Effective photodegradation of cefixime and carvedilol mediated by visibly active MoO3/CoMn2O4/Cu2BaSnS4 photocatalytic system: An insight on photocatalytic mechanism, degradation pathway and by-product toxicity analysis

Harini, G.; Okla, Mohammad K.; Abdel-maksoud, Mostafa A.; Janani, B.; Alaraidh, Ibrahim A.; Al-amri, Saud S.; Sudheer Khan, S.

doi:10.1016/j.jiec.2024.01.002

Cited by 3 publications

(1 citation statement)

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“…The reason for using these nanostructures is their high efficiency and the generation of free radicals with high oxidative potential 21 . These radicals are non-selective and have a high reactivity with antibiotic molecules 22 – 24 .…”

Section: Introductionmentioning

confidence: 99%

Cefixime removal via WO3/Co-ZIF nanocomposite using machine learning methods

Sheikhmohammadi,

Alamgholiloo,

Golaki

et al. 2024

Sci Rep

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In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L−1, 3.13, 117.65 min, and 0.19 g L−1) and (5 mg L−1, 3, 120 min, and 0.19 g L−1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment.

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Section: Introductionmentioning

confidence: 99%