Revolutionizing cesium monitoring in seawater through electrochemical voltammetry and machine learning

Abstract

Monitoring radioactive cesium ions (Cs⁺) in seawater is vital for environmental safety but remains challenging due to limitations in the accessibility, stability, and selectivity of traditional methods. This study presents an innovative approach that combines electrochemical voltammetry using nickel hexacyanoferrate (NiHCF) thin-film electrode with machine learning (ML) to enable accurate and portable detection of Cs⁺. Optimizing the fabrication of NiHCF thin-film electrodes enabled the development of a robust sensor that generates cyclic voltammograms (CVs) sensitive to Cs⁺ concentrations as low as 1 ppb in synthetic seawater and 10 ppb in real seawater, with subtle changes in CV patterns caused by trace Cs⁺ effectively identified and analyzed using ML. Using 2D convolutional neural networks (CNNs), we classified Cs⁺ concentrations across eight logarithmic classes (0 - 10⁶ ppb) with 100 % accuracy and an F1-score of 1 in synthetic seawater datasets, outperforming the 1D CNN and deep neural networks. Validation using real seawater datasets confirmed the applicability of our model, achieving high performance. Moreover, gradient-weighted class activation mapping (Grad-CAM) identified critical CV regions that were overlooked during manual inspection, validating model reliability. This integrated method offers sensitive and practical solutions for monitoring Cs⁺ in seawater, helping to prevent its accumulation in ecosystems.

Keywords: Cesium; Convolutional neural network; Cyclic voltammetry; Deep learning; Electrochemistry.