Hussain, Sajjad; Khan, Hammad; Gul, Saima; Steter, Juliana R; Motheo, Artur de Jesus 
Fonte: Chemosphere. Unidade: IQSC
Assuntos: TRATAMENTO QUÍMICO DE ÁGUAS RESIDUÁRIAS, FÁRMACOS
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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: 14 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.2021.130151NLM
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. 14 ] Available from: https://doi.org/10.1016/j.chemosphere.2021.130151Vancouver
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. 14 ] Available from: https://doi.org/10.1016/j.chemosphere.2021.130151
