Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic...

Area of Science:

• Biomedical Engineering
• Materials Science
• Analytical Chemistry

Background:

• Small-molecule detection is crucial for health monitoring and early disease diagnosis.
• Current point-of-care (PoC) technologies face challenges in detecting specific markers in complex biological samples.

Purpose of the Study:

• To develop a novel PoC device for detecting hippuric acid (HA), a key biomarker for various health conditions.
• To integrate molecularly imprinted polymers (MIPs), 2D material-based electrolyte-gated transistors (EGTs), and machine learning (ML) for enhanced analyte detection.

Main Methods:

• Utilized reduced graphene oxide (rGO) as the sensory material and MIPs as supramolecular receptors within EGTs.
• Employed supervised ML techniques, including symbolic regression and compressive sensing, for multivariate analysis of EGT transfer curves.
• Developed a PoC device for hippuric acid detection in artificial urine.

Main Results:

• The device demonstrated low operating voltages (<0.5 V) and rapid response times (≤10 s).
• Achieved a wide detection range (0.05–200 nmol L⁻¹) with a low limit of detection (LoD) of 39 pmol L⁻¹.
• ML-based multivariate analysis increased device sensitivity by 2.5-fold compared to human data analysis.

Conclusions:

• The developed method offers an accurate and sensitive approach for small-molecule detection in complex media using PoC technologies.
• The combination of MIPs, 2D EGTs, and ML represents a significant advancement for point-of-care diagnostics.