A machine learning strategy-incorporated BiFeO₃/Ti₃C₂ MXene electrochemical platform for simple, rapid detection of...

The electrochemical technique has been increasingly used for the detection of heavy metal ions in the water system. However, the process for determining the optimum experimental conditions was cumbersome, time-consuming, and unsynchronized, resulting in unsatisfactory detection efficiency. Herein, a new machine learning (ML) strategy combined with BiFeO₃/Ti₃C₂ MXene (BiFeO₃/MXene) was used to fabricate a simple but efficient electrochemical Pb²⁺ sensor. The interconnected BiFeO₃/MXene composites prepared by a hydrothermal method possessed an interconnected conductive framework, abundant active sites, and a large surface area, which gave them excellent electronic conductivity and high accumulation of Pb²⁺. Meanwhile, ML methods such as back-propagation artificial neural network (BPANN) and genetic algorithm (GA) combined with orthogonal experimental design (OED) were used to optimize sensor parameters such as the pH of the supporting electrolyte, the BiFeO₃/MXene content, deposition potential, and deposition time. Compared with OED and the one factor at a time (OFAT) methods, the OED-ML method greatly simplified the experimental procedures and improved the electrochemical detection performance. The developed sensor showed superior detection performance for Pb²⁺ with a detection limit of 0.0001 μg L^-1 using the OED-ML method, which was much lower than that of the OED and OFAT methods (0.0003 μg L^-1). In addition, the sensor showed good repeatability, reproducibility, stability, and interference capability. The feasibility of the method was verified by detecting Pb²⁺ in lake samples with recoveries ranging from 98.79% to 101.3%. To our knowledge, the ML strategy was introduced for the first time in an electrochemical sensor for Pb²⁺ detection, which proved the feasibility and practicality of ML.