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Machine learning-assisted Te–CdS@Mn3O4 nano-enzyme induced self-enhanced molecularly imprinted ratiometric electrochemiluminescence sensor with smartphone for portable and visual monitoring of 2,4-D

电化学发光 鲁米诺 分子印迹聚合物 检出限 材料科学 阳极 阴极 电化学 化学 纳米技术 分析化学(期刊) 选择性 电极 色谱法 催化作用 有机化学 物理化学
作者
Zhiwei Lu,Shijie Dai,Tao Liu,Jun Yang,Mengmeng Sun,Wu Chun,Gehong Su,Xianxiang Wang,Hanbing Rao,Huadong Yin,Xinguang Zhou,Jianshan Ye,Yanying Wang
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:222: 114996-114996 被引量:30
标识
DOI:10.1016/j.bios.2022.114996
摘要

Here, a novel and portable machine learning-assisted smartphone-based visual molecularly imprinted ratiometric electrochemiluminescence (MIRECL) sensing platform was constructed for highly selective sensitive detection of 2,4-Dichlorophenoxyacetic acid (2,4-D) for the first time. Te doped CdS-coated Mn3O4 (Te-CdS@Mn3O4) with catalase-like activity served as cathode-emitter, while luminol as anode luminophore accompanied H2O2 as co-reactant, and Te-CdS@Mn3O4 decorated molecularly imprinted polymers (MIPs) as recognition unit, respectively. Molecular models were constructed and MIP band and binding energies were calculated to elucidate the luminescence mechanism and select the best functional monomers. The peroxidase activity and the large specific surface area of Mn3O4 and the electrochemical effect can significantly improve the ECL intensity and analytical sensitivity of Te-CdS@Mn3O4. 2,4-D-MIPs were fabricated by in-situ electrochemical polymerization, and the rebinding of 2,4-D inhibits the binding of H2O2 to the anode emitter, and with the increase of the cathode impedance, the ECL response of Te-CdS@Mn3O4 decreases significantly. However, the blocked reaction of luminol on the anode surface also reduces the ECL response. Thus, a double-reduced MIRECL sensing system was designed and exhibited remarkable performance in sensitivity and selectivity due to the specific recognition of MIPs and the inherent ratio correction effect. Wider linear range in the range of 1 nM-100 μM with a detection limit of 0.63 nM for 2,4-D detection. Interestingly, a portable and visual smartphone-based MIRECL analysis system was established based on the capture of luminescence images by smartphones, classification and recognition by convolutional neural networks, and color analysis by self-developed software. Therefore, the developed MIRECL sensor is suitable for integration with portable devices for intelligent, convenient, and fast detection of 2,4-D in real samples.
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