Cross-Attention Feature Fusion network for robust estimation of Cd2+ and Pb2+ in water samples using Cyclic Voltammetry

Voltammetry is a promising technique for estimating heavy metal pollution such as Cadmium (Cd 2 + ) and Lead (Pb 2 + ) in water. Its advantages include rapid analysis and cost-effectiveness over established methods like Atomic Absorption Spectroscopy (AAS) and Inductively Coupled Plasma - Mass Spectrometry (ICP-MS). However, current analysis often depends only on peak data, ignoring the rest of the voltammetric signal which may contain useful information that could potentially improve measurement accuracy. To address this limitation, the Cross-Attention Feature Fusion (CAFF) network is proposed to analyze Cyclic Voltammetry (CV) signals acquired using a 3-electrode setup with a Glassy Carbon Electrode (GCE) as the working electrode, Platinum as the counter, and Ag/AgCl as the reference. Unlike standard self-attention mechanisms or simple concatenation fusion methods, CAFF introduces a novel dual-stream architecture that dynamically captures the inter-dependencies between raw CV signals and extracted peak data—an approach previously unexplored in electrochemical sensing. The model integrates an Improved Beluga Whale Optimization (IBWO) algorithm that automatically determines the optimal hyperparameters, resulting in a more robust model. Robustness was assessed using Chemically-Informed Degradation Simulation (CIDS). As a result, the proposed CAFF-IBWO model demonstrated superior performance, achieving R 2 values of 0.97 for Cd 2 + and 1.00 for Pb 2 + . It also significantly reduced the Mean Absolute Percentage Error (MAPE) by 65.79% for Cd 2 + and 72.50% for Pb 2 + compared to single-input attention networks. Furthermore, CAFF-IBWO exhibited remarkable resilience against signal degradation, maintaining stable prediction performance across varying noise conditions. While the study focuses specifically on Cd 2 + and Pb 2 + and requires further validation for broader generalization, the demonstrated performance is highly promising. These findings underscore the model’s potential for real-world environmental sensing applications. • Proposed CAFF network accurately estimates Cd 2 + and Pb 2 + concentrations. • Improved Beluga Whale Optimization (IBWO) automates optimal hyperparameter search. • Chemically-Informed Degradation Simulation (CIDS) ensures model robustness. • CAFF-IBWO achieves superior R 2 of 0.97 (Cd 2 + ) and 1.00 (Pb 2 + ). • Prediction errors (MAPE) are reduced by up to 72.50% using this model.