4.6 Article

Influential parameters of surface waters on the formation of coating on TiO2 nanoparticles under natural conditions

期刊

ENVIRONMENTAL SCIENCE-NANO
卷 8, 期 11, 页码 3153-3166

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ROYAL SOC CHEMISTRY
DOI: 10.1039/d1en00431j

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资金

  1. German Research Foundation, research unit INTERNANO [FOR1536, SCHA849/16]

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Formation of natural coatings on nanoparticles in aquatic media is influenced by the dynamic composition of the medium. This study used the dialysis bag method to expose n-TiO(2) to 18 surface waters with varying physicochemical parameters, revealing important relationships between surface water parameters and the composition of the natural coating.
Formation of natural coatings on nanoparticles in aquatic media is highly influenced by the dynamic composition of the medium. Hundreds of research articles were dedicated to this question and an in-depth understanding of the underlying mechanisms has been obtained for some materials. Nonetheless, it is still unknown to which extent these results obtained under controlled conditions in the laboratory can be extrapolated to environmental conditions. In this study, we used the recently introduced dialysis bag method to expose n-TiO(2)in situ to 18 surface waters with contrasting physicochemical parameters by varying the sampling location and season and characterized the resulting natural coatings. ATR-FTIR and 2D-IR spectroscopy (thermal perturbation), thermogravimetry, and X-ray photoelectron spectroscopy analyses of the exposed nanoparticles revealed clear differences in the composition of the natural coatings in the presence of both organic (carboxylate and aromatic groups) and inorganic (carbonate) components. Subsequently, multivariate data analysis (correlation matrix, principal component analysis, and random forest) was used to explore the relationships between surface water parameters and the composition of the natural coating; the results enabled us to identify pH, electrical conductivity, and Ca2+-Mg2+ concentration as the three most important parameters for predicting the composition of the natural coating of TiO2 nanoparticles.

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