# Carbon dots from Manila tamarind for heavy metal ion removal and sensing through automatic classification

## TL;DR

This paper introduces carbon dots made from Manila tamarind that can detect and remove heavy metal ions, using optical signals and machine learning for environmental cleanup.

## Contribution

The novelty lies in using Manila tamarind-derived carbon dots for dual sensing and removal of heavy metals with an 'on-off-on' strategy and machine learning classification.

## Key findings

- Carbon dots effectively detect and remove Fe3+, Pb2+, and Sn2+ through electron transfer and electrostatic interactions.
- Machine learning algorithms can identify 17 metal ions using spectral and statistical features of the carbon dots.
- XPS analysis confirms the reduction of Fe3+ to Fe+2 and Pb2+ to Pb0 states by the carbon dots.

## Abstract

Environmental remediation research has been focused on the detection and removal of heavy metal ions. In this work, Manila tamarind-derived simple and sustainable carbon dots (CDs) have been proposed for the optical detection/removal of heavy metal ions. Based on the distinct optical response of CDs toward Fe3+ and Zn2+/paraquat, an “on-off-on” strategy could be implemented for dual sensing of Fe3+/Zn+2 and Fe3+/paraquat systems. Higher concentrations of CDs revealed the facile removal of Fe3+ and Pb2+ through electron transfer mechanism and of Sn2+ possibly via electrostatic interaction. X-ray photoelectron spectroscopy (XPS) reveals the CD-induced reduction of Fe3+ and Pb2+ ions to Fe+2 and Pb0 states, respectively. The spectral and statistical features were analyzed for possible identification of 17 different metal ions through machine learning approaches. The demonstrated multifunctionality of the suggested carbon-based optical nanoprobes toward the heavy metal ion identification/removal and/or reduction can be applied in wastewater treatment and water quality assessment.

•Manila tamarind-derived carbon dots for detection and removal of metal ions•On-off-on signal quenching and recovery with two systems: Fe3+/Zn2+ & Fe3+/paraquat•CDs induced precipitation and reduction of Fe3+ and Pb2+ from contaminated water•Characteristic-based machine learning algorithms for identification of metal ions

Manila tamarind-derived carbon dots for detection and removal of metal ions

On-off-on signal quenching and recovery with two systems: Fe3+/Zn2+ & Fe3+/paraquat

CDs induced precipitation and reduction of Fe3+ and Pb2+ from contaminated water

Characteristic-based machine learning algorithms for identification of metal ions

Environmental management; Environmental nanotechnology; Machine learning