Hypothesis: Charge-stabilized colloidal cellulose nanocrystals (CNCs) can self-assemble into higher-ordered chiral nematic structures by varying the volume fraction. The assembly process exhibits distinct dynamics during the isotropic to liquid crystal phase transition, which can be elucidated using X-ray photon correlation spectroscopy (XPCS). Experiments: Anionic CNCs were dispersed in propylene glycol (PG) and water spanning a range of volume fractions, encompassing several phase transitions. Coupled with traditional characterization techniques, XPCS was conducted to monitor the dynamic evolution of the different phases. Additionally, simulated XPCS results were obtained using colloidal rods and compared with the experimental data, offering additional insights into the dynamic behavior of the system. Findings: The results indicate that the particle dynamics of CNCs undergo a stepped decay in three stages during the self-assembly process in PG, coinciding with the observed phases. The phase transitions are associated with a total drop of Brownian diffusion rates by four orders of magnitude, a decrease of more than a thousand times slower than expected in an ideal system of repulsive Brownian rods. Given the similarity in the phase behaviors in CNCs dispersed in PG and in water, we hypothesize that these dynamic behaviors can be extrapolated to other polar solvent environments. Importantly, these findings represent the direct measurement of CNC dynamics using XPCS, underscoring the feasibility of directly assessing the dynamic behavior of other rod-like colloidal suspensions.

Biopolymers and their networks are fundamental to numerous biological and synthetic systems, with applications ranging from extracellular matrices in biological tissues to engineered nanostructured materials like cellulose-based nanocomposites. Understanding the dynamics of biopolymers in these networks is crucial due to their potential in material science and biotechnology, such as in developing sustainable materials and enhancing drug delivery mechanisms. The intricate network structures, from fibrous matrices in natural systems to designed frameworks in advanced materials, play a pivotal role in determining the mechanical and transport properties of the overall system.

This thesis delves into the dynamics of biopolymers, focusing specifically on the diffusion processes within such networks. The complexity of biopolymer behavior in networked environments involves multiple factors including polymer stiffness, network structure, and the interaction between biopolymer components. The diffusion of biopolymer fibres themselves, as well as nanoparticles within these networks, is explored through detailed coarse-grained molecular dynamics simulations. These simulations aim to model the nuanced interaction dynamics that influence diffusion, providing insights into how these factors affect biopolymer networks' rheological properties and functional capabilities.

This work contributes to the broader understanding of how biopolymers behave in complex environments by investigating the fundamental mechanisms of diffusion in biopolymer networks. It addresses the need for a deeper exploration of biopolymer dynamics to inform the design and synthesis of new biomaterials and bio-based materials. The findings from this thesis are expected to offer implications for enhancing the functionality of biopolymer-based systems in various applications, from improving the efficiency of biomaterials used in medical applications to optimizing the performance of bio-based composites in industrial applications. 

The diffusion and interaction dynamics of charged nanoparticles (NPs) within charged polymer networks are crucial for understanding various biological and biomedical applications. Using a combination of coarse-grained molecular dynamics simulations and experimental diffusion studies, we investigate the effects of the NP size, relative surface charge density (ζ), and concentration on the NP permeation length and time. We propose a scaling law for the relative diffusion of NPs with respect to concentration and ζ, highlighting how these factors influence the NP movement within the network. The analyses reveal that concentration and ζ significantly affect NP permeation length and time, with ζ being critical, as critical as concentration. This finding is corroborated by controlled release experiments. Further, we categorize NP dynamics into sticking, sliding, and bouncing regimes, demonstrating how variations in ζ, concentration, and NP size control these behaviors. Through normalized attachment time (NAT) analyses, we elucidate the roles of electrostatic interactions, steric hindrance, and hydrodynamic forces in governing NP dynamics. These insights provide guidance for optimizing NP design in targeted drug delivery and advanced material applications, enhancing our understanding of NP behavior in complex environments.

Biopolymer network dynamics play a significant role in both biological and materials science. This study focuses on the dynamics of cellulose nanofibers as a model system given their relevance to biology and nanotechnology applications. Using large-scale coarse-grained simulations with a lattice Boltzmann fluid coupling, we investigated the reptation behavior of individual nanofibers within entangled networks. Our analysis yields essential insights, proposing a scaling law for rotational diffusion, quantifying effective tube diameter, and revealing release mechanisms during reptation, spanning from rigid to semiflexible nanofibers. Additionally, we examine the onset of entanglement in relation to the nanofiber flexibility within the network. Microrheology analysis is conducted to assess macroscopic viscoelastic behavior. Importantly, our results align closely with previous experiments, validating the proposed scaling laws, effective tube diameters, and onset of entanglement. The findings provide an improved fundamental understanding of biopolymer network dynamics and guide the design of processes for advanced biobased materials. 

We demonstrate a framework of interpreting data from x-ray photon correlation spectroscopy experiments with the aid of numerical simulations to describe nanoscale dynamics in soft matter. This is exemplified with the transport of passive tracer gold nanoparticles in networks of charge-stabilized cellulose nanofibers. The main structure of dynamic modes in reciprocal space could be replicated with a simulated system of confined Brownian motion, a digital twin, allowing for a direct measurement of important effective material properties describing the local environment of the tracers. 

In this paper, cavitating flows are characterized in 29 microfluidic devices in order to achieve a comprehensive perspective regarding flow patterns in microscale, which is crucial in the applications, such as energy harvesting and biomedical treatment. While the assessment of size effects is vital for the design and development of microfluidic devices involving phase change, surface/sidewall roughness and pressure pulses as a result of nanomechanical oscillations increase the performance with respect to cavitation by providing more cavitation bubbles. A typical device generates cavitating flows under different conditions (from inception to choked flow). In this device, a restrictive element and a big channel downstream of the restrictive element--where the cavitation is formed and developed--are included. The cavitating flows are obtained inside 24 sidewall roughened and 5 surface roughened/plain microfluidic devices at different pressure drops. The length and height of the sidewall roughness elements are varied to achieve the most optimum performance in terms of cavitation generation. Moreover, different surface roughened and plain devices are considered to provide a comprehensive overview of cavitation generation in microscale. The results show that sidewall roughness elements have a remarkable effect on the cavitation inception and flow patterns. [2019-0025] IEEE