Synthetic nanomagnetite has been suggested as a potential reactant for the in-situ treatment of contaminated groundwater. Although the application of nanomagnetite for environmental remediation is promising, a full understanding of its reactivity has been deterred by the propensity of the nanoparticles to aggregate and form clusters. To characterize the factors responsible for this aggregation behavior, we determined the magnetic properties of magnetite using a superconducting quantum interference device (SQuID). Importantly, because magnetite readily reacts with O2 to produce maghemite, we analyzed the effect of oxidation on its magnetic properties. We observed that oxidation caused a decrease in the saturation magnetization and the anisotrophic barrier of magnetite resulting in less significant magnetic interactions between particles. Consequently, a decrease in the aggregation of magnetite clusters and a potential increase in stability are expected after oxidation. To support these findings, an extended series of experiments to measure the aggregation and the sedimentation of clusters of unoxidized and oxidized magnetite nanoparticles were conducted. Although the individual particle diameter remained constant after oxidation, the cluster size and the aggregation and sedimentation kinetics of magnetite were determined to be different. Oxidized samples of magnetite tended to have lower aggregation rates and were more resistant to sedimentation. These findings can be used to have a better understanding of the overall fate, transport, and reactivity of nanomagnetite, and to gain new insights on its role as a remediation agent in the subsurface environment. / Ph. D.

Superaggregates are clusters formed by diverse aggregation mechanisms at different scales. They can be found in fluidized nanoparticles and soot formation. An aggregate, with a single aggregation mechanism, can be described by the fractal dimension, df , which is the measure of the distribution and configuration of primary particles into the aggregates. Similarly, a su-peraggregate can be analyzed by the different fractal dimensions that are found at each scale. In a fractal structure aggregate, a self-similarity can be identified at different scales and it has a power law relation between the mass and aggregate size, which can be related to properties like density or light scattering. The fractal dimension, df , can be influenced by aggregation mechanism, particles concentration, temperature, residence time, among other variables. More-over, this parameter can help on the estimation of aggregates’ properties which can help on the design of new processes, analyze health issues and characterize new materials.A multi-dimensional soot aggregate was simulated with the following approach. The first aggregation stage was modeled with a Diffusion Limited cluster-cluster aggregation (DLCA) mechanism, where primary clusters with a fractal dimension, df1, close to 1.44 were obtained. Then, the second aggregation stage was specified by Ballistic Aggregation (BA) mechanism, where the primary clusters generated in the first stage were used to form a superaggregate. All the models were validated with reported data on different experiments and computer models. Using the Ballistic Aggregation (BA) model with primary particles as the building blocks, the fractal dimension, df2, was close to 2.0, which is the expected value reported by literature. However, a decrease on this parameter is appreciated using primary clusters, from a DLCA model, as the building blocks because there is a less compact distribution of primary particles in the superaggregate’s structure.On the second aggregation stage, the fractal dimension, df2, increases when the superaggre-gate size increases, showing an asymptotic behavior to 2.0, which will be developed at higher scales. Partial reorganization was implemented in the Ballistic Aggregation (BA) mechanism where two contact points between primary clusters were achieved for stabilization purposes. This implementation showed a faster increase on the fractal dimension, df2, than without par-tial reorganization. This behavior is the result of a more packed distribution of primary clusters in a short range scales, but it does not affect the scaling behavior of multi-dimensional fractal structures. Moreover, the same results were obtained with different scenarios where the building block sizes were in the range from 200 to 300 and 700 to 800 primary particles.The obtained results demonstrate the importance of fractal dimension, df , for aggregate characterization. This parameter is powerful, universal and accurate since the identification of the different aggregation stages in the superaggregate can increase the accuracy of the estimation of properties, which is crucial in physics and process modeling.