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

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Dopamine detection is crucial for diagnosing neurological disorders.
• Existing methods often lack sensitivity or require complex sample preparation.
• Molybdenum disulfide (MoS2) shows promise as a 2D material for electrochemical sensing.

Purpose of the Study:

• To develop and validate a MoS2-functionalized paper sensor for sensitive dopamine detection.
• To investigate the sensing mechanism using theoretical and experimental approaches.
• To enhance sensor performance with machine learning integration.

Main Methods:

• Density Functional Theory (DFT) for theoretical analysis of MoS2-dopamine interaction.
• Electrochemical experiments for sensor validation with serum samples.
• Machine learning-driven predictive modeling and web app development for performance enhancement.

Main Results:

• DFT confirmed MoS2's suitability for dopamine detection via physisorption and electrostatic interactions.
• The sensor demonstrated high accuracy (>96%) and a low limit of detection (10 nM) in spiked serum.
• The machine learning web app improved sensor accuracy to nearly 99%.

Conclusions:

• MoS2-functionalized paper sensors offer a promising platform for sensitive and accurate dopamine detection.
• Combining theoretical calculations, experimental validation, and machine learning significantly enhances biosensing capabilities.
• This integrated approach paves the way for advanced point-of-care diagnostic tools.