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Issue 15, 2020
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Ensuring high selectivity for preconcentration and detection of ultra-trace cadmium using a phage-functionalized metal–organic framework

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Abstract

Metal–organic frameworks functionalized with Cd(II) binding phages were for the first time fabricated for the isolation and preconcentration of ultra-trace cadmium. Highly specific Cd(II) binding phages were screened through biological panning from a phage display peptide library. Thereafter, chloroauric acid (HAuCl4) was reduced by the phages to provide phage-stabilized gold nanoparticles (AuNPs). The phage-AuNP networks were then assembled onto the metal–organic framework UiO66-NH2 to serve as an affinity probe for the selective recognition and isolation of ultra-trace cadmium. 2.0 μg L−1 Cd2+ was selectively captured by the derived UiO66-NH2@phage composite with an adsorption efficiency of 100%. UiO66-NH2@phage exhibits favorable anti-interference capability against the coexisting species. It ensures highly selective and sensitive quantification of ultra-trace cadmium with detection by graphite furnace atomic absorption spectrometry (GFAAS). An enrichment factor of 17.4 was obtained along with a limit of detection (LOD) of 3.9 ng L−1 within a linear range of 0.01–0.35 μg L−1. The procedure was further validated by analyzing cadmium content in a certified reference material (CRM, simulated water, GBW08608) and a series of environmental water samples. In general, the present study provides a new protocol for the development of novel adsorbents toward the target by biopanning to regulate the selectivity.

Graphical abstract: Ensuring high selectivity for preconcentration and detection of ultra-trace cadmium using a phage-functionalized metal–organic framework

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Supplementary files

Article information


Submitted
10 May 2020
Accepted
28 Jun 2020
First published
29 Jun 2020

Analyst, 2020,145, 5280-5288
Article type
Paper

Ensuring high selectivity for preconcentration and detection of ultra-trace cadmium using a phage-functionalized metal–organic framework

Y. Li, X. Wang, X. Liu, T. Yang, M. Chen and J. Wang, Analyst, 2020, 145, 5280
DOI: 10.1039/D0AN00944J

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