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Applying Classical Particle Aggregation Modeling Techniques to Investigate the Heteroaggregation of Environmental Biocolloids

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Date
2022
Author
Hicks, Ethan Conley
Advisor
Wiesner, Mark R.
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Abstract

As biological challenges to environmental health, such as the proliferation of antibiotic resistance genes, continue to emerge there is a greater need for the generation of models capable of predicting the fate and transport of biological particles. For over 100 years, Smoluchowski’s watershed 1917 work has provided a foundation for the construction of such models. Classically, this approach has been used for inert nano-scale particles. However, given that several of the most pressing challenges are biological in nature, it is imperative that predictive models of particle transport be adapted to include particles with a biological signature.This work uses modified Smoluchowskian aggregation modeling parameters to investigate the transport of three primary biological particles: bacteriophages, extracellular vesicles, and extracellular DNA, each of them often existing on the nanoscale. This was done by 1) using modified Smoluchowskian aggregation parameters to predict phage-induced host lysis, 2) characterize phage-kaolinite heteroaggregation, and 3) construct a multi-particle predictive model incorporating the heteroaggregation of all three particle types. This work found that modified Smoluchowskian aggregation parameters used in concert with appropriate population balances were largely successful in predicting such particles’ transport and provided unique insight into possible design features for engineered environmental systems.

Description
Dissertation
Type
Dissertation
Department
Civil and Environmental Engineering
Subject
Environmental engineering
Permalink
https://hdl.handle.net/10161/25778
Citation
Hicks, Ethan Conley (2022). Applying Classical Particle Aggregation Modeling Techniques to Investigate the Heteroaggregation of Environmental Biocolloids. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/25778.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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