Electrochromic sensor augmented with machine learning for enzyme-free analysis of antioxidants

Saba Ranjbar, Amir Hesam Salavati, Negar Ashari Astani, Naimeh Naseritaheri, Navid Davar, Mohammad Reza Ejtehadi

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

Our study presents an electrochromic sensor that operates without the need for enzymes or multiple oxidant reagents. This sensor is augmented with machine learning algorithms, enabling the identification, classification, and prediction of six different antioxidants with high accuracy. We utilized polyaniline (PANI), Prussian blue (PB), and copper-Prussian blue analogues (Cu-PBA) at their respective oxidation states as electrochromic materials (ECMs). By designing three readout channels with these materials, we were able to achieve visual detection of antioxidants without relying on traditional “lock and key” specific interactions. Our sensing approach is based on the direct electrochemical reactions between oxidized electrochromic materials (ECMsox) as electron acceptors and various antioxidants, which act as electron donors. This interaction generates unique fingerprint patterns by switching the ECMsox to reduced electrochromic materials (ECMsred), causing their colors to change. Through the application of density functional theory (DFT), we demonstrated the molecular-level basis for the distinct multicolor patterns. Additionally, machine learning algorithms were employed to correlate the optical patterns with RGB data, enabling complex data analysis and the prediction of unknown samples. To demonstrate the practical applications of our design, we successfully used the EC sensor to diagnose antioxidants in serum samples, indicating its potential for the on-site monitoring of antioxidant-related diseases. This advancement holds promise for various applications, including the real-time monitoring of antioxidant levels in biological samples, the early diagnosis of antioxidant-related diseases, and personalized medicine. Furthermore, the success of our electrochromic sensor design highlights the potential for exploring similar strategies in the development of sensors for diverse analytes, showcasing the versatility and adaptability of this approach.

Original languageEnglish
Pages (from-to)4281-4292
Number of pages12
JournalACS Sensors
Volume8
Issue number11
DOIs
Publication statusPublished - 24 Nov 2023

Keywords

  • antioxidants
  • electrochromism
  • enzyme-free analysis
  • machine learning-assisted sensor
  • point of care

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