journal article·2022-01

ZnAl2O4 supported on lychee-biochar applied to ibuprofen photodegradation

Silvestri Siara, Carissimi Elvis, Rajendran Harishkumar, Padmanaban Velayudhaperumal Chellam

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Abstract

Abstract In this study, composites of biochar-ZnAl2O4 produced by the pyrolysis of the lychee waste biomass were synthesized. The composites were characterized by XRD, FTIR, SEM, BET, and BJH. The biochar-ZnAl2O4 composite was successfully formed and the efficiency was tested in adsorption and photocatalysis experiments. The adsorption equilibrium was obtained with a contact time of 60 min. Data on modeling and optimization of ibuprofen removal by statistical approach were fitted to quadratic equations using multiple regressions and analysis of variance (ANOVA). An RSM-CCF has been proposed for the enhanced removal of ibuprofen by adsorption followed by photocatalysis. The composite reached 100% efficiency of degradation of ibuprofen (20 mg L−1) in 120 min, under conditions of pH = 6.5, 25 °C, 1 g L−1 of composite. The ZnAl2O4 supported significantly increased the composite adsorption performance and factors such as specific surface area (111.69 m2 g−1), pore volume and pore size (mesoporous region) of the biochar synergistically promoted the photocatalytic activity of ZnAl2O4, with k = 0.0093 min−1. The adsorption and photocatalytic efficiencies were confirmed to contribute to the degradation of the ibuprofen aqueous solution by the radicals •OH and O2-• in lower molecular weight by-products. The composite developed in this work showed that the support of ZnAl2O4 in the biochar is essential to increase photoactivity, therefore, it has shown to be a promising material for adsorption and degradation of emerging pollutants such as the ibuprofen, avoiding transfer of pollution to another environmental medium.
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Research context

OpenAlex topics: Advanced Photocatalysis Techniques · Covalent Organic Framework Applications · Pharmaceutical and Antibiotic Environmental Impacts

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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