Source: Chemosphere. Unidade: IQSC
Subjects: TRATAMENTO QUÍMICO DE ÁGUAS RESIDUÁRIAS, FÁRMACOS
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: 01 nov. 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 nov. 01 ] 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 nov. 01 ] Available from: https://doi.org/10.1016/j.chemosphere.2021.130151