Rational design of nitrogen-doped carbon quantum dots decorated g-C3N4 nanosheets: a fluorescent chemosensor for the selective detection of bismuth ions and its application in real samples
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Abinaya Shanmugavel
und
Sujin P. Jose
Abstract
In this work, nitrogen-doped carbon quantum dots (N-CQDs) decorated graphitic carbon nitride (g-C3N4) nanocomposite (N-CQDs/g-C3N4) has been synthesized through hydrothermal method. The resulting nanocomposite was characterized using X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, elemental mapping, high-resolution transmission electron microscopy, elemental dispersive X-ray spectroscopy, and UV–vis analysis. Utilizing this nanocomposite, we developed a novel fluorescent probe for the selective detection of Bi3+ ions. The presence of carbon and nitrogen groups in the nanocomposite enables Bi3+ ion detection via a fluorescent ‘turn off–on’ process with a detection limit of 0.289 µM in a DMF:H2O (1:1) mixture. This nanoprobe demonstrates exceptional reusability as it can be chemically regenerated and employed for Bi3+ ion detection for up to 10 cycles without any loss in fluorescence properties. Furthermore, it reaches equilibrium within 2 min of Bi3+ ion addition and remains stable for over 10 min ensuring its reliability and stability. The efficacy of the N-CQDs/g-C3N4 chemosensor was validated in real-world applications through the analysis of water samples from Kaveri and Siruvani Rivers (Tamil Nadu, India), delivering impressive recovery rates ranging from 87.4 to 90.1 % and 76.2 to 83.9 % respectively.
Funding source: Rashtriya Uchchatar Shiksha Abhiyan (RUSA Phase 2.0)
Award Identifier / Grant number: File No. 046/RUSA/MKU/2020-2021
Acknowledgments
The authors acknowledge Rashtriya Uchchatar Shiksha Abhiyan (RUSA Phase 2.0) File No. 046/RUSA/MKU/2020-2021, Madurai Kamaraj University, Madurai for providing financial support. Narmatha expresses her thanks to Karunya Institute of Technology and Sciences, Coimbatore for the Research Associateship.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Abinaya Shanmugavel: Conceptualization, Methodology, Data curation, Writing- Original draft, Writing-Review and Editing, Narmatha Ganesan: Data curation, Formal analysis, software, Nelson Prabu Louie Pastiur: Formal analysis, Software, Raju Nandhakumar: Writing- Review and Editing, Visualization, Formal analysis, Validation, Sujin P Jose: Formal analysis, Supervision, Resources, Validation.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: Rashtriya Uchchatar Shiksha Abhiyan (RUSA Phase 2.0) File No. 046/RUSA/MKU/2020-2021.
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Data availability: Data available on request from the authors.
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