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Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry

Author

Listed:
  • Tingting Hao

    (Ningbo University)

  • Huiqian Zhou

    (Ningbo University)

  • Panpan Gai

    (Qingdao Agricultural University)

  • Zhaoliang Wang

    (Ningbo University)

  • Yuxin Guo

    (Xi’an Jiaotong-Liverpool University)

  • Han Lin

    (Ningbo University)

  • Wenting Wei

    (Ningbo University)

  • Zhiyong Guo

    (Ningbo University)

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+ and/or Cu2+). In this work, an electrochemical sensor for sensitive quantitative detection of 0.4−4 amol L−1 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−1 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−1 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.

Suggested Citation

  • Tingting Hao & Huiqian Zhou & Panpan Gai & Zhaoliang Wang & Yuxin Guo & Han Lin & Wenting Wei & Zhiyong Guo, 2024. "Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54743-8
    DOI: 10.1038/s41467-024-54743-8
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    1. Vahid Ganjalizadeh & Gopikrishnan G. Meena & Thomas A. Wall & Matthew A. Stott & Aaron R. Hawkins & Holger Schmidt, 2022. "Fast custom wavelet analysis technique for single molecule detection and identification," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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