Physical and Deep-Learning-Based Explorations of Microbe-Mediated Reactive Transport Processes in Porous Media Across Scales
type: CreativeWork
description: Reactive transport (RT) simulators are important tools often used by researchers to gain insights into subsurface processes. These multi-physics simulations attempt to represent many hydro-biogeochemical phenomena, but they often fall short in terms of computational speed and physical accuracy. This dissertation provides several tools and conceptual advancements that can be used to improve the speed and accuracy of RT simulations and further our understanding of their outputs. Specifically, this work investigates microbial motility in porous microfluidic devices, a comparison of particle tracking methods in porous media, and an investigation of biomass growth and chromium reduction in the hyporheic zone. Furthermore, this dissertation details the development and performance-testing of deep-learning-based tools for the extraction of motion statistics from videos of particles and the upscaling of RT simulations. Overall, new tools and insights are provided to help improve environmental management strategies, such as bioremediation of contaminated groundwater or improved understanding of nutrient cycles in water systems.This dissertation advances our understanding of microbe-mediated reactive transport processes through a multi-scale approach that combines experimental observations, computational modeling, and innovative deep learning techniques. At the micro scale, experimental investigations reveal how different bacterial motility mechanisms respond to varying flow conditions, with peritrichous flagella enabling more resilient motility under higher flow rates compared to monotrichous flagella or pili. These findings provide crucial insights for developing more accurate models of microbial transport in subsurface environments. To improve micro-scale investigations of bacterial transport, this dissertation gives a comparison of particle tracking (PT) methods and presents a novel deep-learning-based PT method. The comparison between PT methods provides guidance for futu…
abstract: Reactive transport (RT) simulators are important tools often used by researchers to gain insights into subsurface processes. These multi-physics simulations attempt to represent many hydro-biogeochemical phenomena, but they often fall short in terms of computational speed and physical accuracy. This dissertation provides several tools and conceptual advancements that can be used to improve the speed and accuracy of RT simulations and further our understanding of their outputs. Specifically, this work investigates microbial motility in porous microfluidic devices, a comparison of particle tracking methods in porous media, and an investigation of biomass growth and chromium reduction in the hyporheic zone. Furthermore, this dissertation details the development and performance-testing of deep-learning-based tools for the extraction of motion statistics from videos of particles and the upscaling of RT simulations. Overall, new tools and insights are provided to help improve environmental management strategies, such as bioremediation of contaminated groundwater or improved understanding of nutrient cycles in water systems.This dissertation advances our understanding of microbe-mediated reactive transport processes through a multi-scale approach that combines experimental observations, computational modeling, and innovative deep learning techniques. At the micro scale, experimental investigations reveal how different bacterial motility mechanisms respond to varying flow conditions, with peritrichous flagella enabling more resilient motility under higher flow rates compared to monotrichous flagella or pili. These findings provide crucial insights for developing more accurate models of microbial transport in subsurface environments. To improve micro-scale investigations of bacterial transport, this dissertation gives a comparison of particle tracking (PT) methods and presents a novel deep-learning-based PT method. The comparison between PT methods provides guidance for futu…
author: Berghouse, Marc Jonathon
language: en_US
language: English
keywords: Bioremediation, Machine Learning, Microbial Motility, Particle Tracking, Porous Media, Reactive Transport
date published: 2024
date modified: 2026-07-27T21:47:47Z
provider: University of Nevada - Reno
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Nevada - Reno