Carbon dot-based colorimetric sensor array for the discrimination of different water samples

文献情報

出版日 2019-10-11

DOI 10.1039/C9AY01439J

インパクトファクター 2.896

著者

Masoud Shariati-Rad, Zahra Ghorbani

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要旨

Herein, a colorimetric sensor array has been introduced which uses carbon dots (CDs) to distinguish between different water samples. The basis of the method is that CDs interact differently with heavy metal ions in the presence of different water samples. The color change resulting from this interaction is recorded by a digital camera, and RGB values are analyzed by unsupervised and supervised chemometric methods, such as hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). To increase the variables in the designed array, instead of using different CDs, the interaction of a single CD with different heavy metal ions and at different concentration levels is employed. The efficiency of the colorimetric sensor array in discriminating between deionized water and three real water samples was excellent. In addition to efficiency, the designed colorimetric sensor array is simple and inexpensive.

掲載誌

Analytical Methods

CiteScore: 5.1

自己引用率: 3.7%

年間論文数: 655

Analytical Methods welcomes early applications of new analytical and bioanalytical methods and technology demonstrating the potential for societal impact. We require that methods and technology reported in the journal are sufficiently innovative, robust, accurate, and compared to other available methods for the intended application. Developments with interdisciplinary approaches are particularly welcome. Systems should be proven with suitably complex and analytically challenging samples. We encourage developments within, but not limited to, the following technologies and applications: global health, point-of-care and molecular diagnostics biosensors and bioengineering drug development and pharmaceutical analysis applied microfluidics and nanotechnology omics studies, such as proteomics, metabolomics or glycomics environmental, agricultural and food science neuroscience biochemical and clinical analysis forensic analysis industrial process and method development