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• W4386880020 endingPage "111460" @default.
• W4386880020 startingPage "111460" @default.
• W4386880020 abstract "Clean and potable water is a basic human need for survival on Earth. Among the available water sources, wastewater has become increasingly valuable. However, certain types of wastewaters, such as spent caustic, are considered highly polluted and pose significant environmental hazards, making their proper treatment imperative. To address this issue, the present study focuses on developing a cost-effective and environmentally friendly solution using a ZnO nanocomposite synthesized with Iranian green tea as an adsorbent for sulfide adsorption from spent caustic wastewater. The synthesized nanoparticles were characterized using various analytical tools, including XRD, FTIR, FE-SEM, PSA, and EDX. The experiments were designed using Design Expert software to examine the impact of initial concentration, nanoparticle loading, initial pH, and time, leading to the determination of the optimum conditions. The optimized parameters for spent caustic recovery with spherical ZnO nanoparticles, obtained through the I-optimal method, were a nanoparticle loading of 0.37 wt.%, an initial concentration of 777.5 ppm, a reaction time of 14.33 minutes, and a pH of 9.85, resulting in a remarkable 97% sulfide elimination. The adsorption isotherm and kinetics data indicated that the pseudo-second-order kinetics and Langmuir isotherm models were well-suited for sulfide elimination. Additionally, the ZnO nanoparticles exhibited the advantage of recyclability, maintaining up to 65% of their adsorption efficiency over five cycles. Consequently, the implementation of ZnO nanoparticles emerges as a promising and effective strategy for removing sulfide from spent caustic wastewater." @default.
• W4386880020 created "2023-09-21" @default.
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• W4386880020 date "2023-12-01" @default.
• W4386880020 modified "2023-10-01" @default.
• W4386880020 title "Green Synthesis of ZnO Nanoparticles for Spent Caustic Recovery: Adsorbent Characterization and Process Optimization Using I-Optimal Method" @default.
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• W4386880020 doi "https://doi.org/10.1016/j.inoche.2023.111460" @default.
• W4386880020 hasPublicationYear "2023" @default.
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