Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39604355.
- Also identified by DOI 10.1038/s41467-024-54743-8 and PMC identifier 11603177.
- Licence recorded as CC BY-NC-ND.
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Abstract
Cell ion channels, cell proliferation and metastasis, and many other life activities are inseparable from the regulation of trace or even single copper ion (Cu<sup>+</sup> and/or Cu<sup>2+</sup>). In this work, an electrochemical sensor for sensitive quantitative detection of 0.4-4 amol L<sup>-1</sup> copper ions is developed by adopting: (1) copper ions catalyzing the click-chemistry reaction to capture numerous signal units; (2) special adsorption assembly method of signal units to ensure signal generation efficiency; and (3) fast scan voltammetry at 400 V s<sup>-1</sup> to enhance signal intensity. And then, the single-atom detection of copper ions is realized by constructing a multi-layer deep convolutional neural network model FSVNet to extract hidden features and signal information of fast scan voltammograms for 0.2 amol L<sup>-1</sup> of copper ions. Here, we show a multiple signal amplification strategy based on functionalized nanomaterials and fast scan voltammetry, together with a deep learning method, which realizes the sensitive detection and even single-atom detection of copper ions.