Date of Award

5-2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Bioengineering

Committee Chair/Advisor

Bruce Z. Gao, PhD

Committee Member

David Karl Karig, PhD

Committee Member

Lucas P. Schmidt, PhD

Abstract

Various microbial communities in marine biofilms cause biofouling on submerged surfaces, posing challenges to marine industries. Despite their ecological and economic importance, biofilms' biomechanical and biochemical responses to hydrodynamic shear stress are insufficiently understood, particularly in dynamic flow conditions. To fill this gap, our study uses an innovative fiber-optic hyperspectral imaging (HSI) system and a supercontinuum laser source to examine marine biofilms' structure, composition, and detachment behavior at different shear stress levels.

To detect spectral and spatial heterogeneity in live biofilms without labeling, we created a custom imaging pipeline that captures reflectance spectra in the 600-850 nm range, targeting microbial pigments and extracellular polymeric substances. We used baseline spectral mapping, supervised machine learning for bacterial strain classification, and real-time imaging in a controlled flow chamber to observe morphological transitions and pigment redistribution in laminar, low-turbulent, and high-turbulent shear conditions.

Results show biofilms under high-shear stress increase density and viscosity, while low-shear conditions promote mound-shaped microcolonies. Two-strain biofilms had more substantial spectral shifts than single-strain systems, indicating enhanced mechano-chemical responses from interspecies interactions.

This study advances hyperspectral imaging in marine microbiology by enabling stain-free, high-resolution biofilm spectral mapping under dynamic flow conditions. Understanding shear conditions for structural alteration and detachment can inform antifouling strategies for marine engineering, biomedical device sterilization, and industrial pipeline management. This research could expand to include environmental variables like temperature and nutrient gradients to create predictive models of biofilm resilience.

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