Design of a novel hybrid sensor with microelectrode array and LAPS for heavy metal determination using multivariate nonlinear calibration

Abstract

This paper presents a novel hybrid sensor array for heavy metal determination with self-calibration process by multivariate nonlinear regression (MNLR). Microelectrode array (MEA) and light addressable potentiometric sensor (LAPS) were synchronously fabricated on the same wafer by microfabrication technology. The electrochemical behavior of gold MEAs in different sizes was characterized by cyclic voltammetry. Meanwhile, the detection and quantification of zinc, lead and copper were conducted by differential pulse stripping voltammetry. The sensitivity of zinc, lead and copper are 36.3nA/ppb, 11.2nA/ppb and 4.6nA/ppb, respectively. The electrochemical performance of pH in LAPS was studied with sensitivity of 52.1mV/pH. The reproducibility and long-term stability were investigated in which relative standard deviation of merely 1.45% was observed and less than 4mV deviation was presented in a continuous test of 2h. The acidity of solutions influences the stripping process to great extent due to hydrogen evolution, hydrolysis of heavy metal and hydroxylation of the working electrode. Therefore, a self-calibration process through MNLR was carried out to calibrate the influence of pH and to enhance the detection performance based on the outcomes of heavy metal in MEA and pH in LAPS. The results validate good capability of the hybrid sensor array for trace heavy metal analysis.