Robust and reliable quantitative method to correct for solution chemistry interferences in molecularly imprinted polymer – electrochemical sensor (MIP-ES) measurements of complex environmental water matrices

The goal of this research is to develop a robust and reliable quantitative method to correct for solution chemistry interferences in MIP-ES measurements of complex environmental water matrices. One of the challenges using MIP-ES to measure contaminants such as per- and polyfluoroalkyl substances (PFAS) in environmental samples is understanding how bulk solution chemistry contributes to the electrochemical background signal. The development of a ratiometric comparison approach with MIP-ES will involve bench scale proof of concept testing and then escalate to field deployed trials. Instrumentation required for MIP-ES fabrication and sensing includes multichannel potentiostats capable of differential pulse voltammetry and cyclic voltammetry. Differential pulse voltammetry will be used to collect current differences at 1-minute and 15-minute incubation time points in the sample matrices. These current values will be used to build the ratiometric comparison approach.

The ratiometric approach will involve the comparison of normalized current difference ratios, outlined in Equation 1, between a matrix matched solution that contains all matrix constituents minus the analyte in question and an unknown solution that is composed of the sample matrix and may or may not contain the analyte.

The use of a ratiometric comparison isolates current differences from the association of the analyte with the MIP-ES binding sites from any change in current from interferent effects due to the rest of the sample matrix constituents (i.e., humic acids, fulvic acids, ions, non-fluorinated counterparts, etc.). A ratio close to 1 indicates the absence of the analyte due to similar normalized current difference values in the ratio. A statistically different ratio from 1 indicates the presence of the analyte in question in the sample matrix.

This project supports the administration's priority to protecting human health by generating data and tools that identify potential risks to drinking water quality. By enabling early detection and informed decision-making, this work helps safeguard communities from exposure to harmful contaminants and ensures access to safe, clean drinking water.