Explainable artificial intelligence-enhanced dual-mode electrochemical sensor for online monitoring of dimethoate

Electrochemical impedance spectroscopy (EIS) uses small alternating-current perturbations to probe charge-transfer and mass-transport processes across frequencies. The physical mechanisms underlying EIS responses are governed by the measurement frequency range: high-frequency responses reflect rapid charge-transfer kinetics, whereas low-frequency signals reveal diffusion-controlled mass-transport processes. Leveraging machine learning to directly interpret these multiscale electrochemical signatures, this study reports an intelligent dual-mode sensing platform that bypasses conventional circuit-fitting workflows and enables sensitive detection of the organophosphate dimethoate. A composite gold nanoparticle/graphene (AuNPs/GR) interface enhances conductivity and electroactive surface area accelerate electron transfer and reduces the charge-transfer resistance (Rct), creating an optimal microenvironment for acetylcholinesterase (AChE) biocatalysis. Molecular docking revealed potential Au-S interactions between AChE and gold nanoparticles and supported the binding of dimethoate at the enzyme's active site. A Tabular Prior Data Fitted Network-based machine-learning strategy optimized the analytical conditions. By integrating differential pulse voltammetry with EIS and developing a Bayesian-optimized Extreme Gradient Boosting for the latter, the model directly predicts Rct from raw EIS data and achieves full decision transparency through Shapley additive explanations. This strategy avoids labor-intensive circuit fitting and enables automated analysis. The dual-mode sensor delivers a wide linear range, good selectivity, reliable precision, and strong recovery in real samples, not only offering a new paradigm for next-generation Point-of-Care Testing, but also demonstrating the potential of integrating advanced machine-learning techniques into electrochemical analysis.