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<p>In the present study, a sensitive and selective electrochemical sensing scheme for cadmium (Cd²⁺) detection in water was developed, using citrate-capped platinum nanoparticles (PtNPs) on a gold (Au) sensing surface. While the PtNPs/Au platform enhances electron transfer and Cd2 + adsorption, the core innovation is the synergistic integration of Square Wave Voltammetry (SWV) with Partial Least Squares Regression (PLSR) chemometrics. This approach enables accurate Cd<sup>2+</sup> quantification despite complex matrix interferences common in groundwater. The sensor exhibited a linear range of 0.1–0.5 µM and a high sensitivity of 36.7 µA µM⁻¹. PLSR modelling demonstrated excellent predictive capability, R² = 0.99, correlation coefficient = 0.99, with a low Root Mean Square Error of Prediction (RMSEP) of 0.02, confirming its robustness. Successful application to real-world groundwater samples validated the methodology, revealing spatial Cd<sup>2+</sup> contamination patterns. This advanced combination of nanomaterial engineering, SWV, and multivariate chemometrics constitutes a significant step forward in reliable, trace-level heavy metal monitoring for environmental and public health protection.</p> Graphical abstract <p></p>

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PtNPs-enhanced electrochemical and chemometric approach for robust trace Cd2+ detection in water

  • 期刊论文

  • 发表日期：

    2026年3月20日

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  • Monika Antil,
  • Babankumar S. Bansod

摘要

In the present study, a sensitive and selective electrochemical sensing scheme for cadmium (Cd²⁺) detection in water was developed, using citrate-capped platinum nanoparticles (PtNPs) on a gold (Au) sensing surface. While the PtNPs/Au platform enhances electron transfer and Cd2 + adsorption, the core innovation is the synergistic integration of Square Wave Voltammetry (SWV) with Partial Least Squares Regression (PLSR) chemometrics. This approach enables accurate Cd2+ quantification despite complex matrix interferences common in groundwater. The sensor exhibited a linear range of 0.1–0.5 µM and a high sensitivity of 36.7 µA µM⁻¹. PLSR modelling demonstrated excellent predictive capability, R² = 0.99, correlation coefficient = 0.99, with a low Root Mean Square Error of Prediction (RMSEP) of 0.02, confirming its robustness. Successful application to real-world groundwater samples validated the methodology, revealing spatial Cd2+ contamination patterns. This advanced combination of nanomaterial engineering, SWV, and multivariate chemometrics constitutes a significant step forward in reliable, trace-level heavy metal monitoring for environmental and public health protection.

Graphical abstract