Paper Sensor Modified with MoS2 for Detection of Dopamine Using a Machine-Intelligent Web App Interface

The sensing behavior of a MoS2-functionalized paper sensor towards dopamine was explored through a combinatorial approach of theoretical analysis, subsequent experimental validation, and machine-learning-driven predictive modeling of the measured electrochemical outputs. The suitability of the chosen 2D material for efficient detection of dopamine was confirmed using density functional theory. The physisorption behavior along with electrostatic interaction due to the incorporation of dopamine on MoS2 was unraveled under the purview of theoretically estimated noncovalent interaction and charge density difference plot. The theoretical Lo''wdin population analysis elucidates the alteration in oxidation potential of dopamine, as observed in electrochemical experiments. The electrochemical responses of the developed sensor with the spiked serum samples showed an average accuracy of more than 96% with a limit of detection of 10 nM. Furthermore, implementation of a machine-intelligent interactive web app interface improved the resolution of the sensing platform significantly with an enhanced accuracy of nearly 99%.