Akademik Veri Yönetim Sistemi
Materials Science and Engineering: B, cilt.308, 2024 (SCI-Expanded, Scopus)
The escalating levels of environmental pollution, particularly from commercially used products, highlight the imperative of efficient sensor technologies to facilitate timely and effective remediation strategies. Herein, a simple method is proposed to enhance the electrochemical performance of CoWO4 structures by coupling them with activated carbon (AC) for direct tetrabromobisphenol A (TBPA) oxidation. The systematic comparison shows that incorporation of AC with CoWO4 not only improves the overall effective surface area (ESA) of the catalyst by 1.8-fold but also improves the cyclic voltammetry (CV) based irreversible oxidation current by 2.0-fold owing to improved conductivity and surface characteristics. Differential pulse voltammetry (DPV) based optimization of the sensory characteristics confirmed robust electrocatalytic TBPA oxidation within a 0.1 to 1.0 µM concentration range, achieving a 0.024 µM detection limit within PBS (0.1 M) (pH 5.0). Moreover, density functional theory (DFT) analysis confirms the non-covalent interaction favorability between TBPA and CoWO4 surface, validating TBPA's easy adsorption onto the catalytic surface and, thus, facilitated electron mobility between the molecule and the catalyst during the surface oxidation process. The DPV sensing interpreted using machine learning (ML) algorithms confirmed the detection accuracy of the developed sensor. Among the adopted models, artificial neural networks (ANN) achieved the highest R2 score (0.9659) and the lowest values across Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) error matrices confirming its suitability in deciphering DPV correlations. Integrating ANN with DPV based electrocatalytic oxidation sensing underscores machine learning's potential in analyzing complex electrochemical signatures, thus advancing intelligent sensing for precise environmental monitoring.