CuO/TiO2/ZnO NPs Anchored Hydrogen Exfoliated Graphene: To Comprehend the Role of Graphene in Catalytic Reduction of p‑Nitrophenol

The present study deals with sonochemically in situ synthesis of a novel functional catalyst using hydrogen exfoliated graphene (HEG) supported titanium dioxide (TiO2) and copper sulfate (CuSO4) doped with zinc oxide (ZnO) (abbreviated as Ti/Cu/Zn-HEG). The synthesis of the Ti/Cu/Zn-HEG nanocomposit...

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Veröffentlicht in:ACS omega 2023-11, Vol.8 (45), p.42164-42176
Hauptverfasser: Behera, Meerambika, Alqahtani, Fatimah Othman, Chakrabortty, Sankha, Nayak, Jayato, Banerjee, Shirsendu, Kumar, Ramesh, Jeon, Byong-Hun, Tripathy, Suraj K
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Sprache:eng
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Zusammenfassung:The present study deals with sonochemically in situ synthesis of a novel functional catalyst using hydrogen exfoliated graphene (HEG) supported titanium dioxide (TiO2) and copper sulfate (CuSO4) doped with zinc oxide (ZnO) (abbreviated as Ti/Cu/Zn-HEG). The synthesis of the Ti/Cu/Zn-HEG nanocomposite (NCs) catalyst was confirmed through its characterizations by XRD, SEM-EDX, TEM, XPS, FTIR, and BET methods. It was assessed for catalytic conversion of a model aromatic compound para-nitrophenol (p-NP) in an aqueous solution. The p-NP is a nitroaromatic compound that has a toxic and mutagenic effect. Its removal from the water system is necessary to protect the environment and living being. The newly synthesized Ti/Cu/Zn-HEG NCs were applied for their higher stability and catalytic activity as a potential candidate for reducing p-NP in practice. The operating parameters, such as p-NP concentration, catalyst dosage, and operating time were optimized for 150 ppm, 400 ppm, and 10 min through response surface methodology (RSM) in Design-Expert software to obtain the maximum reduction p-NP up to 98.4% at its normal pH of 7.1 against the controls (using HEG, Ti/Cu-HEG, and Zn-HEG). Analysis of variance of the response suggested the regression equation to be significant for the process with a major impact on catalyst concentration and operating time. The model prediction data (from RSM) and experimental data were corroborated well as reflected through model’s low relative error (RE < 0.10), high regression coefficient (R 2 > 0.97), and Willmott d-index (d will‑index > 0.95) values.
ISSN:2470-1343
2470-1343
DOI:10.1021/acsomega.3c03859