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Portable paper sensors for the detection of heavy metals based on light transmission-improved quantification of colorimetric assays

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Abstract

An accurate quantification method with a wide linearity range is paramount for the development of low-cost, portable and point-of-care sensors. This work reports a new approach to analyze the colorimetric assays on paper-based sensors using the quantification from a light transmission method. Compared to the commonly-developed color intensity measurement on scanned digital images, a portable transmission densitometer is capable of directly quantifying the optical density of colorimetric results. The detection of heavy metals in an aqueous system, including Fe(II), Cu(II), and Ni(II), was carried out to demonstrate the good performance and reliability of this method. Our measurements show that the linear quantification range spans from 0.5–500 mg L−1 for the assays of Cu(II) and Fe(II) and from 2–500 mg L−1 for Ni(II) based on the reading of transmitted light through the assay spot. As a comparison, the linear range is restricted to 0.5–50 mg L−1 for the same assays when analysed by the common reflection method, suggesting a significant improvement in the accuracy and sensitivity of high analyte concentrations from the light transmission method. By expanding the linearity range, this method further streamlines the sampling procedure during analysis and will greatly advance the future development of paper-based analytical sensors.

Graphical abstract: Portable paper sensors for the detection of heavy metals based on light transmission-improved quantification of colorimetric assays

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Publication details

The article was received on 05 Jul 2019, accepted on 06 Sep 2019 and first published on 06 Sep 2019


Article type: Paper
DOI: 10.1039/C9AN01131E
Analyst, 2019, Advance Article

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    Portable paper sensors for the detection of heavy metals based on light transmission-improved quantification of colorimetric assays

    J. Wu, M. Li, H. Tang, J. Su, M. He, G. Chen, L. Guan and J. Tian, Analyst, 2019, Advance Article , DOI: 10.1039/C9AN01131E

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