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- All Subjects: chemical engineering
- Genre: Doctoral Dissertation
- Creators: Forzani, Erica
Description
High temperature CO2 perm-selective membranes offer potential for uses in various processes for CO2 separation. Recently, efforts are reported on fabrication of dense ceramic-carbonate dual-phase membranes. The membranes provide selective permeation to CO2 and exhibit high permeation flux at high temperature. Research on transport mechanism demonstrates that gas transport for ceramic-carbonate dual-phase membrane is rate limited by ion transport in ceramic support. Reducing membrane thickness proves effective to improve permeation flux. This dissertation reports strategy to prepare thin ceramic-carbonate dual-phase membranes to increase CO2 permeance. The work also presents characteristics and gas permeation properties of the membranes. Thin ceramic-carbonate dual-phase membrane was constructed with an asymmetric porous support consisting of a thin small-pore ionic conducting ceramic top-layer and a large pore base support. The base support must be carbonate non-wettable to ensure formation of supported dense, thin membrane. Macroporous yttria-stabilized zirconia (YSZ) layer was prepared on large pore Bi1.5Y0.3Sm0.2O3-δ (BYS) base support using suspension coating method. Thin YSZ-carbonate dual-phase membrane (d-YSZ/BYS) was prepared via direct infiltrating Li/Na/K carbonate mixtures into top YSZ layers. The thin membrane of 10 μm thick offered a CO2 flux 5-10 times higher than the thick dual-phase membranes. Ce0.8Sm0.2O1.9 (SDC) exhibited highest CO2 flux and long-term stability and was chosen as ceramic support for membrane performance improvement. Porous SDC layers were co-pressed on base supports using SDC and BYS powder mixtures which provided better sintering comparability and carbonate non-wettability. Thin SDC-carbonate dual-phase membrane (d-SDC/SDC60BYS40) of 150 μm thick was synthesized on SDC60BYS40. CO2 permeation flux for d-SDC/SDC60BYS40 exhibited increasing dependence on temperature and partial pressure gradient. The flux was higher than other SDC-based dual-phase membranes. Reducing membrane thickness proves effective to increase CO2 permeation flux for the dual-phase membrane.
ContributorsLu, Bo (Author) / Lin, Yuesheng (Thesis advisor) / Crozier, Peter (Committee member) / Herrmann, Macus (Committee member) / Forzani, Erica (Committee member) / Lind, Mary Laura (Committee member) / Arizona State University (Publisher)
Created2014
Description
Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
ContributorsDong, Yuwen (Author) / Rivera, Daniel E (Thesis advisor) / Dai, Lenore (Committee member) / Forzani, Erica (Committee member) / Rege, Kaushal (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Volatile Organic Compounds (VOCs) are central to atmospheric chemistry and have significant impacts on the environment. The reaction of oxygenated VOCs with OH radicals was first studied to understand the fate of oxygenated VOCs. The rate constants of the gas-phase reaction of OH radicals with trans-2-hexenal, trans-2-octenal, and trans-2 nonenal were determined using the relative rate technique. Then the interactions between VOCs and ionic liquid surfaces were studied. The goal was to find a material to selectively detect alcohol compounds. Computational chemistry calculations were performed to investigate the interactions of ionic liquids with different classes of VOCs. The thermodynamic data suggest that 1-butyl-3-methylimindazolium chloride (C4mimCl) preferentially interacts with alcohols as compared to other classes of VOCs. Fourier transform infrared spectroscopy was used to probe the ionic liquid surface before and after exposure to the VOCs that were tested. New spectral features were detected after exposure of C4mimCl to various alcohols and a VOC mixture with an alcohol in it. The new features are characteristic of the alcohols tested. No new IR features were detected after exposure of the C4mimCl to the aldehyde, ketone, alkane, alkene, alkyne or aromatic compounds. The experimental results demonstrated that C4mimCl is selective to alcohols, even in complex mixtures. The kinetic study of the association and dissociation of alcohols with C4minCl surfaces was performed. The findings in this work provide information for future gas-phase alcohol sensor design. CO2 is a major contributor to global warming. An ionic liquid functionalized reduced graphite oxide (IL-RGO)/ TiO2 nanocomposite was synthesized and used to reduce CO2 to a hydrocarbon in the presence of H2O vapor. The SEM image revealed that IL-RGO/TiO2 contained separated reduced graphite oxide flakes with TiO2 nanoparticles. Diffuse Reflectance Infrared Fourier Transform Spectroscopy was used to study the conversion of CO2 and H2O vapor over the IL-RGO/TiO2 catalyst. Under UV-Vis irradiation, CH4 was found to form after just 40 seconds of irradiation. The concentration of CH4 continuously increased under longer irradiation time. This research is particularly important since it seems to suggest the direct, selective formation of CH4 as opposed to CO.
ContributorsGao, Tingting (Author) / Andino, Jean M (Thesis advisor) / Forzani, Erica (Committee member) / Kavazanjian, Edward (Committee member) / Arizona State University (Publisher)
Created2012
Description
Environmentally responsive microgels have drawn significant attention due to their intrinsic ability to change volume in response to various external stimuli such as pH, temperature, osmotic pressure, or electric and magnetic fields. The extent of particle swelling is controlled by the nature of the polymer-solvent interaction. This thesis focuses on design and synthesis of environmentally responsive microgels and their composites, and encompasses methods of utilizing microgel systems in applications as vehicles for the adsorption, retention, and targeted delivery of chemical species. Furthermore, self-assembled microgel particles at ionic liquid (IL)-water interfaces demonstrate responsive colloidal lattice morphology. The thesis first reports on the fundamental aspects of synthesis, functionalization, and characteristic properties of multifunctional environmentally responsive microgels derived from poly(N-isopropylacrylamide) (PNIPAm) and other functional co-monomers. In particular, the uptake and release of active chemical species such as rheology modifiers into and from these ionic microgels is demonstrated. Moreover, a facile tunable method for the formation of organic-inorganic composites with Fe3O4 nanoparticles adsorbed and embedded within ionic microgel particles is explored. Additionally, the development of zwitterionic microgels (ZI-MG) is presented. These aqueous ZI-MG dispersions exhibit reversible parabolic swelling as a function of pH and display a minimum hydrodynamic diameter at a tunable isoelectric point (IEP). This study also elucidates the controlled uptake and release of surfactants from these particle systems. The extent of surfactant loading and the ensuing relative swelling/deswelling behaviors within the polymer networks are explained in terms of their binding interactions. The latter part of this thesis highlights the versatility of fluorescently labeled microgel particles as stabilizers for IL-water droplets. When the prepared particles form monolayers and equilibrate at the liquid-liquid interface, the colloidal lattice organization may re-order itself depending on the surface charge of these particles. Finally, it is shown that the spontaneously formed and densely packed layers of microgel particles can be employed for extraction applications, as the interface remains permeable to small active species.
ContributorsChen, Haobo (Author) / Dai, Lenore L (Committee member) / Chen, Kangping (Committee member) / Forzani, Erica (Committee member) / Lind, Mary Laura (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Connected health is an emerging field of science and medicine that enables the collection and integration of personal biometrics and environment, contributing to more precise and accurate assessment of the person’s state. It has been proven to help to establish wellbeing as well as prevent, diagnose, and determine the prognosis of chronic diseases. The development of sensing devices for connected health is challenging because devices used in the field of medicine need to meet not only selectivity and sensitivity of detection, but also robustness and performance under hash usage conditions, typically by non-experts in analysis. In this work, the properties and fabrication process of sensors built for sensing devices capable of detection of a biomarker as well as pollutant levels in the environment are discussed. These sensing devices have been developed and perfected with the aim of overcoming the aforementioned challenges and contributing to the evolving connected health field. In the first part of this work, a wireless, solid-state, portable, and continuous ammonia (NH3) gas sensing device is introduced. This device determines the concentration of NH3 contained in a biological sample within five seconds and can wirelessly transmit data to other Bluetooth enabled devices. In this second part of the work, the use of a thermal-based flow meter to assess exhalation rate is evaluated. For this purpose, a mobile device named here mobile indirect calorimeter (MIC) was designed and used to measure resting metabolic rate (RMR) from subjects, which relies on the measure of O2 consumption rate (VO2) and CO2 generation rate (VCO2), and compared to a practical reference method in hospital. In the third part of the work, the sensing selectivity, stability and sensitivity of an aged molecularly imprinted polymer (MIP) selective to the adsorption of hydrocarbons were studied. The optimized material was integrated in tuning fork sensors to detect environmental hydrocarbons, and demonstrated the needed stability for field testing. Finally, the hydrocarbon sensing device was used in conjunction with a MIC to explore potential connections between hydrocarbon exposure level and resting metabolic rate of individuals. Both the hydrocarbon sensing device and the metabolic rate device were under field testing. The correlation between the hydrocarbons and the resting metabolic rate were investigated.
ContributorsLiu, Naiyuan (Author) / Forzani, Erica (Thesis advisor) / Raupp, Gregory (Committee member) / Holloway, Julianne (Committee member) / Thomas, Marylaura (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2018
Description
Among the alternative processes for the traditional distillation, adsorption and membrane separations are the two most promising candidates and metal-organic frameworks (MOFs) are the new material candidate as adsorbent or membrane due to their high surface area, various pore sizes, and highly tunable framework functionality. This dissertation presents an investigation of the formation process of MOF membrane, framework defects, and two-dimensional (2D) MOFs, aiming to explore the answers for three critical questions: (1) how to obtain a continuous MOF membrane, (2) how defects form in MOF framework, and (3) how to obtain isolated 2D MOFs. To solve the first problem, the accumulated protons in the MOF synthesis solution is proposed to be the key factor preventing the continuous growth among Universitetet I Oslo-(UiO)-66 crystals. The hypothesis is verified by the growth reactivation under the addition of deprotonating agent. As long as the protons were sufficiently coordinated by the deprotonating agent, the continuous growth of UiO-66 is guaranteed. Moreover, the modulation effect can impact the coordination equilibrium so that an oriented growth of UiO-66 film was achieved in membrane structures. To find the answer for the second problem, the defect formation mechanism in UiO-66 was investigated and the formation of missing-cluster (MC) defects is attributed to the partially-deprotonated ligands. Experimental results show the number of MC defects is sensitive to the addition of deprotonating agent, synthesis temperature, and reactant concentration. Pore size distribution allows an accurate and convenient characterization of the defects. Results show that these defects can cause significant deviations of its pore size distribution from the perfect crystal. The study of the third questions is based on the established bi-phase synthesis method, a facile synthesis method is adopted for the production of high quality 2D MOFs in large scale. Here, pyridine is used as capping reagent to prevent the interplanar hydrogen bond formation. Meanwhile, formic acid and triethylamine as modulator and deprotonating agent to balance the anisotropic growth, crystallinity, and yield in the 2D MOF synthesis. As a result, high quality 2D zinc-terephthalic acid (ZnBDC) and copper-terephthalic acid (CuBDC) with extraordinary aspect ratio samples were successfully synthesized.
ContributorsShan, Bohan (Author) / Mu, Bin (Thesis advisor) / Forzani, Erica (Committee member) / Dai, Lenore (Committee member) / Lin, Jerry (Committee member) / Liu, Jingyue (Committee member) / Arizona State University (Publisher)
Created2019
Description
Excessive weight gain during pregnancy is a significant public health concern and has been the recent focus of novel, control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually tailored and intensively adaptive intervention to manage weight gain for overweight or obese pregnant women using control engineering approaches. Motivated by the needs of the HMZ, this dissertation presents how to use system identification and state estimation techniques to assist in dynamical systems modeling and further enhance the performance of the closed-loop control system for interventions.
Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.
In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.
Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.
In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.
ContributorsGuo, Penghong (Author) / Rivera, Daniel E. (Thesis advisor) / Peet, Matthew M. (Committee member) / Forzani, Erica (Committee member) / Deng, Shuguang (Committee member) / Pavlic, Theodore P. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Vapor intrusion (VI) pathway assessment often involves the collection and analysis of groundwater, soil gas, and indoor air data. There is temporal variability in these data, but little is understood about the characteristics of that variability and how it influences pathway assessment decision-making. This research included the first-ever collection of a long-term high-frequency indoor air data set at a house with VI impacts overlying a dilute chlorinated solvent groundwater plume. It also included periodic synoptic snapshots of groundwater and soil gas data and high-frequency monitoring of building conditions and environmental factors. Indoor air trichloroethylene (TCE) concentrations varied over three orders-of-magnitude under natural conditions, with the highest daily VI activity during fall, winter, and spring months. These data were used to simulate outcomes from common sampling strategies, with the result being that there was a high probability (up to 100%) of false-negative decisions and poor characterization of long-term exposure. Temporal and spatial variability in subsurface data were shown to increase as the sampling point moves from source depth to ground surface, with variability of an order-of-magnitude or more for sub-slab soil gas. It was observed that indoor vapor sources can cause subsurface vapor clouds and that it can take days to weeks for soil gas plumes created by indoor sources to dissipate following indoor source removal. A long-term controlled pressure method (CPM) test was conducted to assess its utility as an alternate approach for VI pathway assessment. Indoor air concentrations were similar to maximum concentrations under natural conditions (9.3 μg/m3 average vs. 13 μg/m3 for 24 h TCE data) with little temporal variability. A key outcome was that there were no occurrences of false-negative results. Results suggest that CPM tests can produce worst-case exposure conditions at any time of the year. The results of these studies highlight the limitations of current VI pathway assessment approaches and demonstrate the need for robust alternate diagnostic tools, such as CPM, that lead to greater confidence in data interpretation and decision-making.
ContributorsHolton, Chase Weston (Author) / Johnson, Paul C (Thesis advisor) / Fraser, Matthew (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2015
Description
Energy Expenditure (EE), a key diagnostic measurement for treatment of obesity, is measured via indirect calorimetry method through breath biomarkers of CO2 production and/or O2 consumption rates (VCO2 and/or VO2, respectively). Current technologies are limited due to prevailing designs requiring wearable facial accessories that present accuracy, precision, and usability concerns with regards to free living measurement. A novel medical device and smart home system, named Smart Pad, has been developed, with the capability of energy expenditure assessment via VCO2 measured from a room’s CO2 concentration. The system has 3 distinct capabilities: contactless EE measurement, air quality optimization via actuation of room ventilation, and efficiency optimization via ventilation actuation of only human-occupied environments. The Smart Pad shows accuracy of 90% for 14-19 minutes of resting measurement and accuracy of 90% for 4.8-7.0 minutes of exercise measurement after calibrating for air exchange rate (λ [hour-1]) using a reference method. Without reference instrument calibration, the Smart Pad system shows average accuracy of nearly 100% with correlations of Y=1.02X, R=0.761 for high resolution measurements and Y=1.06X, R=0.937 for averaged measurements over 50-60 minutes. In addition, the Smart Pad validation for contactless EE measurement has been performed in different environments, including a vehicle, medical office, a private office, and an ambulatory enclosure with rooms, ranging in volume from 3.1 m3 to 18.8m3. It was concluded that contactless EE measurements can be accurately performed in all tested scenarios with both low and high air exchange environments with λ ranging from 1.5 Hours-1 to 10.0 Hours -1. The system represents a new way to assess EE of individuals under free-living conditions in an unobstructive, passive, and accurate manner, and it is comparable or better in single breath gas measurement accuracy (with comparisons sourced from FDA data) than other medical devices (e.g. Vyntus CPXTM, MasterScreen CPXTM, Oxycon ProTM, and MedGemTM) which were 510(k) cleared by the FDA for prescription use in metabolic/cardiopulmonary diagnostics.
ContributorsSprowls, Mark (Author) / Forzani, Erica (Thesis advisor) / Destaillats, Hugo (Committee member) / Kulick, Doina (Committee member) / Nikkhah, Mehdi (Committee member) / Raupp, Gregory (Committee member) / Arizona State University (Publisher)
Created2021
Description
Freshwater as the resource for the survival of humans and all lives on earth is very precious but scarce. The shortage of the original freshwater resources and the interfering activities by human and other natural factors form this issue together. To reduce the water supply pressure and deterioration of freshwater systems (for example, river, wetland, and groundwater), the quantity-increase and the quality-increase strategies should be implemented at the same time. Therefore, corresponding membrane technologies have been developed to achieve water purification with high efficiency and low cost. For desalinating seawater and other types of saline water, pervaporation has been proved that has the potential to complete desalination with salt rejection rate over 99 % when dealing with high salinity water that reverse osmosis (RO) cannot handle. In this dissertation, except the discussion of commonly used materials to synthesize pervaporation membranes, two types of novel pervaporation desalination membranes (nanophotonic-enhanced membrane and free-standing sulfonated membrane) have been presented and discussed. The novel membranes were tested to see the potential of pervaporation to desalinate seawater and saline water with more complex ionic composition, and the possibility of achieving zero liquid discharge in the desalination field when having pervaporation as the assistance. For mitigating polluted water that is caused by human activities, especially agricultural activities, electrodialysis is an effective method to remove specific ions from water, and it does not require extra chemical cost or regeneration. A type of anion exchange membranes inspired by ion exchange resins was synthesized and tested, and the performance on nitrate removal has been evaluated in this dissertation.
ContributorsLi, Yusi (Author) / Lind, Mary Laura (Thesis advisor) / Perreault, Francois (Thesis advisor) / Forzani, Erica (Committee member) / Seo, S. Eileen (Committee member) / Walker, W. Shane (Committee member) / Arizona State University (Publisher)
Created2023