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Filtros : "Gul, Saima" "Chemosphere" Limpar

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Artigo de periodico
Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach (2024)
Gul, Saima; Hussain, Sajjad ; Khan, Hammad ; Arshad, Muhammad; Khan, Javaid Rabbani ; Motheo, Artur de Jesus
Fonte: Chemosphere. Unidade: IQSC
Assuntos: INTELIGÊNCIA ARTIFICIAL, REDES NEURAIS, ANTIBIÓTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GUL, Saima et al. Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach. Chemosphere, v. 357, p. 141868, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.chemosphere.2024.141868. Acesso em: 30 set. 2024.
APA
Gul, S., Hussain, S., Khan, H., Arshad, M., Khan, J. R., & Motheo, A. de J. (2024). Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach. Chemosphere, 357, 141868. doi:10.1016/j.chemosphere.2024.141868
NLM
Gul S, Hussain S, Khan H, Arshad M, Khan JR, Motheo A de J. Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach [Internet]. Chemosphere. 2024 ; 357 141868.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.chemosphere.2024.141868
Vancouver
Gul S, Hussain S, Khan H, Arshad M, Khan JR, Motheo A de J. Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach [Internet]. Chemosphere. 2024 ; 357 141868.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.chemosphere.2024.141868
Artigo de periodico
Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study (2021)
Hussain, Sajjad; Khan, Hammad; Gul, Saima; Steter, Juliana R; Motheo, Artur de Jesus
Fonte: Chemosphere. Unidade: IQSC
Assuntos: TRATAMENTO QUÍMICO DE ÁGUAS RESIDUÁRIAS, FÁRMACOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HUSSAIN, Sajjad et al. Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study. Chemosphere, v. 276, n. 130151 August 2021, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.chemosphere.2021.130151. Acesso em: 30 set. 2024.
APA
Hussain, S., Khan, H., Gul, S., Steter, J. R., & Motheo, A. de J. (2021). Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study. Chemosphere, 276( 130151 August 2021). doi:10.1016/j.chemosphere.2021.130151
NLM
Hussain S, Khan H, Gul S, Steter JR, Motheo A de J. Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study [Internet]. Chemosphere. 2021 ; 276( 130151 August 2021):[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.chemosphere.2021.130151
Vancouver
Hussain S, Khan H, Gul S, Steter JR, Motheo A de J. Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study [Internet]. Chemosphere. 2021 ; 276( 130151 August 2021):[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.chemosphere.2021.130151

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