Neural networks based fluorescence and electrochemistry dual-modal sensor for sensitive and precise detection of cadmium and lead simultaneously

灵敏度(控制系统) 荧光 情态动词 电化学 对偶(语法数字) 生物系统 人工神经网络 干扰(通信) 灵活性(工程) 材料科学 计算机科学 化学 环境科学 物理 人工智能 电子工程 数学 工程类 统计 电信 光学 艺术 频道(广播) 物理化学 文学类 生物 冶金 电极 高分子化学
作者
Xinyi Wang,Wencheng Lin,Changming Chen,Liubing Kong,Zhuoru Huang,Dmitry Kirsanov,Andrey Legin,Hao Wan,Ping Wang
出处
期刊:Sensors and Actuators B-chemical [Elsevier BV]
卷期号:366: 131922-131922 被引量:23
标识
DOI:10.1016/j.snb.2022.131922
摘要

Heavy metals are harmful and it’s meaningful to achieve co-detection. In this work, fluorescence (FL) and electrochemistry (EC) dual-modal sensors combined with neural networks are proposed to detect cadmium (Cd2+) and lead (Pb2+) without pretreatment for the first time. Dual-modal sensing eliminates individual limitations of FL and EC and combines their superiority. Quantum dots and sea urchin-like FeOOH are used as sensitive materials, among which FeOOH is used for the first time to detect Pb2+ with high repeatability and sensitivity. Combining with the proposed neural networks, the mean absolute error of Cd2+ and Pb2+ predicted are 0.2176 μg/L and 0.6002 μg/L, respectively, which are far better than traditional analysis methods. The R-Squared between the predicted value and the true value is 0.974 (Cd2+) and 0.999 (Pb2+), respectively, which verifies the feasibility of the designed sensor. This model eliminates the mutual interference between Cd2+ and Pb2+ based on the synergistic effect and can be used for low-level detection in water samples with complex background. In addition, the designed model could combine with other types of sensors to accurately monitor global-local waters. It also provides new ideas for data fusion, which expands the flexibility in environmental protection and health care.
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