Artificial neural network modeling of cefixime photodegradation by synthesized CoBi 2 O 4 nanoparticles
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RESEARCH ARTICLE
Artificial neural network modeling of cefixime photodegradation by synthesized CoBi2O4 nanoparticles Oussama Baaloudj 1 & Noureddine Nasrallah 1 & Mohamed Kebir 1,2 & Bouzid Guedioura 3 & Abdeltif Amrane 4 & Phuong Nguyen-Tri 5,6 & Sonil Nanda 7 & Aymen Amin Assadi 4 Received: 8 August 2020 / Accepted: 16 November 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract CoBi 2 O 4 (CBO) nanoparticles were synthesized by sol-gel method using polyvinylpyrrolidone (PVP) as a complexing reagent. For a single phase with the spinel structure, the formed gel was dried and calcined at four temperatures stages. Various methods were used to identify and characterize the obtained spinel, such as X-ray diffraction (XRD), scanning electron micrograph (SEM-EDX), transmission electron microscope (TEM), Fourier transform infrared (FT-IR), X-ray fluorescence (XRF), Raman, and UV-Vis spectroscopies. The photocatalytic activity of CBO was examined for the degradation of a pharmaceutical product cefixime (CFX). Furthermore, for the prediction of the CFX degradation rate, an artificial neural network model was used. The network was trained using the experimental data obtained at different pH with different CBO doses and initial CFX concentrations. To optimize the network, various algorithms and transfer functions for the hidden layer were tested. By calculating the mean square error (MSE), 13 neurons were found to be the optimal number of neurons and produced the highest coefficient of correlation R2 of 99.6%. The relative significance of the input variables was calculated, and the most impacting input was proved to be the initial CFX concentration. The effects of some scavenging agents were also studied. The results confirmed the dominant role of hydroxyl radical OH• in the degradation process. With the novel CoBi2O4/ZnO hetero-system, the photocatalytic performance has been enhanced, giving an 80% degradation yield of CFX (10 mg/L) at neutral pH in only 3 h. Keywords CoBi2O4 spinel . Characterization . Cefixime . Artificial neural network . Optimization
Highlights • Pure CoBi2O4 nanoparticles are synthesized and used as photocatalysts. • A strong degradation of cefixime was demonstrated by using this catalyst. • An artificial neural network was used to optimize the cefixime removal. • A new hetero-system CoBi2O4/ZnO has been discussed. Responsible Editor: Santiago V. Luis * Phuong Nguyen-Tri [email protected]
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Reactor division, Nuclear Research Center, Draria, Algeria
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Univ Rennes - ENSCR / UMR CNRS 6226 “Chemical Sciences of Rennes” ENSCR, Campus de Beaulieu, 11, allée de Beaulieu - CS 50837 - 35708 Rennes, 35708 Rennes, France
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, Québec G9A 5H7, Canada
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Department of Chemical and Biological Engineering, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5A9, Canada
* Aymen Amin Assadi [email protected] 1
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Labora
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