| Texto completo | |
| Autor(es): |
Costa, Heloisa Pereira de Sa
;
Duarte, Emanuele Dutra Valente
;
da Silva, Meuris Gurgel Carlos
;
Vieira, Melissa Gurgel Adeodato
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF WATER PROCESS ENGINEERING; v. 58, p. 13-pg., 2024-01-31. |
| Resumo | |
This study proposes an adsorbent composed of multi-walled carbon nanotubes functionalized with iron nanoparticles, synthesized through a green approach, for the removal of diclofenac (DIC) and losartan (LOS) from aqueous solutions. Fourier Transform Infrared Spectroscopy and X-ray photoelectron spectroscopy analyses confirm the effectiveness of the green functionalization and the adsorption process. The results indicated consistent maximum adsorption capacities within the studied temperature range (qeDIC = 0.539-0.559 mmol/g, qeLOS = 0.534-0.569 mmol/g). Moreover, the DIC curves can be well-represented by the Langmuir model, while the LOS is well-fitted to Sips and Langmuir. Thermodynamics revealed the process as exothermic, spontaneous, and favorable. Machine learning techniques exhibited greater accuracy in predicting outcomes than traditional models. Adsorption involves simultaneous mechanisms such as pi-pi, n-pi, hydrogen, electrostatic, and hydrophobic interactions. The results presented aim not only to advance the understanding of adsorption processes, but also offer a sustainable alternative for the removal of emerging contaminants from aqueous solutions. The combination of a green approach, effective adsorption capacities, and machine learning analysis enhances the overall impact of this research in the field of wastewater treatment. This study paves the way for further exploration and application of environmentally friendly materials and advanced modeling techniques to address the challenges of water pollution. (AU) | |
| Processo FAPESP: | 20/16004-9 - Desenvolvimento de bioadsorventes avançados e modelos baseados em inteligência artificial para aplicação na remoção e monitoramento de poluentes emergentes em meios aquosos |
| Beneficiário: | Meuris Gurgel Carlos da Silva |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |