Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry
Tingting Hao, Huiqian Zhou, Panpan Gai, Zhaoliang Wang, Yuxin Guo, Han Lin, Wenting Wei, Zhiyong Guo
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. Life activities are inextricably linked to the regulation of trace copper ions. Here, the authors report a deep learning-assisted electrochemical sensor for single-atom detection of copper ions based on click chemistry and fast scan voltammetry.