Synergistic AC–ZnO/Al2O3 composite: a dual-function adsorbent for simultaneous petroleum desulfurization and aqueous pollutant removal
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Shaimaa Mohsen Essa
Abstract
This study synthesized and characterized a new composite of activated carbon-zinc oxide/aluminum oxide (AC–ZnO/Al2O3) to assess its capability to simultaneously extract sulfur compounds from crude oil and other chemical pollutants from sewage water. The composite was made by wet impregnation with subsequent heating at 450 °C which resulted in even distribution of the metal oxide nanoparticles with diameters of ZnO 18.3 nm and Al2O3 15.7 nm on the carbon matrix. Surface properties showed improvement with BET surface area increasing from 1110 m2/g to 1247 m2/g and total pore volume expanding to 0.68 cm3/g. Greater adsorption performance was exhibited for dibenzothiophene (DBT) removal with a maximum capacity of 58.7 mg/g which is a 66.8 % improvement over pristine activated carbon. From synthetic sewage, pollutant removal efficiencies of 95.2 % for Pb2+, 87.4 % for phenol, and 92.8 % for methylene blue were obtained. Kinetics suggest a pseudo second-order rate and chemisorption limited in rate, Langmuir isotherm was the best fit for equilibrium data. Some spontaneous and exothermic thermodynamic processes described the adsorption. In practical applications, 85.0 % sulfur removal from crude oil and marked improvements in municipal sewage quality parameters were observed. The composite also retained 85 % of DBT removal capacity and 82 % of Pb2+ removal efficiency after five thermal regeneration cycles. The synergistic combined effects of physical adsorption on the surfaces of carbon and the chemical interactions with metal oxide sites showcased that the material could be used for industrial contaminations enabling multi-functional capabilities to simultaneously treat aqueous waste streams along with petroleum.
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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: All other authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Supplementary Material
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