Methylene blue removal using a nanomagnetic support: a response surface approach.
Thais Ribeiro do Nascimento Moraes CalazansAna Carolina de Lima BarizãoThais de Andrade SilvaFabiana Vasconcelos CamposSérvio Tulio Alves CassiniAraceli Veronica Flores Nardy RibeiroMadson de Godoi PereiraMarco Cesar Cunegundes GuimarãesJairo Pinto de OliveiraJoselito Nardy RibeiroPublished in: Nanoscale advances (2024)
Textile wastewater is commonly released into water bodies without appropriated treatment, resulting in environmental damages. Processes involving separation and adsorption using nanomagnetic supports have been considered a promising alternative to address this concern. However, challenges concerning the low stability of nanoparticles and the reproducibility of experiments make their large-scale application difficult. In this study, we evaluated the efficiency of methylene blue (MB) removal by Fe 3 O 4 nanoparticles coated with sodium dodecyl sulfate (MNP-SDS). The nanomaterial was characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, and Fourier-transform infrared spectroscopy (FTIR). The adsorption process was optimized in two stages using factorial design. In the first stage the most influential variables (reaction time, temperature, agitation, pH, and dye concentration) were selected based on the existing literature and applied in a fractional factorial (2 (5-1) ). In the second stage, the main variables identified were used in a complete factorial (3 2 ). The highest removal percentage was obtained using 15 g L -1 of MNP-SDS, which led to a q exp of 1.5 mg g -1 . Isothermal analysis parameters and a negative Gibbs free energy indicate that the process was spontaneous, favorable, and that the data were best fitted to the Langmuir model.
Keyphrases
- electron microscopy
- raman spectroscopy
- aqueous solution
- wastewater treatment
- systematic review
- electronic health record
- magnetic resonance imaging
- magnetic resonance
- liquid chromatography
- computed tomography
- machine learning
- human health
- highly efficient
- mass spectrometry
- walled carbon nanotubes
- smoking cessation
- contrast enhanced