Process optimization and kinetics analysis for photocatalytic degradation of emerging contaminant using N-doped TiO2-SiO2 nanoparticle: Artificial Neural Network and Surface Response Methodology approach

Kassahun, Shimelis Kebede; Kiflie, Zebene; Kim, Hern; Baye, Anteneh F.

doi:10.1016/j.eti.2021.101761

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…Minitab, 284 Design Expert, 285 Statistica and MATLAB are popular software applications for employing DOE for physicochemical removal of pigments. 286 Full factorial design. The full factorial design method is a standard experimental design with 2-3 input variable levels.…”

Section: Response Surface Methodology (Rsm) Optimization Approachmentioning

confidence: 99%

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Response surface methodology: a powerful tool for optimizing the synthesis of metal sulfide nanoparticles for dye degradation

Sandhu,

Raza,

Farwa

et al. 2023

Mater. Adv.

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Section: Response Surface Methodology (Rsm) Optimization Approachmentioning

confidence: 99%

“…Minitab, 284 Design Expert, 285 Statistica and MATLAB are popular software applications for employing DOE for physicochemical removal of pigments. 286…”

Section: Optimization Strategies For the Degradation Of Metal Sulfidesmentioning

confidence: 99%

Response surface methodology: a powerful tool for optimizing the synthesis of metal sulfide nanoparticles for dye degradation

Sandhu,

Raza,

Farwa

et al. 2023

Mater. Adv.

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“…Thus, this model can guide the optimization of experimental conditions in photocatalytic pollutant degradation. Kassahun et al employed NN models to optimize the synthesis conditions of the N-doped TiO 2 /SiO 2 heterojunction to get an optimum ciprofloxacin degradation efficiency under visible light . Similar NN approaches were extended to other systems such as S-doped g-C 3 N 4 /ZnO, g-C 3 N 4 /Ce-doped ZnO/Ti, and a CdS/CaFe 2 O 4 Z-scheme system. − These studies showed that NNs had considerable promise for optimization of the synthesis of photocatalysts containing two materials, but that some of these models might be overfitted as the training sets were small.…”

Section: Application Of Machine Learning To Electrocatalysts and Phot...mentioning

confidence: 99%

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

et al. 2022

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Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/ photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/ photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

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“…In the recent few decades, semiconductor based photocatalysis has been regarded as the most promising technology for the elimination of low concentration contaminants. [5][6][7] The photocatalytic technology has mild reaction conditions, low cost, superior recoverability and less secondary pollution and etc. Therefore, photocatalysis technology has been extensively investigated for the removal of dyes and antibiotics.…”

Section: Introductionmentioning

confidence: 99%

Induced abundant oxygen vacancies in Sc₂VO_5−δ/g-C₃N₄ heterojunctions for enhanced photocatalytic degradation of levofloxacin

Feng

Yang

et al. 2023

RSC Adv.

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Process optimization and kinetics analysis for photocatalytic degradation of emerging contaminant using N-doped TiO2-SiO2 nanoparticle: Artificial Neural Network and Surface Response Methodology approach

Cited by 18 publications

References 27 publications

Response surface methodology: a powerful tool for optimizing the synthesis of metal sulfide nanoparticles for dye degradation

Response surface methodology: a powerful tool for optimizing the synthesis of metal sulfide nanoparticles for dye degradation

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Induced abundant oxygen vacancies in Sc₂VO_5−δ/g-C₃N₄ heterojunctions for enhanced photocatalytic degradation of levofloxacin

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Process optimization and kinetics analysis for photocatalytic degradation of emerging contaminant using N-doped TiO2-SiO2 nanoparticle: Artificial Neural Network and Surface Response Methodology approach

Cited by 18 publications

References 27 publications

Response surface methodology: a powerful tool for optimizing the synthesis of metal sulfide nanoparticles for dye degradation

Response surface methodology: a powerful tool for optimizing the synthesis of metal sulfide nanoparticles for dye degradation

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Induced abundant oxygen vacancies in Sc2VO5−δ/g-C3N4 heterojunctions for enhanced photocatalytic degradation of levofloxacin

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Product

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About

Induced abundant oxygen vacancies in Sc₂VO_5−δ/g-C₃N₄ heterojunctions for enhanced photocatalytic degradation of levofloxacin