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Artificial neural network modeling of cefixime photodegradation by synthesized CoBi2O4 nanoparticles

Abstract

CoBi2O4 (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.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

The authors gratefully acknowledge the financial support from the Thematic Research Agency for Science and Technology (ATRST) through the national research program (PM, PRFU Project N°A16N01UN160420190002) and the Directorate-General for Scientific Research and Technological Development (DGRSDT) of Algeria.

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Authors and Affiliations

Authors

Contributions

Oussama Baaloudj and Mohamed Kebir: investigation, formal analysis, visualization, writing original draft

Noureddine Nasrallah and Aymen Amin Assadi: conceptualization, funding acquisition, methodology, resources, project administration, supervision, writing-review and editing

Phuong Nguyen-Tri: writing-review and editing, methodology, conceptualization

Bouzid Guedioura: formal analysis

Abdeltif Amrane and Sonil Nanda: investigation, visualization

Corresponding authors

Correspondence to Phuong Nguyen-Tri or Aymen Amin Assadi.

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Competing interests

The authors declare that they have no competing interest.

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Responsible Editor: Santiago V. Luis

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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.

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Baaloudj, O., Nasrallah, N., Kebir, M. et al. Artificial neural network modeling of cefixime photodegradation by synthesized CoBi2O4 nanoparticles. Environ Sci Pollut Res 28, 15436–15452 (2021). https://doi.org/10.1007/s11356-020-11716-w

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  • DOI: https://doi.org/10.1007/s11356-020-11716-w

Keywords

  • CoBi2O4 spinel
  • Characterization
  • Cefixime
  • Artificial neural network
  • Optimization