, which then results in the color distortion of the reaction solution. The chip provides both naked eye and spectrophotometer modalities. Cu2+ concentrations have an ideal linear relationship, with absorbance values ranging from 3.91 nM to 256 µM. The proposed enzyme method-based microfluidic chip detects Cu2+ with a limit of detection (LOD) of 0.87 nM. Other common metal ions do not affect the operation of the chip. The successful detection of Cu2+ was achieved using three real seawater samples, verifying the ability of the chip in practical applications. Furthermore, the chip realizes the functions of two AND gates in series and has potential practical implementations in biochemical detection and biological computing.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article