Application of multi-variable calibration model of electrochemical sensor in high-accuracy long-term monitoring of cadmium ions

Abstract

Background

As the issue of heavy metal pollution has become increasingly prominent, the electrochemical detection of trace heavy metal ions in the marine environment has emerged as a significant and commonly utilized method. The detection of heavy metal ions has been a gradual replacement of traditional methods with electrochemical detection. However, the temperature and pH of seawater vary with time and location, leading to biases in the detection results of conventional ion-selective membrane sensors that only focus on the relationship between concentration and voltage.

Results

This paper treats an experimental and modeling study to predict the electrochemical performance of ion-selective membrane sensors under varying temperatures and pH. By investigating the relationship between the working electrode response potential, pH, temperature, and cadmium ion concentration, a multivariate self-calibration model including pH, T, and the logarithm of cadmium ion concentration as independent variables was constructed. The determination coefficient R² of the model was 0.9997, indicating a good fit. Automatic correction of electrochemical sensor minimizes the impact of temperature and pH, ensuring accurate readings of cadmium ion concentration during long-term monitoring. In addition, the recovery rates of Cd(II) concentration measurements were 113% (coexisting with Na(I)) and 121% (coexisting with Cu(II)), indicating good resistance to interference. Accuracy tests showed recovery rates ranging from 98% to 121%, demonstrating the good accuracy.

Significance

Therefore, the ion-selective membrane sensors can reduce the interference of temperature and pH on the measurement of heavy metal ion concentrations through the multivariate self-calibration model. This study provides a reliable detection method for addressing the issue of heavy metal pollution in water bodies and has a broad application prospect.