Abstract

The use of silver nanoparticles (NPs) in medical, industrial and agricultural fields is becoming more widespread every year. This leads to an increasing number of experimental toxicological and microbiological studies of silver NPs aimed at establishing the risk–benefit ratio for their application. The following key parameters affecting the biological activity of silver dispersions are traditionally taken into consideration: mean diameter of NPs, surface potential of NPs and equilibrium concentration of Ag+. These characteristics are mainly predetermined by the chemical nature of the capping agent used for stabilization. However, the extent to which they influence the biological activity and the toxicity of silver NPs varies greatly. In this work, dispersions of silver NPs stabilized with a wide array of substances of different chemical nature were used for quantitative evaluation of whether the various measurable properties of silver NPs fit as descriptors of linear QNAR (quantitative nanostructure–activity relationship) models for silver NP toxicity evaluation with respect to a model eukaryotic microorganism—Saccharomyces cerevisiae yeast cells. It was shown that among the factors that determine silver NP toxicity, the charge of particles, their colloidal stability and the ability to generate Ag+ ions carry more importance than the descriptors related to the particle size. A significant synergistic effect between the ζ-potential and the colloidal stability of silver NPs on their toxicity was also discovered. Following this, a new descriptor has been proposed for the integral characterization of the silver dispersion colloidal stability. According to the obtained data, it can be considered applicable for building QNAR models of higher efficacy. The validity testing of the proposed model for theoretical prediction of silver NP toxicity using a wide range of living organisms has shown that this new descriptor correlates with toxicity much better compared to most traditionally used descriptors. Consequently, it seems promising in terms of being used not only in situations involving the rather narrow array of the objects tested, but also for the construction of silver NP toxicity models with respect to other living organisms.

Highlights

  • Metal nanoparticles (NPs), especially silver NPs, are widely used in production of various household and industrial goods [1,2,3]

  • Similar with QSAR models, which are used for organic compound activity prediction, the descriptors typically used to make linear QNAR models as well as models based on neural net methodology characterize the nanoobject structure: the chemical composition of a nanoparticle core, the capping agent structure and similar characteristics [19,20,22,23,24,25,26]

  • Eukaryotic S. cerevisiae cells and the aqueous dispersions of silver NPs stabilized with an uncharacteristically wide array of positively and negatively charged surfactants and polymers as well as their uncharged counterparts were used for quantitative evaluation of whether the Nanomaterials 2020, 10, x theNavnaomriaoteurisalse2x0p2e0,r1im0, 1e4n5t9ally measured properties of silver NPs are suitable as QNAR linear model descriptors for toxicity assessment of silver NPs

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Summary

Introduction

Metal nanoparticles (NPs), especially silver NPs, are widely used in production of various household and industrial goods [1,2,3]. Eukaryotic S. cerevisiae cells and the aqueous dispersions of silver NPs stabilized with an uncharacteristically wide array of positively and negatively charged surfactants and polymers as well as their uncharged counterparts were used for quantitative evaluation of whether the Nanomaterials 2020, 10, x theNavnaomriaoteurisalse2x0p2e0,r1im0, 1e4n5t9ally measured properties of silver NPs are suitable as QNAR (quantitat3ivoef 22 nanostructure–activity relationship) linear model descriptors for toxicity assessment of silver NPs. We conducted further assessment of the theoretical significance of various descriptors using exvpaerriiomuesnteaxlpdeartiamreenlatatelldytomtehaesutorxeidcitpyroopf esirltvieesr NofPssiwlviethr rNesPpsecatretoszueibtarabflieshasemQbNryAoRs (D(qaunaino trietraitoi,ve frensahnwoastterur cfitsuhreb–ealocntigviintyg rtoeltahtieoCnyshpirpin) ildinaeeaframmiolyd)e,lmdyecseclriiapltpohrsytfooprattohxoicgietnyicasfsuensgsim(AenltteronfasriilavseorlaNniPs. anWdeRhailzsooctcoonniadusocltaendi)faunrdthferresahswseastsemr ceynatnoofbtahcetetrhiaeoSryentieccahlocsyigstnisifiscpa.nPcCeCof68v0a3r.ious descriptors using experimental data related to the toxicity of silver NPs with respect to zebrafish embryos To determine the value of β after the background subtraction, the calculation of the experimental X-ray peak (111) of silver using the pseudo-Voigt function was conducted

Spectrophotometric Measurements
Experimental Evaluation of the Yeast Growth Suppression
Experimental Evaluation of the Mycelial Fungi Growth Suppression
Assessment of the Silver NP Activity against Cyanobacteria
Building of Linear QNAR Models Based on Proposed Descriptors
Danio Rerio Embryos
Mycelial Fungi and Cyanobacteria
Solani
Conclusions
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