Plasma modified Co3O4 nanoparticles for catalytic degradation process through enhanced peroxidase-like activity

This study highlights a simple and efficient nanochemistry-based approach for the effective degrada-tion of triphenylmethane and toxic dye, malachite green (MG) using Argon cold plasma-modified cobalt oxide nanoparticles (Ar-Co3O4-NPs). Synthesized particles were characterized using scanning electron microscope, X-ray diffraction, and Fourier-transform infrared spectroscopy. The peroxidase-mimic activity of Co3O4-NPs was evaluated, and the results confirmed that the catalytic activity of Co3O4-NPs was enhanced after plasma modification. The decomposition of MG was tested using the improved catalytic activity of Ar-Co3O4-NPs in model aqueous solution. The results indicated the abil-ity of 0.16 g/mL Ar-Co3O4-NPs to completely degrade 40 lM 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 sug-gested that the designed model could accurately predict MG dye removal efficiency under different operating conditions. (c) 2023 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

Automated summary

This study presents a simple and efficient approach for degrading triphenylmethane and malachite green using Argon cold plasma-modified cobalt oxide nanoparticles. The catalytic activity of the nanoparticles was enhanced after plasma modification, leading to the complete degradation of MG within 70 minutes. The produced metabolites were found to be less toxic, and a neural network model accurately predicted the efficiency of MG removal under different conditions.