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

Arijit Pal1, Souvik Biswas1, Koel Chaudhury1

  • 1School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India.

ACS Applied Materials & Interfaces
|August 29, 2023
PubMed
Summary

This study introduces a novel paper sensor using molybdenum disulfide (MoS2) for dopamine detection. The sensor achieves high accuracy in serum samples, enhanced by a machine learning web app.

Keywords:
MoS2density functional theoryelectrochemical sensorhyperparameter-tuned support vector regressorpapertronicssmart web app interface

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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.