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A parallel and adaptive interface tracking approach for evolving geometry problems
No full textDecember 2019School of EngineeringNumerical simulations employing an interface tracking approach are desired in many evolving-geometry applications, particularly those involving fluid-structure and/or multiphase interactions with moving boundaries/interfaces. For example, a projectile fired from a cannon or a solid combusting into gases. In these problems, interface tracking can be crucial to accurately model and capture the interface physics, for example, a shear layer or discontinuous variables (such as density or normal velocity) at the interface. A necessary capability in an interface tracking approach is the evolution of the computational domain and mesh while maintaining a desired level of accuracy. This thesis presents a parallel and adaptive interface tracking approach for evolving geometry problems. In the current approach, the computational domain is defined using a geometric model which is updated and maintained as dictated by the analysis and the mesh is updated to be consistent with the geometric model at every step. At the interface, a frame that moves at the interface velocity is employed, while an arbitrary Lagrangian-Eulerian (ALE) frame is used elsewhere with arbitrary mesh motion. A combination of mesh motion and mesh modification is employed to update the mesh. Mesh motion is applied until mesh deformation leads to undesirable elements, at which point local mesh modification/adaptation is used. A mesh size field, which describes the desired mesh resolution over the domain, is used to drive mesh adaptation. In addition, the mesh size field is determined at every time step using a VMS-based explicit error estimator when discretization error control is applied. Further, during adaptation the local structure of the highly anisotropic layered elements in the mesh is maintained. All steps are performed on partitioned meshes on distributed-memory parallel computers. We demonstrate the effectiveness of the current approach for problems with large motion or deformation in the geometry, where changes in the location and/or size of the geometric features are significant (i.e., of the same order as the characteristic length), for example, a projectile moving from one end of the cannon to the other or phase change resulting in a significant volume reduction of a phase.Ph
Elucidating the role of multimodal ligand surfaces in protein chromatography using molecular dynamics simulations
No full textAugust 2022School of EngineeringOver the past several years, protein-based therapeutics have revolutionized the pharmaceutical industry, with applications for treatment of a range of diseases from autoimmune to cancer. This rise has been accompanied by a transition in the manufacturing industry, emphasizing the need for more efficient purification methods. A new mode of chromatography, Multimodal (MM) chromatography, has been shown to achieve unique selectivities for difficult separations. Multimodal chromatography employs ligands with adjacent moieties of different classes to present a range of possible interactions for proteins. These ligands often consist of charged and hydrophobic moieties that allow for a synergistic combination of electrostatic, hydrogen bonding, and hydrophobic interactions with a protein. With optimally designed geometries and ligand densities, these ligands are able to address challenging purification problems that were cumbersome using traditional single-mode chromatography. While multimodal chromatography is being adopted into the industry, the molecular origins of the associated selectivity in different proteins systems are still not well understood. Consequently, prediction of chromatographic behavior in these systems is difficult, impeding rapid induction of these materials into purification platforms.This thesis addresses these challenges by investigating the molecular interactions underlying multimodal phenomena using molecular dynamics simulations. First, we have investigated the behavior of a series of commercially relevant multimodal ligands when immobilized on a surface. Moiety distribution maps, cluster size distributions and patch area distributions have been used to characterize ligand-ligand self-association. These calculations have also been performed at a higher salt concentration, to mimic an elution condition, and ligand clustering has been found to be unaffected even in the presence of these large number of counterions. This insensitivity to high-salt conditions has allowed us to quantify surface characteristics into novel molecular descriptors for MM-ligand surfaces.
We have then focused on the hydration preferences for these MM-ligand surfaces. Biased simulation methods have been utilized to obtain dehydration free energies and to probe the drying behavior of ligand solvation shells for MM-ligands on low ligand density and high ligand density SAMs. Our results indicate that the hydrophobic nature of self-associating ligands is indeed context dependent and is significant at high immobilization density. In addition, desolvation behavior in a large cuboidal volume adjacent to MM-ligand surfaces has been characterized. Results from these calculations indicate that based on ligand chemistry, density, and point of immobilization, MM-surfaces can present not only distinct surface patterns, but also different hydration behaviors. These results have important implications for our understanding of protein--MM-surface interactions.
Further, interactions of these MM-ligand surfaces with both a small model protein and a commercially relevant therapeutic protein have been studied. The impact of increasing ligand density, and associated ligand self-association, on the protein adsorption free energy have also been investigated. Our results indicated that MM-surfaces with clustering ligands resulted in a marked increase in the adsorption free energy for the small model protein. In addition, a marked decrease in binding free energy was observed for clustering MM-ligand surfaces, when simulated at an elution salt condition. Limiting the self-association tendency of clustering ligands drastically decreased the free energy of adsorption, indicating that ligand-ligand self-association is crucial for these surfaces to effectively bind proteins. On the other hand, for the larger protein with a more diffuse binding region, a significant impact on the adsorption free energy was not observed at the low or high densities. However, subtle differences in the protein-ligand interactions between the clustering and non-clustering surfaces were observed, which may have an impact on selectivity.
These investigations shed light on the role of different kinds of multimodal ligand surfaces in modulating protein--ligand surface interactions, and provide key insights into how ligand chemistry, geometry, and density affect resin surface properties. These studies lay the groundwork for rational design of new multimodal ligands, with desired surface properties, to enhance selectivity and enable efficient separations.Ph
Erie county smile : on exilic media and identity construction
No full textAugust 2021School of Humanities, Arts, and Social SciencesThis dissertation text is intended to provide the historical background and theoretical framing for my doctoral video project, ERIE COUNTY SMILE (2021) and to contextualize the theory and practices that led to the project’s completion. The video project ERIE COUNTY SMILE (running time 27:52, 2021), or ECS, is a short film about the fantasies of a girl working at her family’s nail salon. It is a parody of a Vietnamese-language variety show called Paris By Night (PBN). In ECS, I utilize performance techniques such as humor, satire, parody, and reenactment to stage interventions about PBN. I have selected PBN as a case study because of its notoriety within the Vietnamese community living abroad. I performed as every character within the narrative video (ECS) and corresponding sets via digital imaging and animation with the help of collaborators working remotely. ECS was created entirely during the global COVID-19 pandemic while under lockdown.
In this textual analysis of my practice-based project and research, I will examine: 1) the process of collaboration in a remote setting during the historic 2020 pandemic; 2) the process of producing ECS, from “failures” of early planning to its current iteration; and 3) the source material of PBN, which serves as an impressive cultural media archive. These ideas are laid out in the form of thematic chapters with supporting images. To conduct my exploration, I ground my research approaches, or methodologies, in ethnography and media archive analysis. I used autoethnographic research—an introspective tool that transforms private, insider experience into public, apparent knowledge. I use this method not as a way to expose the intimacies of lived cultural knowledge, but as a way to find a commonality between intimacies that exist in small, mutually isolated cultural pockets usually referred to as a diaspora.Ph
Protein developability and downstream bioprocessing : from predictive tools to mechanistic analysis
No full textAugust 2022School of EngineeringAlthough protein therapeutics, especially monoclonal antibodies (mAb), have raised tremendous attention in recent decades due to their efficacy in treating a wide range of diseases, they often face a number of development challenges during downstream and formulation stages. In downstream, laborious screening of a range of conditions and materials is often required in order to establish effective conditions for removal of impurities. For formulation, mAbs often suffer from poor developability attributes such as high viscosity and low solubility, particularly at elevated concentrations. This thesis addresses these challenges by developing quantitative structure activity relationship (QSAR) based models to predict protein biophysical properties and chromatographic behavior. Additionally, novel molecular descriptors and surface property analyses are employed to gain mechanistic insights into the underlying interactions in these systems. Solubility data determined from high-throughput PEG induced precipitation was used to develop regression and classification models for a set of mAbs. The models were able to effectively estimate mAbs solubility levels. Further, the selected charge-based descriptors of the Fab region, were shown to significantly contribute to these predictions. Interestingly, the two mAb isotypes examined in these models, IgG1 and IgG4, showed dramatically different solubility behavior. Accordingly, two separate QSAR models were developed to accurately predict the solubility levels of each isotype. In addition, the role of electrostatic interactions in determining the solubilities was evaluated by introducing salt into the formulation of a subset of the mAbs. The results indicated that different classes of salt response behaviors were connected to the mAb’s surface charge distributions determined at the different ionic strength conditions. Since IgG4 exhibited more complicated mechanisms related to developability, an in-depth investigation of two homologous IgG4 antibody series varying in solubility and viscosity behavior was conducted. This study employed localized charge and hydrophobicity descriptors along with surface property map analyses to deconvolute the contribution of each mutation residue. The hydrophobicity of exposed mutation residues in the CDR and the charge profile of all the mutation residues were found to be correlated well with viscosity/solubility. To further improve QSAR model robustness and reproducibility, an analysis of the stability of molecular descriptors with respect to protein dynamics was carried out by calculating the descriptor value fluctuations based on a number of Fab conformations. In addition to solubility and viscosity, protein chromatographic behavior in downstream was also studied by developing QSAR models to estimate elution salt concentration in various chromatographic systems. Briefly, predictive models were developed for the elution behavior of: A) an orthogonal model protein set in multimodal cation exchange resins; B) acidic model proteins in a novel guanidine-based resin set; C) bispecific related antibodies in multimodal cation exchange resins. Overall, this work demonstrates the utility of QSAR based methods for estimation of protein developability related properties and downstream chromatographic behavior. The availability of these predictive in silico tools are expected to aid in accelerating process development. In addition, the mechanistic understanding gained from this work will help to shed light on future biological product design with improved developability properties.Ph
Moringa flocculation : towards a low carbon wastewater management system for apparel factories
No full textAugust 2021School of ArchitectureThe global apparel industry is one of the largest consumers as well as polluters of freshwater, contributing to 17% of the total industrial water contamination through the discharge of hazardous dye effluent, chemicals, detergents, and other toxins, exposing workers, communities and marine life to severe health and environmental risks. A major part of the pollution occurs downstream of the apparel industry supply chain, across millions of small unregulated apparel manufacturing factories spread across emerging economies, that lack resources to incorporate existing expensive wastewater treatment systems like membrane reactors or synthetic flocculants. With recent research identifying bio-flocculants in agroforestry and fishing industry waste streams, there lies an opportunity to develop a bottom-up wastewater management systems for small factories, ensuring on-site water contaminant removal as well as reuse of process water for multiple factory applications. Seeds from moringa oleifera, a widely cultivated tree species and cash crop in tropical climates providing multiple additional ecological benefits, has been observed to perform as effectively as synthetic flocculants in treating colored wastewater. Treated effluent can be reused for manufacturing applications as well as deployed as an effective thermal mass for collecting and distributing excess heat on the building envelope, mitigating wastewater discharge while delivering thermal comfort, air decontamination and energy capture for factory end uses. The thesis aimed to develop an integrated water management system using moringa oleifera waste as a flocculant for treating dye effluent, with Global Mamas Fair Trade Zone (GMFTZ), a fairtrade apparel factory in Ghana, as a case study. Identifying the FTZ dyeing zone as the largest source of water contaminants, the material experiments compared the dosage and efficiency of locally sourced moringa oleifera pressmeal for treating Global Mamas’ effluent containing synthetic vat dyes with effluent from a typical H&M supplier in Bangladesh and Ganga Maki Factory in India, containing reactive and natural dyes respectively. The treated effluent quality was assessed based on reductions in pH, suspended solids and color and its reusability was determined through repeated iterations of dyeing and moringa treatment. Experiments showed that approximately 2.5kg of moringa pressmeal per kL of typical dye effluent is required to remove color, moderate pH and reduce suspended solids in the effluent within recommended limits by the Ghana Environment Protection Agency.
Finally, the thermal comfort benefits of circulating water across the dye workshop envelope were evaluated through mathematical analysis of potential interior temperature reduction that demonstrated a 12°C drop during the peak sun hour window in May. The experimental results coupled with lifecycle considerations demonstrated effectiveness of moringa flocculation to be greatest for natural dye-based effluent and least for Global Mamas effluent. A framework of the building-integrated moringa flocculation system was developed for Global Mamas, and various system configurations and layouts were assessed on the basis of their ease of use, thermal comfort potential, energy reduction and air quality benefits. The thesis provides an economical and bottom-up alternative for wastewater treatment and reuse in small apparel factories like Global Mamas, that can enable the transition to zero discharge and zero carbon apparel production while sustaining the local bioeconomy and protecting the ecosphere at the regional scale.M
Ultrathin , graphene oxide-based membranes for co2 capture from flue gas
No full textMay 2019School of EngineeringMembrane technology for CO2 capture from flue gas exhibits the superior separation performance and attractive levelized cost of energy to achieve the post combustion capture goal set by the US Department of Energy (DOE), 90% capture of CO2 with purity of >95% with less than 35% increase in energy cost. Novel membrane materials with potential to greatly improve membrane performance, such as 2-dimensional graphene and graphene oxide (GO) and its derivatives, have attracted great attention as a new membrane building block, primarily owing to their potential to make the thinnest possible membranes and thus provide the highest permeance for separation. My research work is focused on ultrathin GO-based membranes for CO2 capture from flue gas. Previous studies have shown that free-standing GO laminates in flat-sheet has potential for selective transport of gas molecules. However, the relatively poor separation performance and lack of scalable membrane fabrication methods limit the development of practical use of GO-based membranes. We developed facile and scalable methods for depositing ultrathin GO-based membranes on both hollow fiber and flat sheet substrates. We firstly synthesized GO by modified Hummers method and further modified or functionalized GO material by ultra-sonication or amine-treatment. To deposit GO-based membranes on polymeric hollow fiber substrate, specifically on the inner surface of hollow fiber, we developed a novel, two-step modified vacuum-assisted coating method. Uniform and high-quality GO membranes were successfully deposited on hollow fibers according to our characterizations by field emission scanning electron microscope (FESEM), X-ray photoelectron spectroscopy (XPS), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and X-ray diffraction (XRD). Gas separation was conducted with the lab-designed permeation system, and the results on base-GO hollow fiber membranes showed a moderate CO2 separation performance. We then functionalized our GO membranes by incorporating CO2-philic agent, amines (including primary and secondary amine molecules), and verified the successful functionalization by further characterizations. The amine-functionalized GO hollow fiber membranes offered a predominant facilitated CO2 transport mechanism as an addition to solution-diffusion mechanism, and therefore presented a highly-efficient CO2 capture performance under elevated temperature and wet feed condition. Zero-dimensional material, graphene oxide quantum dots, was firstly used as membrane building blocks by a smart strategy that to deposit a carbon framework skeleton with single-walled carbon nanotubes, and then to fill the carbon frame layer by nitrogen-doped graphene oxide quantum dots (N-GOQDs). Membrane was prepared in both hollow fiber and flat sheet substrates to demonstrate its potential on different separation purposes. Characterizations indicated the unique membrane structure and carbon-based chemical compositions, and the gas permeation and water treatment tests suggested the excellent performance for molecular separations. Consequently, the N-GOQD membranes showed superior CO2 capture performance from model flue gas, and exhibited high rejection for various dye molecules and divalent salt. We also developed a scalable printing method for depositing large-area (>100 cm2) GO-based membranes on polymeric flat-sheet support. Membrane quality was characterized and improved by modifying the membrane printer, the GO ink composition and the printing methods. Preliminary results on gas permeation with different single gases indicated a great potential of the printed GO membranes for separating smaller gas molecules. Further study by adding CO2-philic agent as a second printing ink demonstrated a good CO2 capture performance.Ph
Data-driven methods for probabilistic fault diagnosis in multirotor aircraft
No full textAugust 2022School of EngineeringThe next revolution in aviation is upon us with advances in novel configurations of electric vertical take-off and landing (eVTOL) aircraft. Advanced air mobility (AAM) will offer on-demand services for human and cargo transport and package delivery in the major cities of the world. Its other anticipated uses include surveillance for public safety, humanitarian aid, infrastructure supervision, remote sensing, etc. However, the operational success of mass transportation services by aerial vehicles will require absolute safety and reliability making efficient health and usage monitoring (HUMS) of these systems vital. According to a technical report by Uber Elevate, the safety level in air-taxi aviation needs to improve from 1.2 to 0.3 fatalities per 100 million passenger miles through full autonomy and innovation, with large amounts of data from real-world operations after the first generation VTOL aircraft are in production. Therefore, research and development are imperative to realize real-time system-level awareness and decision-making, in future intelligent and autonomous VTOL aircraft. This line of work focuses on fault detection and identification (FDI) of system faults in potential AAM vehicles utilizing in-flight data streams. Knowledge of system faults in real-time is critical for control reallocation or vehicle reconfiguration to complete the flight safely. Moreover, the incorporation of condition monitoring from the early phases of eVTOL operation will boost aircraft readiness, enhance flight safety, and lower maintenance, and operating costs. These will ensure the commercial success of the large fleet of frequently flying aerial vehicles. There has been extensive research going on to implement fault-tolerant control on multirotor aircraft, most of which relies on information about system faults in real-time to switch onto more power-efficient optimum control schemes or plan alternate trajectories with limited control authority awareness, depending upon the type and extent of faults. However, the analytical FDI approaches are mostly limited by the requirement of in-depth physical knowledge of the aircraft and lack of efficient handling of noise, and uncertainty, while the data-driven approaches suffer from a lack of explainability due to focusing on fitting the data and concentrate mostly on structural faults in blades and powertrain components of single rotor platforms. Therefore, the research gap related to probabilistic actuator FDI has been explored in this thesis.
In this study, the following challenges pertaining to the development of a probabilistic multicopter FDI technique have been addressed. First, it should be robust under operational variability, environmental disturbances, and uncertainty. Second, online fault monitoring should be made possible by improving the run-time of the decision-making scheme through low-dimensional representations of the dynamic information contained in the multi-modal sensory data. Third, these low-dimensional representations must be physically explainable, based on stochastic representations of the multicopter dynamics contained in data streams (aircraft states and controls time-series data).
Therefore, in the context of probabilistic FDI, a stochastic framework for FDI in multicopters is proposed, which attains the goal of being accurate, robust, and data-driven with improved physical interpretability. Its cornerstone lies in ``global'' stochastic time-series models which can appropriately represent the dynamics of the aircraft flight signals under multiple flight states, different fault types and magnitude, changing environmental disturbances, and uncertainty via functional pooling of data. At first, residual-based statistical time-series methods have been investigated with a novel application to multicopter rotor FDI. Some of these methods exhibit excellent accuracy but suffer from certain limitations that have been addressed through an innovative approach that integrates statistical time-series modeling and a machine learning algorithm. This method, titled the time-series assisted neural network has the following advantages over the former: (i) it requires only the healthy stochastic model to derive fault-sensitive (type and magnitude), and disturbance-rejecting features, (ii) it makes probabilistic decisions regarding the rotor faults in a single step using a simple neural network, (iii) it is applicable throughout the entire flight regime and has better accuracy with shorter signals enabling faster FDI.
In the second part of this thesis, flexible booms have been incorporated into the multicopter to generate simulated data. This opened new avenues for signal selection from remote and local sensors to achieve better uncertainty quantification in rotor fault magnitude estimation. It was achieved via inverse optimization techniques with the aforementioned ``global" stochastic models representing the functional dependence of signal dynamics with varying fault magnitude. Exploring local sensors mounted on the booms also led to the development of a probabilistic rotor fault diagnosis framework based on simple machine learning algorithms. It was developed using out-of-plane strain signals at individual boom roots and exhibited over 99\% rotor FDI accuracy under any admissible operating conditions and external disturbances without the need for dynamic representations or knowledge of the operating states. In the final task, the time-series assisted neural network performance was validated with experimental data from flight tests of a quadcopter and a hexacopter.Ph
Understanding neuronal vesicle transport : a study of the complex interplay of molecular motors and effects of anesthetics
No full textAugust 2021School of ScienceNeurons are a unique cell type with many distinct membrane domains. Two of these domains, the dendrites and the axon, perform distinct functions in neuronal signaling and require specific complements of membrane proteins. These proteins are delivered by vesicle transport that is mediated by molecular motors. Kinesins and dynein mediate long-range transport along microtubules, and dimeric myosins mediate short-range transport near the cellular membrane. Nearly all eukaryotic cells utilize vesicle transport for survival, but a neuron’s large size and polarity make it particularly reliant on intracellular transport. Correspondingly, deficits in vesicle transport in neurons can contribute to neurodegenerative conditions. This document contains two separate studies examining elements of myosins and kinesins in vesicle trafficking and protein accumulation. Myosin V motor proteins are dimeric and transport to the barbed end of actin filaments. To systematically characterize the vesicle populations bound by myosin V, I developed a novel labeling strategy to visualize myosin Va- and Vb-labeled vesicles in cultured hippocampal neurons. Both myosin Vs bound vesicles that were polarized to the somatodendritic domain where they underwent bidirectional long-range transport. Through a series of two-color imaging experiments, it became clear that myosin Vs specifically colocalized with two different dendrite-selective vesicle populations. Additionally, myosin V bound vesicles concurrently with two Kinesin-3 family members, KIF13A and KIF13B. These results show that coregulation of kinesin and myosin V on vesicles is likely to play an important role in neuronal vesicle transport. This new assay will be applicable in a broad range of cell types to determine the function of myosin V motor proteins.
My second project was directed to ask whether the most widely used general anesthetic propofol (2,6-diisopropylphenol) alters the transport properties of neuronal vesicles whose transport is mediated by Kinesin-1 family member KIF5C and Kinesin-3 family member KIF1A. Propofol is known to disrupt the processivity of three kinesins including Kinesin-1 in single molecule motility experiments by shortening the run length without disrupting the velocity. I assessed the effect of propofol on vesicles transported by KIF5C and KIF1A in cultured hippocampal neurons. The experiments showed that propofol reduced the run length and velocity of vesicles transported by KIF5C. This decrease in processivity resulted in a reduction in cargo accumulation at the axon terminal suggesting the potential for propofol to impact neuronal vesicle transport during anesthesia for surgery. Furthermore, vesicles moved by Kinesin-3 family member KIF1A, the primary transporter of presynaptic vesicles, also displayed reduced run length and velocity in response to propofol treatment. These results illustrate that propofol has physiologically relevant effects on at least two different neuronal vesicle populations. Future experiments will address whether propofol also alters fusion of synaptic vesicles at the axonal terminal.
In summary, I developed a novel labeling strategy to characterize myosin V-labeled vesicles and I studied the impact of propofol on Kinesin-1- and Kinesin-3-mediated vesicle transport. These results provide new insights into the complexities of the intracellular trafficking machinery that ensures that the vesicle proteins and membrane reach their specific destinations.Ph
Effects of past behavior
No full textDecember 2021School of Humanities, Arts, and Social SciencesHow does past behavior affect future choices? Several different answers exist in the decision-making and behavior change domains, but the focus of previous literature is on merely stating that past behavior can influence intentions, and that people may default to past behavior in the absence of a strong intention. It fails to explain the mechanism of the effects of past behavior, or how it can bridge decision-making and behavior change. One possible way to look at the effects of past behavior is to consider situations where past behavior is not repeated, and understand why and how novel behaviors were adopted. We conducted one such quasi-experiment amidst the COVID-19 pandemic. The COVID-19 pandemic had a significant impact on self-management behaviors used to manage chronic conditions such as diabetes, and diabetics were forced to adapt their behaviors in order to keep up with their self-management during a changing and uncertain environment. We conducted semi-structured interviews on diabetes self-management and COVID-19 was raised in the context of challenges to self-management as part of the wider interview. There were 21 participants with Type 2 diabetes that stated they played a major role in making their food choices. We used content analysis to identify behavior changes resulting from the COVID-19 pandemic. We saw that several different areas of diabetes self-management showed a behavior change as a result of the COVID-19 pandemic, and several participants showed adaptations to their self-management routines when there were challenges to past behaviors. Aside from the COVID-19 pandemic, the circumstances in which participants choose new behaviors over past behaviors can be explored in an experimental set-up designed to test different factors that could be used to influence the choice of what behavior to implement.M
Mesoscale simulation approach for the dynamics and assembly of deformable objects
No full textAugust 2022School of EngineeringThe dynamics and self-assembly of small, deformable objects are investigated in this study.These objects are represented as deformable spheres in contact, with the geometry of the
spheres representing the interactions of repulsive soft particles in nature, using a method
inspired by the Kelvin packing problem (minimizing contact area of equal-sized polyhedra).
A mesoscale approach known as ‘vertex models’ is used to track the geometries of spheres
and polyhedra, where the number and positions of the vertices indicate the geometry of
the object. Monte-Carlo steps are used to ’move’ the deformable objects. The behavioral
difference between droplets in dilute and concentrated suspensions emphasizes the context in
which this model is used. Surfactant micelles and emulsion droplets, in particular, frequently
take spherical shapes in dilute suspensions. However, at high enough concentrations, contact
between micelles or droplets results in non-spherical shapes. The dynamics and assembly
of the suspension are more dependent on the interfaces between objects than on the bulk
objects themselves at this limit. Self-assembled particle domains, such as block copolymers,
and electron clouds of atoms are other examples of deformable objects. In this project,
the term ”balloon model” refers to the novel application of vertex models to the dynamics
and assembly of soft, deformable objects. The balloon model is based on the hypothesis
that the periodic, aperiodic, and disordered structures observed in a material are primarily
determined by the surface area of the material’s deformable particles. This contradicts the
current widely held belief that these structures have more to do with the particle volume
fraction. As a result, this research project has two objectives in order to investigate this
hypothesis. The first goal is to use the balloon model to investigate the equilibrium structures
of various simulated materials. The effects of thermal fluctuation and particle size variability
on the equilibrium structure will be quantified here.
The second goal of the research project is to demonstrate the dynamic evolution of the
structures or states over time, which includes investigating the roles of metastable states.
This structure evolution study includes the nucleation of ordered states from disordered
states as well as diffusionless transformations from one aperiodically or periodically ordered
state to another. Because the balloon model is based on the evolution of deformable object
surface areas, comparing surface energy and material transfer between particles to thermal energy is critical to achieving these project goals. These variables govern the presence
and movement of defects, as well as the dynamics of metastable states. The use of the
balloon model in this project demonstrated that multiple ordered states are possible in
3D from disordered or other ordered states. These ordered states’ metastability has been
quantified, and a ”diffusionless transformation” between them has been observed. These
transformations are well-known in metallic systems but have only recently been discovered
in soft material experiments. In this work, the method of studying soft materials is similar
to that of foams and biological tissues, in which the interfaces between deformable objects
(gas bubbles or cells) play an important role. As a result, the goal of this research is to create
a unifying framework for understanding micelles, emulsions, biological tissues, and possibly
small molecule metals and glasses.Ph