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Green synthesis of highly luminous lemon juice-based carbon dots for antimicrobial assessment and fingerprint detection

  • Arshad Hussain Wazir ORCID logo EMAIL logo , Qudratullah Khan , Faizan Ullah and Khurram Yaqoob
Published/Copyright: January 30, 2025
Become an author with De Gruyter Brill

Abstract

Carbon dots are regarded as a brand new class of nanostructures in the carbonaceous family that have piqued the curiosity of researchers in a wide range of bio applications. This work focuses on the synthesis and characterization of carbon dots, as well as their latent fingerprint detection and antibacterial/antifungal capabilities. Highly luminous carbon dots were prepared by optimizing simple hydrothermal carbonization settings at 180 °C for 12 h using lemon juice as a raw precursor. The resulting product was examined using Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray analysis, X-ray diffractometery, and ultraviolet–visible spectrophotometer. The as-prepared carbon dots were found to be extremely bright when excited under ultraviolet light (λ = 365 nm). The presence of carbon and oxygen functionalities on the surface of the carbon dots was revealed by infrared spectrocopy. The diffraction pattern confirmed the amorphous structure of the carbon dots, with an average size of 7 nm determined using the Scherrer equation. The surface morphology analysis revealed that the carbon dots exhibited an aggregated form with irregular spherical shapes. The chemical structure examination validated the elemental makeup of the prepared lemon juice-based carbon dots. The detection of latent fingerprints on carbon dots under ultraviolet light yielded positive results. In addition, the obtained carbon dots displayed antifungal and antibacterial activity against tested pathogenic fungal and bacterial strains.


Corresponding author: Arshad Hussain Wazir, Carbon Materials Laboratory, Department of Chemistry, University of Science and Technology Bannu, Bannu 28100, Khyber Pukhtunkhwa, Pakistan, E-mail:

  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.

  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: None declared.

  7. Data availability: Not applicable.

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Received: 2024-03-13
Accepted: 2024-08-14
Published Online: 2025-01-30
Published in Print: 2025-02-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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