Facile, one-pot biosynthesis and characterization of iron, copper and silver nanoparticles using Syzygium cumini leaf extract: As an effective antimicrobial and aflatoxin B1 adsorption agents.
Muhammad Asif AsgharErum ZahirMuhammad Arif AsgharJaved IqbalAhad Abdul RehmanPublished in: PloS one (2020)
In this study, a facile, ecological and economical green method is described for the fabrication of iron (Fe), copper (Cu) and silver (Ag) nanoparticles (NPs) from the extract of Syzygium cumini leaves. The obtained metal NPs were categorized using UV/Vis, SEM, TEM, FTIR and EDX-ray spectroscopy techniques. The Fe-, Cu- and Ag-NPs were crystalline, spherical and size ranged from 40-52, 28-35 and 11-19 nm, respectively. The Ag-NPs showed excellent antimicrobial activities against methicillin- and vancomycin-resistance Staphylococcus aureus bacterial strains and Aspergillus flavus and A. parasiticus fungal species. Furthermore, the aflatoxins (AFs) production was also significantly inhibited when compared with the Fe- and Cu-NPs. In contrast, the adsorption results of NPs with aflatoxin B1 (AFB1) were observed as following order Fe->Cu->Ag-NPs. The Langmuir isotherm model well described the equilibrium data by the sorption capacity of Fe-NPs (105.3 ng mg-1), Cu-NPs (88.5 ng mg-1) and Ag-NPs (81.7 ng mg-1). The adsorption was found feasible, endothermic and follow the pseudo-second order kinetic model as revealed by the thermodynamic and kinetic studies. The present findings suggests that the green synthesis of metal NPs is a simple, sustainable, non-toxic, economical and energy-effective as compared to the others conventional approaches. In addition, synthesized metal NPs might be a promising AFs adsorbent for the detoxification of AFB1 in human and animal food/feed.
Keyphrases
- aqueous solution
- oxide nanoparticles
- staphylococcus aureus
- metal organic framework
- quantum dots
- visible light
- silver nanoparticles
- endothelial cells
- highly efficient
- magnetic resonance
- climate change
- oxidative stress
- big data
- high resolution
- photodynamic therapy
- biofilm formation
- machine learning
- electronic health record