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Dimethylsulfoxide functionalized cadmium sulfide quantum dot for heavy metal ion detection

  • Poongodi Ayyanusamy , Ragavendran Venkatesan , Abel Noelson Enose Rajan , Jamespandi Annaraj , Umadevi Mahalingam , Parimaladevi Ramasamy EMAIL logo , Kevin Bethke and Jeyanthinath Mayandi
Published/Copyright: February 28, 2025

Abstract

Heavy metal ions (HMIs) detection in the aquatic, environmental and biological systems is of tremendous interest owing to their adverse effects on the ecosystem and human health, depending on the dose and its toxicity. The calorimetric sensor acts as a potential scavenger for detecting HMIs such as mercury, lead, copper, etc., which is reliable and more sensitive towards metal ions. In this work, chemically synthesized self-assembled cadmium sulfide quantum dots (CdS QDs) were characterized using X-ray diffraction, Fourier transform infrared spectroscopy, UV-visible spectroscopy, transmission electron spectroscopy, energy dispersive X-ray spectroscopy which confirms the structural and morphological formation of QD and it act as a the DMSO@CdS QDs colorimetric sensing probe for the detection of copper, mercury and lead with the high selectivity and sensitivity with the limit of detection of 179.5 nM, 58 and 60 μM. The on-site detection is capable of monitoring heavy metals in environmentally polluted samples.


Corresponding author: Parimaladevi Ramasamy, Department of Physics, Mother Teresa Women’s University, Kodaikanal 624101, India, E-mail:

Acknowledgments

The authors (P.A and P.R) are thankful to Tamilnadu State Council for Science and Technology, Chennai, India for the financial assistance under Student Project Scheme (2021–22).

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. PA: Hypothetical conception, Methodology, Writing – original draft; RV: Data curation, review and editing; ANE: Data curation, review and editing; UM: review & editing; PR: Funding, review and editing; KB: review and editing; JM: Supervision, review and editing.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The authors (P.A and P.R) are thankful to Tamilnadu State Council for Science and Technology, Chennai, India for the financial assistance under Student Project Scheme (2021–22).

  7. Data availability: Data available on request.

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Received: 2024-04-12
Accepted: 2025-02-11
Published Online: 2025-02-28
Published in Print: 2025-08-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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