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Aggregation and dissolution of engineering nano Ag and ZnO pretreated with natural organic matters in the simulated lung biological fluids

Zhong, Laijin, Hu, Xin, Cao, Zhaoming, Wang, Hongwei, Chen, Yijun, Lian, Hong-zhen
Chemosphere 2019 v.225 pp. 668-677
Raman spectroscopy, X-ray diffraction, breathing, citric acid, fulvic acids, humans, ions, lungs, nanoparticles, nanosilver, particle size, risk, silver, tartaric acid, transmission electron microscopes, zinc, zinc oxide
The increasing application of engineered nanoparticles such as silver nanoparticles (nAg) and zinc oxide nanoparticles (nZnO), results in their accumulation in environmental media. The environmental natural organic matter (NOM) adsorbed by these nanoparticles may have great effects on the aggregation and dissolution of metall ions, which are interesting and important for the assessment of the inhalation risks of these airborne suspended NOM-coated nanoparticles to humans. Therefore, the aggregation and dissolution of nAg and nZnO pretreated with citric acid (CA), tartaric acid (TA) and fulvic acid (FA) in simulated lung biological fluids (artificial lysosomal fluid (ALF) and Gamble Solution) were investigated. The surface properties, morphology and size of the NOM-treated ENPs changed, but the crystalline phase was relatively stable when observed using surface-enhanced Raman scattering, transmission electron microscope, and X-ray diffraction. NOM treatment had no significant influence on the particle size of NOM-treated nAg and nZnO except for a decrease in the size of CA-treated nAg, and it could not promote the aggregation of NOM-treated nAg and nZnO except for the aggregation of TA-treated nAg in Gamble Solution or TA-treated nZnO in ALF. CA- and FA-treatments promoted the release of Zn2+ and Ag+, respectively, while no promotion was observed after TA-treatment. Therefore, NOM affects the release of Zn2+ and Ag+ from NOM-treated nAg and nZnO but does not promote the aggregation of NOM-treated nAg and nZnO, which influences the inhalation risk-based assessment.