Coupled diusion and mechanics in battery electrodes

Thesis (Ph.D.)--University of Washington, 2016-08

Moisture Desorption Studies on Polymer Hydrated and Vacuum Extruded Bentonite Clay Mat

Moisture desorption observations from two bentonite clay mats subjected to ten environmental zones with individually different combinations of laboratory-controlled constant temperatures (between 20 °C and 40 °C) and relative humidity (between 15% and 70%) are presented. These laboratory observations are compared with predictions from mathematical models, such as thin-layer drying equations and kinetic drying models proposed by Page, Wang and Singh, and Henderson and Pabis. The quality of fit of these models is assessed using standard error (SE) of estimate, relative percent of error, and coefficient of correlation. The Page model was found to better predict the drying kinetics of the bentonite clay mats for the simulated tropical climates. Critical study on the drying constant and moisture diffusion coefficient helps to assess the efficacy of a polymer to retain moisture and control desorption through water molecule bonding. This is further substantiated with the Guggenheim–Anderson–De Boer (GAB) desorption isotherm model which is presented.

Ecological and evolutionary effects of dispersal on freshwater zooplankton

A recent focus on contemporary evolution and the connections between communities has sought to more closely integrate the fields of ecology and evolutionary biology. Studies of coevolutionary dynamics, life history evolution, and rapid local adaptation demonstrate that ecological circumstances can dictate evolutionary trajectories. Thus, variation in species identity, trait distributions, and genetic composition may be maintained among ecologically divergent habitats. New theories and hypotheses (e.g., metacommunity theory and the Monopolization hypothesis) have been developed to understand better the processes occurring in spatially structured environments and how the movement of individuals among habitats contributes to ecology and evolution at broader scales. As few empirical studies of these theories exist, this work seeks to further test these concepts. Spatial and temporal dispersal are the mechanisms that connect habitats to one another. Both processes allow organisms to leave conditions that are suboptimal or unfavorable, and enable colonization and invasion, species range expansion, and gene flow among populations. Freshwater zooplankton are aquatic crustaceans that typically develop resting stages as part of their life cycle. Their dormant propagules allow organisms to disperse both temporally and among habitats. Additionally, because a number of species are cyclically parthenogenetic, they make excellent model organisms for studying evolutionary questions in a controlled environment. Here, I use freshwater zooplankton communities as model systems to explore the mechanisms and consequences of dispersal and to test these nascent theories on the influence of spatial structure in natural systems. In Chapter one, I use field experiments and mathematical models to determine the range of adult zooplankton dispersal over land and what vectors are moving zooplankton. Chapter two focuses on prolonged dormancy of one aquatic zooplankter, Daphnia pulex. Using statistical models with field and mesocosm experiments, I show that variation in Daphnia dormant egg hatching is substantial among populations in nature, and some of that variation can be attributed to genetic differences among the populations. Chapters three and four explore the consequences of dispersal at multiple levels of biological organization. Chapter three seeks to understand the population level consequences of dispersal over evolutionary time on current patterns of population genetic differentiation. Nearby populations of D. pulex often exhibit high population genetic differentiation characteristic of very low dispersal. I explore two alternative hypotheses that seek to explain this pattern. Finally, chapter four is a case study of how dispersal has influenced patterns of variation at the community, trait and genetic levels of biodiversity in a lake metacommunity.

Quantitative characterization of physico-chemical properties of the active layers of reverse osmosis and nanofiltration membranes, and their relation to membrane performance

The main objectives of this dissertation were: (i) to develop experimental and analytical procedures to quantify different physico-chemical properties of the ultra-thin (~ 100 nm) active layers of reverse osmosis (RO) and nanofiltration (NF) membranes and their interactions with contaminants; (ii) to use such procedures to evaluate the similarities and differences between the active layers of different RO/NF membranes; and (iii) to relate characterization results to membrane performance. Such objectives were motivated by the current limited understanding of the physico-chemical properties of active layers as a result of traditional characterization techniques having limitations associated with the nanometer-scale spatial resolution required to study these ultra-thin films. Functional groups were chosen as the main active layer property of interest. Specific accomplishments of this study include the development of procedures to quantify in active layers as a function of pH: (1) the concentration of both negatively and positively ionized functional groups; (2) the stoichiometry of association between ions (i.e., barium) and ionized functional groups (i.e., carboxylate and sulfonate); and (3) the steric effects experienced by ions (i.e., barium). Conceptual and mathematical models were developed to describe experimental results. The depth heterogeneity of the active layer physico-chemical properties and interactions with contaminants studied in this dissertation was also characterized. Additionally, measured concentrations of ionized functional groups in the polyamide active layers of several commercial RO/NF membranes were used as input in a simplified RO/NF transport model to predict the rejection of a strong electrolyte (i.e., potassium iodide) and a weak acid (i.e., arsenious acid) at different pH values based on rejection results at one pH condition. The good agreement between predicted and experimental results showed that the characterization procedures developed in this study serve as useful tools in the advancement of the understanding of the properties and structure of the active layers of RO/NF membranes, and the mechanisms of contaminant transport through them.

SOCIAL AND ECOLOGICAL FACTORS INFLUENCING COLLECTIVE BEHAVIOR IN GIANT DANIO

A fundamental problem in biology is understanding how and why things group together. Collective behavior is observed on all organismic levels - from cells and slime molds, to swarms of insects, flocks of birds, and schooling fish, and in mammals, including humans. The long-term goal of this research is to understand the functions and mechanisms underlying collective behavior in groups. This dissertation focuses on shoaling (aggregating) fish. Shoaling behaviors in fish confer foraging and anti-predator benefits through social cues from other individuals in the group. However, it is not fully understood what information individuals receive from one another or how this information is propagated throughout a group. It is also not fully understood how the environmental conditions and perturbations affect group behaviors. The specific research objective of this dissertation is to gain a better understanding of how certain social and environmental factors affect group behaviors in fish. I focus on two ecologically relevant decision-making behaviors: (i) rheotaxis, or orientation with respect to a flow, and (ii) startle response, a rapid response to a perceived threat. By integrating behavioral and engineering paradigms, I detail specifics of behavior in giant danio Devario aequipinnatus (McClelland 1893), and numerically analyze mathematical models that may be extended to group behavior for fish in general, and potentially other groups of animals as well. These models that predict behavior data, as well as generate additional, testable hypotheses. One of the primary goals of neuroethology is to study an organism's behavior in the context of evolution and ecology. Here, I focus on studying ecologically relevant behaviors in giant danio in order to better understand collective behavior in fish. The experiments in this dissertation provide contributions to fish ecology, collective behavior, and biologically-inspired robotics.

Chiral polymerisation and the RNA world

The purpose of this paper is to review two mathematical models: one for the formation of homochiral polymers from an originally chirally symmetric system; and the other, to show how, in an RNA-world scenario, RNA can simultaneously act both as information storage and a catalyst for its own production. We note the similarities and differences in chemical mechanisms present in the systems. We review these two systems, analysing steady-states, interesting kinetics and the stability of symmetric solutions. In both systems we show that there are ranges of parameter values where some chains increase their own concentrations faster than others.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.

Combined exposure to multiple mycotoxins in food: are we ready for risk assessment and risk management?

It is nowadays recognized that the risk of human co-exposure to multiple mycotoxins is real. In the last years, a number of studies have approached the issue of co-exposure and the best way to develop a more precise and realistic assessment. Likewise, the growing concern about the combined effects of mycotoxins and their potential impact on human health has been reflected by the increasing number of toxicological studies on the combined toxicity of these compounds. Nevertheless, risk assessment of these toxins, still follows the conventional paradigm of single exposure and single effects, incorporating only the possibility of additivity but not taking into account the complex dynamics associated to interactions between different mycotoxins or between mycotoxins and other food contaminants. Considering that risk assessment is intimately related to the establishment of regulatory guidelines, once the risk assessment is completed, an effort to reduce or manage the risk should be followed to protect public health. Risk assessment of combined human exposure to multiple mycotoxins thus poses several challenges to scientists, risk assessors and risk managers and opens new avenues for research. This presentation aims to give an overview of the different challenges posed by the likelihood of human co-exposure to mycotoxins and the possibility of interactive effects occurring after absorption, towards knowledge generation to support a more accurate human risk assessment and risk management. For this purpose, a physiologically-based framework that includes knowledge on the bioaccessibility, toxicokinetics and toxicodynamics of multiple toxins is proposed. Regarding exposure assessment, the need of harmonized food consumption data, availability of multianalyte methods for mycotoxin quantification, management of left-censored data and use of probabilistic models will be highlight, in order to develop a more precise and realistic exposure assessment. On the other hand, the application of predictive mathematical models to estimate mycotoxins’ combined effects from in vitro toxicity studies will be also discussed. Results from a recent Portuguese project aimed at exploring the toxic effects of mixtures of mycotoxins in infant foods and their potential health impact will be presented as a case study, illustrating the different aspects of risk assessment highlighted in this presentation. Further studies on hazard and exposure assessment of multiple mycotoxins, using harmonized approaches and methodologies, will be crucial towards an improvement in data quality and contributing to holistic risk assessment and risk management strategies for multiple mycotoxins in foodstuffs.

Students’ Self-Report of Motivational Orientation and Teacher Evaluation on Coping and Motivational Orientation Related to Elementary Students’ Mathematical Problem Solving and Reading Comprehension

The present study examined the correlations between motivational orientation and students’ academic performance in mathematical problem solving and reading comprehension. The main purpose is to see if students’ intrinsic motivation is related to their actual performance in different subject areas, math and reading. In addition, two different informants, students and teachers, were adopted to check whether the correlation is different by different informants. Pearson’s correlational analysis was a major method, coupled with regression analysis. The result confirmed the significant positive correlation between students’ academic performance and students’ self-report and teacher evaluation on their motivational orientation respectively. Teacher evaluation turned out with more predictive value for the academic achievement in math and reading. Between the subjects, mathematical problem solving showed higher correlation with most of the motivational subscales than reading comprehension did. The highest correlation was found between teacher evaluation on task orientation and students’ mathematical problem solving. The positive relationship between intrinsic motivation and academic achievement was proved. The disparity between students ’ self-report and teacher evaluation on motivational orientation was also addressed with the need of further examination.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Ultrasound and microwave assisted extraction of ergosterol from Agaricus bisporus L.: optimization through response surface methodology

There is scientific evidence demonstrating the benefits of mushrooms ingestion due to their richness in bioactive compounds such as mycosterols, in particular ergosterol [I]. Agaricus bisporus L. is the most consumed mushroom worldwide presenting 90% of ergosterol in its sterol fraction [2]. Thus, it is an interesting matrix to obtain ergosterol, a molecule with a high commercial value. According to literature, ergosterol concentration can vary between 3 to 9 mg per g of dried mushroom. Nowadays, traditional methods such as maceration and Soxhlet extraction are being replaced by emerging methodologies such as ultrasound (UAE) and microwave assisted extraction (MAE) in order to decrease the used solvent amount, extraction time and, of course, increasing the extraction yield [2]. In the present work, A. bisporus was extracted varying several parameters relevant to UAE and MAE: UAE: solvent type (hexane and ethanol), ultrasound amplitude (50 - 100 %) and sonication time (5 min-15 min); MAE: solvent was fixed as ethanol, time (0-20 min), temperature (60-210 •c) and solid-liquid ratio (1-20 g!L). Moreover, in order to decrease the process complexity, the pertinence to apply a saponification step was evaluated. Response surface methodology was applied to generate mathematical models which allow maximizing and optimizing the response variables that influence the extraction of ergosterol. Concerning the UAE, ethanol proved to be the best solvent to achieve higher levels of ergosterol (671.5 ± 0.5 mg/100 g dw, at 75% amplitude for 15 min), once hexane was only able to extract 152.2 ± 0.2 mg/100 g dw, in the same conditions. Nevertheless, the hexane extract showed higher purity (11%) when compared with the ethanol counterpart ( 4% ). Furthermore, in the case of the ethanolic extract, the saponification step increased its purity to 21%, while for the hexane extract the purity was similar; in fact, hexane presents higher selectivity for the lipophilic compounds comparatively with ethanol. Regarding the MAE technique, the results showed that the optimal conditions (19 ± 3 min, 133 ± 12 •c and 1.6 ± 0.5 g!L) allowed higher ergosterol extraction levels (556 ± 26 mg/100 g dw). The values obtained with MAE are close to the ones obtained with conventional Soxhlet extraction (676 ± 3 mg/100 g dw) and UAE. Overall, UAE and MAE proved to he efficient technologies to maximize ergosterol extraction yields.

Coarse-graining and renormalisation group methods for the elucidation of the kinetics of complex nucleation and growth processes

We review our work on generalisations of the Becker-Doring model of cluster-formation as applied to nucleation theory, polymer growth kinetics, and the formation of upramolecular structures in colloidal chemistry. One valuable tool in analysing mathematical models of these systems has been the coarse-graining approximation which enables macroscopic models for observable quantities to be derived from microscopic ones. This permits assumptions about the detailed molecular mechanisms to be tested, and their influence on the large-scale kinetics of surfactant self-assembly to be elucidated. We also summarise our more recent results on Becker-Doring systems, notably demonstrating that cross-inhibition and autocatalysis can destabilise a uniform solution and lead to a competitive environment in which some species flourish at the expense of others, phenomena relevant in models of the origins of life.

Chiral polymerisation and the RNA world

The purpose of this paper is to review two mathematical models: one for the formation of homochiral polymers from an originally chirally symmetric system; and the other, to show how, in an RNA-world scenario, RNA can simultaneously act both as information storage and a catalyst for its own production. We note the similarities and differences in chemical mechanisms present in the systems. We review these two systems, analysing steady-states, interesting kinetics and the stability of symmetric solutions. In both systems we show that there are ranges of parameter values where some chains increase their own concentrations faster than others.

Upper Limb Posture Estimation in Robotic and Virtual Reality-based Rehabilitation.

New motor rehabilitation therapies include virtual reality (VR) and robotic technologies. In limb rehabilitation, limb posture is required to (1) provide a limb realistic representation in VR games and (2) assess the patient improvement. When exoskeleton devices are used in the therapy, the measurements of their joint angles cannot be directly used to represent the posture of the patient limb, since the human and exoskeleton kinematic models differ. In response to this shortcoming, we propose a method to estimate the posture of the human limb attached to the exoskeleton. We use the exoskeleton joint angles measurements and the constraints of the exoskeleton on the limb to estimate the human limb joints angles. This paper presents (a) the mathematical formulation and solution to the problem, (b) the implementation of the proposed solution on a commercial exoskeleton system for the upper limb rehabilitation, (c) its integration into a rehabilitation VR game platform, and (d) the quantitative assessment of the method during elbow and wrist analytic training. Results show that this method properly estimates the limb posture to (i) animate avatars that represent the patient in VR games and (ii) obtain kinematic data for the patient assessment during elbow and wrist analytic rehabilitation.

Multiscale Modeling and Simulation of Stepped Crystal Surfaces

A primary goal of this dissertation is to understand the links between mathematical models that describe crystal surfaces at three fundamental length scales: The scale of individual atoms, the scale of collections of atoms forming crystal defects, and macroscopic scale. Characterizing connections between different classes of models is a critical task for gaining insight into the physics they describe, a long-standing objective in applied analysis, and also highly relevant in engineering applications. The key concept I use in each problem addressed in this thesis is coarse graining, which is a strategy for connecting fine representations or models with coarser representations. Often this idea is invoked to reduce a large discrete system to an appropriate continuum description, e.g. individual particles are represented by a continuous density. While there is no general theory of coarse graining, one closely related mathematical approach is asymptotic analysis, i.e. the description of limiting behavior as some parameter becomes very large or very small. In the case of crystalline solids, it is natural to consider cases where the number of particles is large or where the lattice spacing is small. Limits such as these often make explicit the nature of links between models capturing different scales, and, once established, provide a means of improving our understanding, or the models themselves. Finding appropriate variables whose limits illustrate the important connections between models is no easy task, however. This is one area where computer simulation is extremely helpful, as it allows us to see the results of complex dynamics and gather clues regarding the roles of different physical quantities. On the other hand, connections between models enable the development of novel multiscale computational schemes, so understanding can assist computation and vice versa. Some of these ideas are demonstrated in this thesis. The important outcomes of this thesis include: (1) a systematic derivation of the step-flow model of Burton, Cabrera, and Frank, with corrections, from an atomistic solid-on-solid-type models in 1+1 dimensions; (2) the inclusion of an atomistically motivated transport mechanism in an island dynamics model allowing for a more detailed account of mound evolution; and (3) the development of a hybrid discrete-continuum scheme for simulating the relaxation of a faceted crystal mound. Central to all of these modeling and simulation efforts is the presence of steps composed of individual layers of atoms on vicinal crystal surfaces. Consequently, a recurring theme in this research is the observation that mesoscale defects play a crucial role in crystal morphological evolution.