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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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

Gianmarco Gabrieli¹, Rui Hu¹, Keiji Matsumoto²

¹IBM Research Europe, Säumerstrasse 4, 8803 Rüschlikon, Switzerland.

Analytical Chemistry

|December 10, 2021

Summary

This summary is machine-generated.

This study introduces a novel machine learning approach for rapid, direct quantification of multiple ions in water using a polymeric sensor array. This method eliminates the need for a reference electrode, offering faster and potentially more accessible chemical analysis.

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Area of Science:

Analytical Chemistry
Materials Science
Machine Learning

Background:

Direct quantification of multiple ions in aqueous mixtures is challenging with traditional methods.
Miniaturized sensor arrays offer potential for portable and rapid analysis.
Conventional potentiometric measurements often require a reference electrode, adding complexity.

Purpose of the Study:

To develop and demonstrate a system for direct quantification of multiple ions in aqueous mixtures.
To integrate an automated machine learning pipeline with transient potentiometric data from a polymeric sensor array.
To obviate the need for a conventional reference electrode in ion quantification.

Main Methods:

Utilized a miniaturized array of 16 polymeric sensors electrodeposited on a printed circuit board (PCB) substrate.
Extracted features from transient differential voltages generated during solution transitions.
Employed a tree-based regression model for concentration determination.

Main Results:

Achieved direct quantification of metal cations (Al3+, Cu2+, Na+, Fe3+) in pure solutions in under 2 minutes.
Demonstrated a mean relative error ranging from 1% (Fe3+) to 44% (Na+) in a model mixture (1-10 mg/L).
Obtained an overall mean relative error of 16% for four ions across 124 tests (2-360 mg/L).

Conclusions:

The developed system leverages data-driven approaches for accelerated testing of multiselective sensor arrays.
This method can complement traditional analytical tools to meet industrial demands for chemical traceability.
The integration of machine learning with sensor arrays shows promise for advanced chemical analysis.