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Issue 89, 2017, Issue in Progress
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An experiment-based model quantifying antimicrobial activity of silver nanoparticles on Escherichia coli

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Abstract

Silver nanoparticles (AgNPs) are well known to exhibit antimicrobial effects through plausible interactions with proteins and/or deoxyribonucleic acid inside the bacteria. Yet a quantitative understanding on the antimicrobial activities of AgNPs remains obscure. Here we conducted in-depth kinetic growth assays and colony-forming unit (CFU) assays on Escherichia coli (E. coli) cultured in AgNPs or Ag ion-containing growth media. Compared to the Ag-absent culture medium, it was found that the growth rate of the bacteria remained unaffected but the lag time of the bacterial growth was extended due to the presence of AgNPs or Ag ions. From the CFU-based time-kill curves, we observed that fractions of E. coli were killed exponentially in the presence of AgNPs or Ag ions. Based on the experimental data, a quantitative model was established to describe the antimicrobial activity of AgNPs. The predictions from this model agree well with the experimental results. We also showed that the parameters in our model as well as their dependence on the concentrations of Ag and bacteria could, in turn, be determined experimentally. It is expected that our quantitative model and the associated parameters provide an alternative means to minimum inhibitory concentration values for characterizing the antimicrobial activities of Ag ions and AgNPs.

Graphical abstract: An experiment-based model quantifying antimicrobial activity of silver nanoparticles on Escherichia coli

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Publication details

The article was received on 21 Sep 2017, accepted on 02 Dec 2017 and first published on 12 Dec 2017


Article type: Paper
DOI: 10.1039/C7RA10495B
RSC Adv., 2017,7, 56173-56182
  • Open access: Creative Commons BY-NC license
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    An experiment-based model quantifying antimicrobial activity of silver nanoparticles on Escherichia coli

    M. A. Haque, R. Imamura, G. A. Brown, V. R. Krishnamurthi, I. I. Niyonshuti, T. Marcelle, L. E. Mathurin, J. Chen and Y. Wang, RSC Adv., 2017, 7, 56173
    DOI: 10.1039/C7RA10495B

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