Fractal aggregation and disaggregation of newly formed iron(iii) (hydr)oxide nanoparticles in the presence of natural organic matter and arsenic

Abstract

Water chemistry affects the nucleation kinetics, precipitate morphology, and quantity of ironIJIII) (hydr)oxide nanoparticles, directly impacting the reactive surface area of geomedia and fate of associated waterborne contaminants. In this study, we utilized in situ grazing-incidence small angle X-ray scattering (GISAXS) and complementary ex situ techniques to investigate heterogeneous ironIJIII) (hydr)oxide nucleation on quartz in the presence of natural organic matter (NOM) and arsenate. Results indicate unique fractal aggregation behavior in the systems containing NOM and precipitating ironIJIII) (hydr)oxide nanoparticles. Furthermore, the coexistence of arsenic and NOM lead to the formation of two distinct particle size ranges: larger particles dominated by arsenic effects, and smaller particles dominated by NOM effects. These new findings provide important implications for understanding the nucleation, growth, and aggregation of ironIJIII) (hydr)oxides in aqueous systems where NOM is present, such as natural surface waters and water and wastewater treatment plants. This study also offers new insight into how NOM-associated ironIJIII) (hydr)oxides can interact with aqueous contaminants such as arsenate.A better understanding of the fate and transport of ironIJIII) (hydr)oxide nanoparticles is crucial for controlling trace toxic contaminants in natural and engineered aquatic systems. Despite this, their nucleation, growth, and aggregation are not well understood. We studied the impacts of natural organic matter and arsenate on newly-forming ironIJIII) (hydr)oxide in situ and found that these constituents altered the nanoparticle size and aggregation state. Our findings allow us to elucidate how contaminants affect the aggregation or disaggregation of ironIJIII) (hydr)oxide nanoparticles in aqueous environments, where organic matter will be nearly ubiquitously present. Outcomes can also be informative in developing reactive transport models that better predict the ability of ironIJIII) (hydr)oxides to attenuate aqueous contaminants.

Fractal dimension of antibody‐PEG precipitate: Light microscopy for the reconstruction of 3D precipitate structures

“…Robinson et al. described the kinds of rate limitation and resulting fractal dimension and according to the literature, one would expect rather low fractal dimensions of 1.7 for a reaction limited precipitation, while a fractal dimension of 2.4 would be expected for diffusion limited precipitation. These results have to be compared to samples with low to no shear force because the analysis done by Robinson et al.…”

“…The fractal dimension of precipitates can be used to describe the complex structure of precipitates, but it has also been used in the literate to infer the rate limiting step of the precipitation from the resulting fractal dimension. Robinson et al [37] described the kinds of rate limitation and resulting fractal dimension and according to the literature, one would expect rather low fractal dimensions of 1.7 for a reaction limited precipitation, while a fractal dimension of 2.4 would be expected for diffusion limited precipitation. These results have to be compared to samples with low to no shear force because the analysis done by Robinson et al does not take shear stress or agitated mixing into account.…”

Characterisation and controls on mineral-sorbed organic matter from a variety of groundwater environments

“…A batch system was set up by mixing 40 ml of the field water samples with a range of clean mineral mass (1, 2.5, 5, 7.5 and 10 g) in 50 ml pre-acid washed (10% HCl) falcon tubes on a rotator (2.5 rpm) in a dark and temperature-controlled (20⁰C ± 1⁰C) room. Samples were collected after 1 h (Neil et al, 2016) for DOC analysis by LC-OCD. The concentration of each DOC fraction is recorded after control correction and the sorbed amount were quantified by using the concentration after correction multiplied by volume of the samples used.…”

Improving the prediction of environmental fate of engineered nanomaterials by fractal modelling

“…Fractal image analysis methods were used to classify organic and inorganic structures (Kimori et al, 2011;Kong et al, 2014;Papanicolaou et al, 2012;Smith et al, 1996, Neil andCurtis, 1997). Neil et al (2016) studied fractal structures of iron oxide nanoparticles aggregated with natural organic matter and arsenate to better predict the sedimentation rates in the environment. The oil industry recently adopted fractals to enhance environmental kinetics models (Mohammadi et al, 2016;Sterling et al, 2005).…”

“…To better represent the complexity of nanoaggregate structures in the environment efforts were made to include Fractal Dimension (FD) into the ENMs kinetic models (Arvidsson et al, 2011;Chowdhury et al, 2013;Neil et al, 2016;Praetorius et al, 2012). Nanoparticle (NP) aggregates are less dense material packings in comparison to monocrystals and form structures that are difficult to describe by means of Euclidian geometry.…”