Using Machine Learning to Design a FeMOF Bidirectional Regulator for Electrochemiluminescence Sensing of Tau Protein

作者：Wei Yuan, Tao Qin, Xuyuan Chen, Tianwen Liu, Jinmin Wang, Xiaoying Wang · 发表于：ACS Applied Materials & Interfaces · 年份：2025 · DOI：10.1021/acsami.4c18204 · 被引用次数：20 · 研究领域：Advanced biosensing and bioanalysis techniques、Electrochemical Analysis and Applications、Electrochemical sensors and biosensors

The single-luminophore-based ratiometric electrochemiluminescence (ECL) sensor coupling bidirectional regulator has become a research hotspot in the detection field because of its simplicity and accuracy. However, the limited bidirectional regulator hinders its further development. In this study, by leveraging the robust predictive capabilities of machine learning, we prepared an Fe-based metal–organic framework (FeMOF) as a bidirectional regulator for modulating the dual-emission ECL signals of a single luminophore for the first time. The proof of concept was demonstrated by applying FeMOF to the classical luminophore Ru(bpy) 3 2+, and the results showed its ability to enhance the cathode ECL signal ( E cathode ) and inhibit the anode ECL signal ( E anode ). As an example, a ratiometric ECL sensor for Tau protein (Tau) detection utilizing the FeMOF/Ru(bpy) 3 2+ system was developed. The incorporation of a bidirectional regulator in the ECL system effectively mitigated erratic fluctuations or minor discrepancies between the two signals and showed a stronger correlation and stability of E cathode / E anode than before regulation. As a result, the ECL sensor showed good analytical performance with a detection limit as low as 3.38 fg mL –1 (S/N = 3). Moreover, it was not only comparable in test results to the commercially available ELISA kit but also could well distinguish between normal and Alzheimer’s disease (AD) patients (80% specificity and 90% sensitivity). Thus, the propo...