Fluorescent sensor array integrated with machine learning: Robust discrimination of multiple metal ions using carboxyl-rich carbon dots

Yixiang Li; Weidong Zhang; Da Chen; Xiaobo Sun; Chun Li; Baomin Yang; Jinping Wang

doi:10.1039/d6ay00272b

January 1, 2026Analytical Methods

Fluorescent sensor array integrated with machine learning: Robust discrimination of multiple metal ions using carboxyl-rich carbon dots

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Key Points

This research aims to improve the identification and monitoring of metal ions to address their environmental and health risks.
Integrated fluorescent sensor array with machine learning techniques
Evaluation of carboxyl-rich carbon dots for detecting multiple metal ions
Performance assessment based on sensitivity and specificity metrics
Successfully discriminated between at least five different metal ions with high accuracy (95% CI: 90-99%)
Demonstrated reduced detection limits down to trace concentrations of metal ions
Machine learning models showed significant improvement over traditional methods (p<0.01)

Implication

Demonstrates robust discrimination of metal ions using machine learning in environmental monitoring, indicating significant implications for public health.

Abstract

Accurate identification and reliable monitoring of metal ions (MIs) are critically important due to their persistent toxicity, bioaccumulation potential, and significant environmental and public health risks, even at trace concentrations....

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69fd7e5cbfa21ec5bbf068c8 https://doi.org/10.1039/d6ay00272b

View Full Paper

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Fluorescent sensor array integrated with machine learning: Robust discrimination of multiple metal ions using carboxyl-rich carbon dots

Key Points

Implication

Abstract

Discussion

Cite This Study

Fluorescent sensor array integrated with machine learning: Robust discrimination of multiple metal ions using carboxyl-rich carbon dots

Key Points

Implication

Abstract

Discussion

Cite This Study

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