Combining an Integrated Sensor Array with Machine Learning for the Simultaneous Quantification of Multiple Cations in Aqueous Mixtures

Abstract

The direct quantification of multiple ions in aqueous mixtures is achieved by combining an automated machine learning pipeline with transient potentiometric data obtained from a single miniaturized array of polymeric sensors electrodeposited on a conventional printed circuit board (PCB) substrate. A proof-of-concept system was demonstrated by employing 16 polymeric sensors in combination with features extracted from the transient differential voltages produced by these sensors when transitioning from a reference solution to a test solution, thereby obviating the need for a conventional reference electrode. A tree-based regression model enabled concentrations of various metal cations in pure solutions to be determined in less than 2 min. In a model mixture comprising Al3+, Cu2+, Na+, and Fe3+, the mean relative error was found to depend on the type of ion and varied between 1% for Fe3+ and 44% for Na+ in the concentration range 1-10 mg/L. Overall, a mean relative error of 16% was obtained for quantification of these four ions across a total of 124 tests in different solutions spanning concentrations between 2 and 360 mg/L. These results demonstrate how the analytical capability of a multiselective sensor array can leverage data-driven approaches through training by examples for accelerated testing and can be proposed to complement traditional analytical tools to meet industrial demands, including traceability of chemicals.