Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.
Razieh Motamedi Khozani1, Samira Abbasi-Moayed2, Mohammad Reza Hormozi-Nezhad3
1Department of Chemistry, Sharif University of Technology, Tehran, 11155-9516, Iran.
Chemosphere
|April 13, 2024
Summary
This study presents a novel fluorometric sensor using quantum dots and carbon dots to detect and differentiate chromium species in water. Machine learning analysis confirms its accuracy for environmental monitoring.
Area of Science:
- Environmental Chemistry
- Analytical Chemistry
- Materials Science
Background:
- Chromium is a toxic pollutant whose environmental risk depends on its oxidation state.
- Accurate speciation analysis is vital for environmental water quality monitoring and industrial waste risk assessment.
Purpose of the Study:
- To develop a single-well fluorometric sensor for detecting and differentiating chromium species (Cr(III) and Cr(VI)).
- To apply machine learning for analyzing fluorescence spectra and quantifying chromium species.
Main Methods:
- Utilized orange emissive thioglycolic acid stabilized CdTe quantum dots (TGA-QDs) and blue emissive carbon dots (CDs).
- Employed linear discriminant analysis (LDA) for classification and partial least squares regression (PLSR) for multivariate calibration.
- Analyzed fluorescence spectral variations upon addition of chromium species.
Main Results:
- LDA achieved high accuracy in differentiating single and bicomponent chromium samples.
- PLSR demonstrated strong linearity for Cr2O72− (1.0–100.0 μM), CrO42− (1.0–100.0 μM), and Cr3+ (0.1–15 μM).
- Achieved detection limits of 2.6 μM for Cr2O72−, 2.9 μM for CrO42−, and 0.7 μM for Cr3+.
Conclusions:
- The developed sensor platform successfully identifies and quantifies chromium species in environmental water samples.
- This work offers innovative insights into speciation analytics for environmental monitoring.
- The sensor provides a sensitive and accurate method for assessing chromium contamination.
