深度学习辅助铜单原子纳米酶传感器的制备及在汗液检测中的应用
A deep learning-based Cu SAzymes sensor ：Preparation and their application in sweat monitoring
摘要
以Cu-ZIF-8为前驱体，通过高温热解策略成功制备了铜单原子纳米酶Cu SAzymes。采用AC-HAADF-STEM表征证实了铜元素以原子级尺寸分散于材料中。酶促动力学研究表明，催化H₂O₂的米氏常数Km为0.31 mM，催化TMB的Km为0.49 mM，体现了Cu SAzymes高效的POD酶活性。在此基础上，将Cu SAzymes与GOx耦合，构建了用于汗液葡萄糖检测的级联催化比色方法。该方法的检测限低至0.06 mM，具有灵敏度高和选择性良好的优势。为实现智能化检测，引入LSTM神经网络深度学习，通过对反应体系颜色图像与葡萄糖浓度之间关系的训练，实现了汗液中葡萄糖的高灵敏度检测。
Abstract
Using Cu-ZIF-8 as a precursor, copper single-atom nanozymes (Cu SAzymes) were successfully prepared through a high-temperature pyrolysis strategy. AC-HAADF-STEM characterization confirmed that copper elements were dispersed at the atomic level within the material. Enzyme-catalyzed kinetic studies showed that the Michaelis constant Km for catalyzing HO was 0.31 mM, and the Km for catalyzing TMB was 0.49 mM, demonstrating the high POD enzyme activity of Cu SAzymes. On this basis, Cu SAzymes were coupled with GOx to construct a cascade catalytic colorimetric method for sweat glucose detection. The detection limit of this method was as low as 0.06 mM, offering advantages of high sensitivity and good selectivity. To achieve intelligent detection, an LSTM neural network deep learning approach was introduced, where the relationship between the color images of the reaction system and glucose concentration was trained to achieve high-sensitivity detection of glucose in sweat.关键词
Cu SAzymes/比色检测/葡萄糖/深度学习Key words
Cu SAzymes/Colorimetry/Glucose/Deep learning引用本文复制引用
沙姝媛,单桂晔.深度学习辅助铜单原子纳米酶传感器的制备及在汗液检测中的应用[EB/OL].(2026-05-21)[2026-05-24].https://chinaxiv.org/abs/202605.00180.学科分类
医学研究方法/计算技术、计算机技术
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