A Nickel-Containing Polyoxomolybdate as an Efficient Antibacterial Agent for Water Treatment.
Jiangnan ChangMing-Xue LiJiyuan DuMin MaCuili XingLin SunPengtao MaPublished in: International journal of molecular sciences (2022)
In view of the water pollution issues caused by pathogenic microorganisms and harmful organic contaminants, nontoxic, environmentally friendly, and efficient antimicrobial agents are urgently required. Herein, a nickel-based Keggin polyoxomolybdate [Ni(L)(HL)] 2 H[PMo 12 O 40 ] 4H 2 O ( 1 , HL = 2-acetylpyrazine thiosemicarbazone) was prepared via a facile hydrothermal method and successfully characterized. Compound 1 exhibited high stability in a wide range of pH values from 4 to 10. 1 demonstrated significant antibacterial activity, with minimum inhibitory concentration (MIC) values in the range of 0.0019-0.2400 µg/mL against four types of bacteria, including Staphylococcus aureus ( S. aureus ), Bacillus subtilis ( B. subtilis ), Escherichia coli ( E. coli ), and Agrobacterium tumefaciens ( A. tumefaciens ). Further time-kill studies indicated that 1 killed almost all (99.9%) of E. coli and S. aureus. Meanwhile, the possible antibacterial mechanism was explored, and the results indicate that the antibacterial properties of 1 originate from the synergistic effect between [Ni(L)(HL)] + and [PMo 12 O 40 ] 3- . In addition, 1 presented effective adsorption of basic fuchsin (BF) dyes. The kinetic data fitted a pseudo-second-order kinetic model well, and the maximum adsorption efficiency for the BF dyes (29.81 mg/g) was determined by the data fit of the Freundlich isotherm model. The results show that BF adsorption was dominated by both chemical adsorption and multilayer adsorption. This work provides evidence that 1 has potential to effectively remove dyes and pathogenic bacteria from wastewater.
Keyphrases
- aqueous solution
- escherichia coli
- staphylococcus aureus
- silver nanoparticles
- bacillus subtilis
- metal organic framework
- electronic health record
- reduced graphene oxide
- heavy metals
- big data
- risk assessment
- human health
- data analysis
- machine learning
- combination therapy
- klebsiella pneumoniae
- essential oil
- multidrug resistant
- air pollution
- replacement therapy
- deep learning
- health risk assessment