Chapter 16 - Detection of triclosan and sodium lauryl sulfate in environmental samples and cosmetic product by electrochemical sensor based on biomimetic recognition combined with electronic nose

Abstract

Emerging water pollutants, such as cosmetic contaminants, are suspected of causing adverse effects on human health. These molecules have become a concern because of their increasingly high concentrations in surface waters. Despite this alarming situation, there is little data on actual concentrations in the environment, as they are not routinely monitored or regulated. This situation is further aggravated by the lack of portable and reliable methods for their determination in the field. The development of an electrochemical and miniaturized sensors for their detection is a promising solution to these problems. This chapter describes technical developments of electrochemical sensors based on biomimetic recognition combined with the electronic nose (e-nose) and its applications for environmental monitoring and cosmetic product. The proposed electrochemical tools are based on a molecularly imprinted polymer (MIP) for the detection of two molecules, triclosan (TCS) and sodium lauryl sulfate (SLS). The purpose of this chapter is to provide some detailed fabrication steps of the electrochemical sensor, which were followed by using electrochemical studies (cyclic voltammetry, differential pulse voltammetry, and electrochemical impedance spectroscopy), and these techniques were applied to characterize the binding interactions. Scanning electron microscopy, atomic force microscopy, and Fourier transform infrared spectroscopy were performed to investigate the changes formed on the surface morphology and electrode surface structure. The optimization of the experimental parameters and the analytical performance of the proposed sensor was widely evaluated. For TCS detection, a limit of detection (LOD) of 0.23pg/mL was reached over a working range of 0.1–1000pg/mL. In a wide linear range (0.1–1000pg/mL), an LOD (0.18pg/mL) was attained for the SLS sensor. Good correlation was obtained by using partial least squares regression (PLS-R) between the MIP sensors and the spectrophotometry as a validation method for the practical detection of both species in wastewater and cosmetics samples. Simultaneously, radar plots constructed from the e-nose responses show different chemical signatures depending on the samples analyzed. The results from both detection systems were used to develop a model using the PLS-R method with a correlation coefficient R=0.98.