Construction of an Electrochemical Impedance Spectroscopy Matching Method Based on Adaptive Multi-Error Driving and...

Electrochemical impedance spectroscopy (EIS) is a technique used to analyze the kinetics and interfacial processes of electrochemical systems. The selection of an appropriate equivalent circuit model for EIS interpretation was traditionally reliant on expert experience, rendering the process subjective and prone to error. To address these limitations, an automated framework for both model selection and parameter estimation was proposed. The methodology was structured such that initial model screening was performed by a global heuristic search algorithm, adaptive optimization was guided by an integrated XGBoost-based error feedback mechanism, and precise parameter estimation was achieved using a Differential Evolution-Levenberg-Marquardt (DE-LM) algorithm. When evaluated on a purpose-built dataset comprising 4.8 × 10⁵ spectra across diverse circuit and biofilm scenarios, a model classification accuracy of 96.32% was achieved, and a 72.3% reduction in parameter estimation error was recorded. The practical utility of the method was validated through the quantitative analysis of bovine serum albumin-Clenbuterol hydrochloride (BSA-CLB), wherein an accuracy of 95.2% was demonstrated and a strong linear correlation with target concentration (R² = 0.999) was found. Through this approach, the limitations of traditional black-box models were mitigated by resolving the physical meaning of parameters. Consequently, the automated and quantitative monitoring of processes such as biofilm formation was facilitated, enabling the efficient evaluation of antimicrobial drugs or anti-fouling coatings.