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Plasma modified Co3O4 nanoparticles for catalytic degradation process through enhanced peroxidase-like activity.
- Source :
- Journal of Industrial & Engineering Chemistry; May2023, Vol. 121, p114-123, 10p
- Publication Year :
- 2023
-
Abstract
- [Display omitted] • Co 3 O 4 -NPs were synthesized and modified by argon cold plasma (Ar-Co 3 O 4 -NPs). • Surface plasma modification improved the peroxidase-mimic activity of Ar-Co 3 O 4 -NPs. • Enhanced catalytic activity of Ar-Co 3 O 4 -NPs was used for the degradation of MG dye. • Ar-Co 3 O 4 -NPs exhibited a high dye removal efficiency of 96.78 % within 70 min. • ANN with a 5:7:1 topology successfully modeled the catalytic degradation process. This study highlights a simple and efficient nanochemistry-based approach for the effective degradation of triphenylmethane and toxic dye, malachite green (MG) using Argon cold plasma-modified cobalt oxide nanoparticles (Ar-Co 3 O 4 -NPs). Synthesized particles were characterized using scanning electron microscope, X-ray diffraction, and Fourier-transform infrared spectroscopy. The peroxidase-mimic activity of Co 3 O 4 -NPs was evaluated, and the results confirmed that the catalytic activity of Co 3 O 4 -NPs was enhanced after plasma modification. The decomposition of MG was tested using the improved catalytic activity of Ar-Co 3 O 4 -NPs in model aqueous solution. The results indicated the ability of 0.16 g/mL Ar-Co 3 O 4 -NPs to completely degrade 40 µM MG within 70 min with a decolorization efficiency of 96.78%. Experimental conditions were optimized for maximum MG removal. Gas chromatography-mass spectrometry was used to determine the byproducts of MG degradation, and the findings indicated the production of less toxic products. The toxicity of the resultant metabolites of MG degradation was evaluated against E. coli and B. subtilis and the results confirmed less toxic product formation. Artificial neural networks (ANNs) were used to model the catalytic degradation data, and the strong correlation between experimental observations and ANN model predictions suggested that the designed model could accurately predict MG dye removal efficiency under different operating conditions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1226086X
- Volume :
- 121
- Database :
- Supplemental Index
- Journal :
- Journal of Industrial & Engineering Chemistry
- Publication Type :
- Periodical
- Accession number :
- 162324494
- Full Text :
- https://doi.org/10.1016/j.jiec.2023.01.015