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Synthesis of CuO nanoparticles through green route using Citrus limon juice and its application as nanosorbent for Cr(VI) remediation: Process optimization with RSM and ANN-GA based model

dc.contributor.authorMohan S.; Singh Y.; Verma D.K.; Hasan S.H.
dc.date.accessioned2025-05-24T09:23:09Z
dc.description.abstractIn present investigation, an attempt has been made for the synthesis of cupric oxide nanoparticles (CuONPs) through a green route by utilizing lemon juice extract as a bioreductant. The synthesized CuONPs were characterized through UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM). The CuONPs were utilized for Cr(VI) removal from water through adsorption method in batch mode at different initial Cr(VI) concentration, pH, temperature and CuONPs dosage. The maximum uptake capacity of CuONPs was found to be 16.63 mg of Cr(VI)/g at pH 4.0. Implementation of response surface methodology (RSM) followed by artificial neural network hybridized with genetic algorithm (ANN-GA) approach has resulted maximum Cr(VI) adsorption of 98.8% under the optimized conditions of initial metal concentration 22.5 mg/L, pH 3.81, CuONPs dose 1.28 g/L and temperature 37.1 °C. Under optimum conditions, adsorption isotherm study was conducted, which showed that the fitness of experimental data was well achieved with Langmuir isotherm model illustrating monolayer pattern of adsorption. Thermodynamic study revealed that the process was spontaneous and endothermic in nature, while adsorption kinetics was best explained by pseudo-second order kinetic model. ©.
dc.identifier.doihttps://doi.org/10.1016/j.psep.2015.05.005
dc.identifier.urihttp://172.23.0.11:4000/handle/123456789/15319
dc.relation.ispartofseriesProcess Safety and Environmental Protection
dc.titleSynthesis of CuO nanoparticles through green route using Citrus limon juice and its application as nanosorbent for Cr(VI) remediation: Process optimization with RSM and ANN-GA based model

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