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Electrochromic Sensor Augmented with Machine Learning for Enzyme-Free Analysis of Antioxidants.

Saba Ranjbar1,2, Amir Hesam Salavati3, Negar Ashari Astani4

  • 1Department of Physics, Sharif University of Technology, Tehran 11365-9161, Iran.

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|November 14, 2023
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Summary

This study introduces an electrochromic sensor using machine learning for accurate antioxidant detection without enzymes. The sensor visually identifies and predicts antioxidants by analyzing color changes from electrochemical reactions.

Keywords:
antioxidantselectrochromismenzyme-free analysismachine learning-assisted sensorpoint of care

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Machine Learning

Background:

  • Traditional antioxidant sensors often require enzymes or multiple reagents.
  • Developing selective and sensitive detection methods for antioxidants is crucial for health monitoring.
  • Existing methods may lack the ability for visual, on-site detection.

Purpose of the Study:

  • To develop an enzyme-free electrochromic sensor for identifying and predicting antioxidants.
  • To utilize machine learning for enhanced accuracy in antioxidant analysis.
  • To demonstrate a novel sensing mechanism based on direct electrochemical reactions and visual color changes.

Main Methods:

  • Fabrication of an electrochromic sensor using polyaniline (PANI), Prussian blue (PB), and copper-Prussian blue analogues (Cu-PBA).
  • Design of three readout channels for visual detection based on color changes of electrochromic materials (ECMs).
  • Application of machine learning algorithms to correlate optical patterns with RGB data for analysis and prediction.
  • Density Functional Theory (DFT) for understanding molecular-level interactions and color pattern origins.

Main Results:

  • The sensor accurately identified, classified, and predicted six different antioxidants.
  • Unique multicolor fingerprint patterns were generated through direct electrochemical reactions between oxidized ECMs and antioxidants.
  • Machine learning algorithms successfully correlated optical patterns with RGB data for complex analysis.
  • Demonstrated successful application in diagnosing antioxidants in serum samples.

Conclusions:

  • The developed electrochromic sensor offers a novel, enzyme-free approach for visual antioxidant detection.
  • Machine learning integration enhances the sensor's accuracy and predictive capabilities.
  • The sensor shows potential for on-site monitoring, early disease diagnosis, and personalized medicine.
  • This strategy can be adapted for developing sensors for a wide range of analytes.