A frugal machine-intelligent paper sensor for quantification of glucose through standalone desktop application: A computational and experimental approach

Abstract

A paper-based electrochemical sensor utilizing MoS2 as sensing material is developed to quantitatively estimate glucose in a complex extracellular fluid matrix. The contribution of MoS2 as sensing material is substantiated in the context of density functional theory formalism. The molecular modelling of MoS2 and glucose interaction is elucidated through the theoretical estimation of non-covalent interactions and charge density difference plots. The charge transfer phenomenon during the oxidation of glucose molecule in electrochemical experiments is verified through electronic band structure analysis and frontier orbital theory. The electrochemical responses indicate that the sensor exhibits an average accuracy of nearly 96%, with a quantification limit of 100 nM. Furthermore, the chronoamperometric measurements highlight the superior sensitivity of the device in presence of various perturbing molecules. The obtained DPV responses were integrated with a robust machine learning-driven standalone app GluQuantify. This app precisely predicts the glucose concentrations in serum samples. The utilization of GluQuantify substantially elevates the overall accuracy of the sensing platform to 99% and automates the detection process. The application also enhances the resolution of the sensor to 0.01 µM, which mitigates the sensor resolution-related issues in manual estimation.