The widespread availability of nano-enabled products in the global market may lead to the release of a substantial amount of engineered nanoparticles in the environment, which frequently display drastically different physiochemical properties than their bulk counterparts. The purpose of the study was to evaluate the impact of citrate-stabilised silver nanoparticles (AgNPs) on the plant Arabidopsis thaliana at three levels, physiological phytotoxicity, cellular accumulation and subcellular transport of AgNPs. The monodisperse AgNPs of three different sizes (20, 40 and 80 nm) aggregated into much larger sizes after mixing with quarter-strength Hoagland solution and became polydisperse. Immersion in AgNP suspension inhibited seedling root elongation and demonstrated a linear dose-response relationship within the tested concentration range. The phytotoxic effect of AgNPs could not be fully explained by the released silver ions. Plants exposed to AgNP suspensions bioaccumulated higher silver content than plants exposed to AgNO3 solutions (Ag(+) representative), indicating AgNP uptake by plants. AgNP toxicity was size and concentration dependent. AgNPs accumulated progressively in this sequence: border cells, root cap, columella and columella initials. AgNPs were apoplastically transported in the cell wall and found aggregated at plasmodesmata. In all the three levels studied, AgNP impacts differed from equivalent dosages of AgNO3.

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Predictive models are beneficial tools for researchers to use in prioritizing nanoparticles (NPs) for toxicological tests, but experimental evaluation can be time-consuming and expensive, and thus, priority should be given to tests that identify the NPs most likely to be harmful. For characterization of NPs, the physical binding of NPs to DNA molecules is important to measure, as interference with DNA function may be one cause of toxicity. Here, we determined the interaction energy between 12 types of NPs and DNA based on the Derjaguin-Landau-Verwey-Overbeek (DLVO) model and then predicted the affinity of the NPs for DNA. Using the single-molecule imaging technique known as atomic force microscopy (AFM), we experimentally determined the binding affinity of those NPs for DNA. Theoretical predictions and experimental observations of the binding affinity agreed well. Furthermore, the effect of NPs on DNA replication in vitro was investigated with the polymerase chain reaction (PCR) technique. The results showed that NPs with a high affinity for DNA strongly inhibited DNA replication, whereas NPs with low affinity had no or minimal effects on DNA replication. The methodology here is expected to benefit the genotoxicological testing of NPs as well as the design of safe NPs.

To describe the aggregation kinetics of nanoparticles (NPs) in aqueous dispersions, a new equation for predicting the attachment efficiency is presented. The rationale is that at nanoscale, random kinetic motion may supersede the role of interaction energy in governing the aggregation kinetics of NPs, and aggregation could occur exclusively among the fraction of NPs with the minimum kinetic energy that exceeds the interaction energy barrier (E(b)). To justify this rationale, we examined the evolution of particle size distribution (PSD) and frequency distribution during aggregation, and further derived the new equation of attachment efficiency on the basis of the Maxwell-Boltzmann distribution and Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. The new equation was evaluated through aggregation experiments with CeO(2) NPs using time-resolved-dynamic light scattering (TR-DLS). Our results show that the prediction of the attachment efficiencies agreed remarkably well with experimental data and also correctly described the effects of ionic strength, natural organic matter (NOM), and temperature on attachment efficiency. Furthermore, the new equation was used to describe the attachment efficiencies of different types of engineered NPs selected from the literature and most of the fits showed good agreement with the inverse stability ratios (1/W) and experimentally derived results, although some minor discrepancies were present. Overall, the new equation provides an alternative theoretical approach in addition to 1/W for predicting attachment efficiency.

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