Nano Janue-like N-doped ZnO bundles as efficient photocatalysis for the removal of endocrine disruptor under visible-light irradiation
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Suganya Josephine Gali Anthoni
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Rubesh Ashok Kumar Selva Kumar
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Vasvini Mary Devaraj
Abstract
Endocrine disruptors are a broad area of concern and is a threat to humanity when present in the environment. With the rise of industrialization and globalization, there has been an increase in the release of endocrine-disrupting agents such as phenolics and phthalates into the environment. This has made it a challenging task for research groups worldwide to remove these agents from water. An herbicide 2,4-dichlorophenoxy acetic acid has been chosen as the target for degradation which is used commonly for the control of weeds with broad leaf in the fields. These materials get into the aquatic system by water run-off during rain and percolate into the soil (LD 50–639 mg/kg to 1,646 mg/kg). Hence, in this study, we have employed nitrogen-doped ZnO nano-bundles for a visible light induced photocatalytic degradation of 2,4-D. The Nano Janue-like N-doped ZnO bundles (NZnO) were prepared and analyzed by various characterization techniques. The NZnO nano janue bundles were found to be in an arranged structure (particle size – 10–12 nm, band gap energy – 3.01 eV). The photocatalytic removal studies were conducted with an optimum catalytic dosage of 10 mg/10 mL. The kinetics were conducted for the different concentrations of 2,4-D (10–25 ppm). The kinetic rate constant was calculated as 5.64 to 2.75 × 10−3 min−1, the reaction followed a pseudo-first order kinetics. The COD removal was found to be 93.75 % for 10 ppm 2,4-D. This study paves the way for employing NZnO nano janue bundles as photocatalysts for environmental remediation under visible light irradiation.
Acknowledgments
Authors would like to thank Dr. S. A. V. Satyamurthy, Director Research, Vinayaka Mission’s Research Foundation for his valuable support. We would also like to thank the Director CLRI for providing infrastructure and lab facilities.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: Not applicable.
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Data availability: Not applicable.
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