Chemical Engineering

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This is the collection for the University of Waterloo's Department of Chemical Engineering.

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The Effect of Liquid Crystal Inclusions on the Mechanical Properties of Liquid Crystal Elastomers
(University of Waterloo, 2024-09-06) Vasanji, Sahad
The synergy between materials with differing mechanical properties is an evolutionary adaptation for survival that pervades all of biology. Recognizing these masterstrokes of the natural world has inspired composite materials that enhance all aspects of quality of life. Composite design is particularly important for soft robots, which have advantages over their rigid-bodied counterparts for precision medicine, aquatic locomotion, and human interaction broadly. The relatively inferior mechanical properties of contemporary soft robots are not yet sufficient to replace hard-bodied robots and must be enriched for high load-bearing situations. Liquid Crystal Elastomers (LCEs) hold much promise as a candidate material for soft robotic bodies due to their rapid and reversible stimuli-responsive shape change. Solid fillers, interpenetrating polymer networks, and microstructural modulation have been employed to stiffen and toughen LCEs, yet these strategies substantially hinder extensibility or the liquid crystalline (LC) order. Liquid metal inclusions have recently been harnessed to profoundly increase the elastic modulus and toughness, though the isotropic droplets still compromise LC order. Ever elusive is a method for amplifying mechanical properties that elevate LC order without substantially compromising extensibility. In this thesis, a stiffened and toughened LCE composite is developed by doping with low molecular weight liquid crystal solvents. First, the influence of the nematogen 4-cyano-4'-pentylbiphenyl, 5CB, is studied. Through miscibility, thermal, and crystallographic studies, the enhanced mechanical properties are shown to emanate from strain-induced short-range smectic order (i.e., cybotacticity) and nanoscale phase separation of the LC solvent from the matrix. Uniquely, cybotacticity arises from components possessing no individual smectic ordering. Improvements of 570% and 370% in stiffness and toughness are conferred and extensibility only decreases by 20%. The first study is built upon by examining LCE modification with the smectogen 8CB (4-cyano-4′-octylbiphenyl). Markedly larger improvements are displayed in the stiffness (760%) and toughness (415%) while retaining 90% of the neat LCE’s elasticity. Strain-induced charge transfer is discovered as another factor responsible for the improved mechanical properties. Designing a stiffer, tougher, and lighter LCE with anisotropic liquids will facilitate the development of more effective soft actuators and attract more interest to the theory and application of liquid inclusion stiffening.
Photopolymerization based 3D printing of thermoresponsive hydrogel precursors
(University of Waterloo, 2024-08-30) Bauman, Lukas
Thermoresponsive hydrogels, which alter their shape in response to temperature changes, have crucial applications in wound dressings, sensors, and other biomedical contexts due to their responsive collapse behavior, high water content, and biocompatibility. Recent advancements in 3D printing have significantly improved the complexity and precision of hydrogel fabrication beyond traditional casting methods. Bioprinting is the most prevalent method for 3D printing hydrogels but is generally expensive, low-resolution, and restricted to academic settings. One alternative photopolymerization-based 3D printing offers greater accessibility and compatibility with synthetic hydrogel systems, capable of creating micrometer-sized features. However, the mechanical limitations of the printed objects and the temperature fluctuations during polymerization pose challenges for printing thermoresponsive hydrogels. This thesis aims to develop 3D printing methods for thermoresponsive hydrogels using a printed organo-gel precursor, which allows for enhanced mechanical properties without triggering thermoresponsive behaviors during printing. This research targets applications in wound dressings and digital health, facilitating point-of-care fabrication. Mask stereolithography was investigated for creating thermoresponsive hydrogels from poly(N-isopropyl acrylamide) (PNIPAm) and poly(oligoethylene glycol) acrylate, incorporating bio-based polysaccharides as strengthening additives and ionic crosslinkers. The first experimental system used PNIPAm with poloxamers and a double network of sodium alginate, yielding a resin capable of printing precise structures and forming patient-specific wound dressings. This system displayed superior mechanical properties at room temperature and temperature-dependent drug release and adhesion. However, the use of dimethyl sulfoxide (DMSO) and NIPAm’s neurotoxicity prompted a shift to poly(oligoethylene glycol) acrylate-based resins. In the second system, quaternized chitosan/3-sulfopropyl acrylate (QCh:SPA) salts and 2-hydroxyethyl acrylate (HEA) was investigated for producing supramolecular hydrogels along with the use of a cellulose-derived solvent Cyrene to replace DMSO making the process greener. These hydrogels exhibited enhanced elasticity and feature resolution compared to other systems, also showing conductive properties due to ionic interactions. In the third system, the studies were conducted to investigate the incorporation ethylene glycol methyl ether acrylate with HEA and the use of octylamine-grafted cellulose nanofibrils (OA-CNF) and sodium alginate to develop core-shell microparticles. This enhanced the hydrogel's mechanical properties and exhibited broad LCST behavior, offering improved stability and laying the groundwork for future enhancements aimed at refining printability and tuning LCST responses.
Investigating the Influence of Bacterial Cell Characteristics on M13 Phage Infection Process
(University of Waterloo, 2024-08-29) Haghayegh Khorasani, Seyedeh Sara
Microbial communities are fundamental to ecosystem health and biodiversity, affecting environments from soil to human microbiomes. Bacteriophages, or phages, are vital components of these communities, shaping bacterial dynamics and genetic diversity through mechanisms like gene transfer. Traditional population-level studies, while informative, can obscure the detailed behaviors and interactions present at the individual cell level. This research seeks to mitigate this oversight by applying single-cell analysis techniques to explore the M13 phage infection process. Focusing specifically on the interactions between the M13 bacteriophage and E. coli, this research employs time-lapse microscopy to investigate how individual bacterial cell characteristics— size, elongation rate, and spatial positioning—impact phage infection susceptibility. The experimental approach incorporates both microfluidic devices and agar pads to compare the effects of direct phage introduction versus in-situ phage production within mixed bacterial cultures. Image processing was conducted using the Ilastik and CellProfiler software, extracting vital cellular metrics, such as size, shape, elongation rate, and spatial distribution, for analysis. Subsequent post-processing, performed with custom MATLAB scripts, generated lineage trees for individual cells, enabling tracking and analysis of cellular behavior over time. Experimental results demonstrate that E. coli cells exhibiting higher elongation rates and larger sizes were notably more susceptible to M13 phagemid infection. This correlation underscores the significance of physical and physiological cell properties in the infection process. Moreover, this research extends its analysis through computational simulations employing the CellModeller platform, to investigate the M13 bacteriophage infection process beyond what is observable in laboratory experiments. The simulations are particularly concentrated on assessing how variations in phage diffusion rates impact the spatial patterns of infection, especially regarding the proximity of infected cells to those producing phages. The simulation results from this study highlight that an increase in the phage diffusion rate leads to a decrease in the distance between infected cells and those producing phages, suggesting that higher diffusion rates facilitate wider spread and more uniform distribution of the phage within the bacterial population. This pattern is consistent with the hypothesis that phage mobility plays a critical role in the dynamics of infection spread.
Development of Semiconducting Polymers with Acid-Cleavable Side Chains for Size-Selective Gas Sensors Based on Organic Thin-Film Transistors
(University of Waterloo, 2024-08-29) Zhong, YuFang
Organic thin-film transistor (OTFT) based gas sensors have gained research interests due to their promising performance and potential for integration into flexible and wearable electronic devices, characterized by low cost, light weight, and ease of fabrication. Herein, a series of novel semiconducting polymers with acid-cleavable side chains were synthesized for application as OTFT based gas sensors employing a size-selective approach. We first established a cost-effective and simple synthetic route to obtain acetal substituted thiophene based polymers. Subsequent conversion of acetal side chains to aldehydes was achieved through a simple acid treatment process applied to the coated polymer film by HCl vapor to generate nanopores. The pore sizes could be tuned by varying the lengths of the acetal side chains used. Screening of suitable polymer structures with desired pore sizes and adequate performance was accomplished by altering the acetal side chain lengths and the comonomer units. Overall results revealed that the aldehyde substituted polymer generated by acid treatment to OC21T polymer demonstrated the least sacrifice in OTFT performance upon side chain cleavage and the best stability under ambient conditions, which makes it the optimal candidate for OTFT based gas sensor material among all the polymers synthesized. The resulting aldehyde substituted thiophene based polymer (OCH1T) showed great sensitivity to methanol and ethanol vapors. Slight sensitivity of OCH1T polymer toward isopropanol vapor was also observed, which could be attributed to the grain boundaries of the polymer film. The promising size-selective effect was further confirmed by exposure of the device to other volatile organic compound vapors with various molecular sizes.
Development of Cellulose-based Softwood Pulp Foam for the Removal of Microplastics
(University of Waterloo, 2024-08-28) Choi, Hanyoung
Microplastics, generated from the decomposition of large plastic products, is one of the emerging pollutants that pose tremendous risks in the aquatic environment. Although previous studies have developed various strategies for the removal of microplastics, they were found to be non-renewable and costly. Cellulose provides green and sustainable approaches in water treatment systems as it is derived from plant sources making it biodegradable and biocompatible. In this study, two cellulose derivatives, microcrystalline cellulose (MCC) and nanocrystalline cellulose (NCC), were cationically and hydrophobically modified by grafting with (3-chloro-2-hydroxypropyl)dodecyldimethylammonium chloride (QUAB 342) to the particles. The modified systems were used in the development of softwood pulp foam for microplastic capture (WFQ342MCC-0.8 and WFQ342NCC-0.8 foams) The filtration performance of WFQ342MCC-0.8 foam was examined by analyzing its removal efficiency for polyethylene (PE) microplastics stabilized by sodium dodecyl benzene sulfonate (SDBS), polysorbate 80 (Tween 80), and hexadecyltrimethylammonium bromide (CTAB) surfactants (PE:SDBS, PE:CTAB, and PE:Tween80). The order of the removal efficiency during the filtration experiment was found to be PE:SDBS > PE:CTAB > PE:Tween80, respectively. Furthermore, WFQ342NCC-0.8 foam displayed removal efficiency of up to 99.8 %, as the addition of NCC improved the foam surface area with better microplastic capture.
Improving Lithium-Ion Battery Management Systems Using Equivalent Circuit Models, Cloud Platforms, and Machine Learning Estimation Techniques
(University of Waterloo, 2024-08-28) Tran, Manh Kien
Building a future that preserves the environment and reduces dependence on fossil fuels is an imperative undertaking, and it greatly hinges on the global transition to renewable energy. Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion (Li-ion) batteries are an excellent solution for energy storage due to their properties including high energy density, high power density, long cycle life, low self-discharge rate, no memory effects, and low environmental pollution. In order to ensure the safety and efficient operation of Li-ion battery systems, battery management systems (BMSs) are required. However, the current design and functionality of the BMS suffer from a few critical drawbacks including its low computational capability and limited data storage. The work in this thesis focuses on improving the BMS by investigating and improving the equivalent circuit model (ECM) which is often the core battery model used in practical BMS, researching and developing a smart BMS utilizing the cloud platform, and proposing potential applications of the cloud-based smart BMS such as cell replacement or SOH estimation using machine learning. One of the main focuses of this work is on the critical role of accurate battery modeling for safe and effective operation. The first contribution of this research is an investigation into the performance of three ECMs—1RC, 2RC, and 1RC with hysteresis—across four common Li-ion chemistries: LFP, NMC, LMO, and NCA. Experimental results demonstrate that all three models can be applied to these chemistries with low error rates, particularly under dynamic current profiles. The findings indicate that the 1RC with hysteresis ECM performs best for LFP and NCA chemistries, while the 1RC ECM is most suitable for NMC and LMO chemistries. These insights are crucial for optimizing BMS applications in real-world scenarios, highlighting the need to tailor ECM selection to specific battery chemistries. This work also seeks to add further to the improvement of the ECM used in the BMS, as another novel contribution. Its next research delves into the effects of state of charge (SOC), temperature, and state of health (SOH) on the parameters of the ECM, particularly the Thevenin model. While SOC and temperature are well-integrated into ECMs, the impact of SOH has been less explored. Through a series of experiments, it was found that as SOH decreases, both the ohmic and polarization resistances increase, while the polarization capacitance decreases. An empirical model was developed to represent the combined effects of SOH, SOC, and temperature on ECM parameters. This model was validated experimentally and showed significant improvements in accuracy without increasing complexity. The proposed model offers a practical solution for real-world BMS applications, enhancing the precision of battery monitoring and control. As part of the next steps in the research, the concept and development of cloud-based smart BMSs were discussed in detail. Traditional BMSs are limited by computational capacity and data storage, which can hinder the development of advanced battery management algorithms. A cloud-based BMS can address these issues by offloading computation and storage to the cloud, enabling more sophisticated and reliable battery algorithms. The study discusses the design, functionality, and benefits of cloud-based BMSs, including improved reliability and performance of Li-ion battery systems. It also explores the division of tasks between local and cloud functions, emphasizing the potential for significant advancements in battery management through cloud integration. This innovation is expected to play a pivotal role in advancing renewable energy technologies. Once the development of the cloud-based smart BMS has been discussed, the practical applications of such innovation are then examined. As the optimization of Li-ion battery pack usage becomes increasingly more necessary and given the inevitable degradation of Li-ion batteries, recent research has focused on maximizing the utilization of battery cells within packs. Another study, which is the next novel contribution of this work, investigates the feasibility and benefits of cell replacement within battery packs, using a simulation framework based on cell voltage and degradation models. The cloud-based BMS, with more data storage, shall be able to advance the cell replacement application by providing significantly more battery historical data. The study, conducted using MATLAB, simulates the life cycles of battery packs with varied cell configurations. Results show that cell replacement can significantly extend the lifespan of battery packs and is economically advantageous compared to a full pack replacement. For practical implementation, the design criteria include individual cell monitoring and easy accessibility for cell replacement, underscoring the potential for more efficient battery usage strategies. Another practical application of the cloud-based BMS with improved computational and memory capability is battery state estimation, specifically complex algorithms such as SOH estimation. A part of this work, its final novel contribution, develops a novel SOH estimation approach, which is crucial for the effective operation of Li-ion battery systems. Existing methods have limitations in adaptability and real-time application. This study introduces a machine learning-based approach for online SOH estimation during fast charging cycles. Using a dataset of 124 cells, with various machine learning algorithms tested, the neural network algorithm demonstrated superior accuracy with an RMSE of 9.50mAh and a MAPE of 0.69%. The methodology, which utilizes partial charge metrics without needing historical data, is highly suitable for real-time BMS integration. This approach enhances the reliability and performance of Li-ion battery systems, contributing to the broader adoption of electric vehicles (EVs) and renewable energy technologies. Overall, this thesis presents significant advancements in the field of battery management systems, particularly through the improvement of the ECM, the introduction of cloud-based smart BMSs, and the development of innovative cell replacement and SOH estimation methods. The cloud-based BMS would be able to solve the problems of limited computational capability and data storage. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. The cloud-based smart BMS is expected to improve the reliability and overall performance of Li-ion battery systems, contributing to the mass adoption of EVs and renewable energy.
Waterborne Biopolymer Dispersions for Barrier Paper Coatings
(University of Waterloo, 2024-08-23) Pieters, Kyle
Demand for alternatives to synthetic, non-degradable, and single-use plastic packaging is continually increasing. Paper products are environmentally friendly and offer a potential solution, but typically do not meet performance demands without coating them with a polymeric film, usually polyolefins. Replacing conventional plastic coatings with biobased and biodegradable alternatives can substantially improve product sustainability. Furthermore, using water as a coating medium imparts further environmental and coatability advantages. In this thesis, the current state of waterborne coatings in industry is analyzed. Waterborne coatings are increasingly being used, containing conventional polyolefins, with the next step being to move towards more sustainable polymer options. Two different waterborne dispersions are formed using distinct biopolymers and stabilization mechanisms. First, poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is dispersed in water via stabilization from the traditional surfactant sodium dodecyl sulfate (SDS). The dispersion demonstrates strong stability and coatability characteristics. Paper coated with the prepared waterborne PHBV dispersion exhibited strong barrier property performance relative to uncoated paper, with substantial improvements to water vapor permeability and grease resistance properties. A second study was performed in which a waterborne dispersion of cellulose acetate was formed using Pickering emulsion technique and cellulose nanocrystals. This dispersion exhibited similarly strong performance characteristics, with the coating again demonstrating strong barrier performance. Comparison to an industrially available product yielded competitive performance results as a food packaging container. Overall, the work demonstrates strong applicability of waterborne biopolymer dispersions as sustainable coating options.
Sustainable Antimicrobial Nanocomposites Using Functionalized Cellulose Nanocrystals
(University of Waterloo, 2024-08-22) Han, Lian
The primary focus of this thesis is to exploit the application of cellulose nanocrystals (CNC) as environmentally friendly antimicrobial nanomaterials. Chitosan, a readily available natural polysaccharide, was combined with CNC to prepare various types of nanohybrid constructs via electrostatic coating or chemical grafting. The surface charge, morphology, stability, rheology, antimicrobial properties, and biocompatibility of these nanohybrids were quantified and elucidated. These innovative antimicrobial nanohybrid systems hold promise for a multitude of applications spanning industries such as textiles, tissue engineering, surgical materials, water treatment, agriculture, and food packaging. A fully biobased colloidal antimicrobial nanohybrid system, comprising cellulose nanocrystals (CNC), chitosan, and a chitosan derivative, was examined. The research focused on the modification of chitosan by varying the degree of substitution, and the preparation of CNC-CS nanohybrids in both acidic and neutral pH. By examining the surface charge, particle size, morphology, and acid-base conductometric titration, the electrostatic coating process of chitosan onto the surface of CNC was elucidated. Lastly, the antimicrobial efficacy of the CNC-CS nanohybrid was assessed through a simple and rapid antifungal assay protocol. A comparative analysis between electrostatically coated and chemically grafted glycidyltrimethylammonium chloride modified-chitosan (GCS) on cellulose nanocrystals (CNC) was conducted. The hypothesis is that through grafting GCS onto modified CNC, the resulting CNC-GCS hybrid could potentially enhance the colloidal stability while maintaining its superior antimicrobial properties. Surface-functionalized aldehyde-CNC would interact with GCS leading to the formation of a durable grafted CNC-CS nanohybrid. The effect of surface charge, stability, rheological properties, antimicrobial efficacy, and biocompatibility of this innovative hybrid system was elucidated. It was found that the covalently attached and reduced rDCGCS exhibited the most potent antimicrobial property, with a minimum inhibitory concentration (MIC) of 150 μg/mL against yeast and a minimum bactericidal concentration (MBC) between 200-400 μg/mL against S. aureus. However, due to its mechanism of action primarily relying on electrostatic interactions, the nanohybrid system demonstrated lower efficacy against gram-negative bacteria. Based on the grafting system methods described earlier, further enhancement of the antimicrobial properties was examined by the in-situ inclusion of silver nanoparticles (AgNPs) on the CNC-CS system. The hypothesis was whether the integration of AgNPs with the CNC-CS nanohybrid could facilitate both the sustainable production of AgNPs and a significant boost in the antimicrobial efficacy of the hybrid material. The preparation procedure, surface charge analysis, stability assessment, antimicrobial performance, and elucidation of the mechanism of action of this composite system were elucidated. When compared to DAC-Ag, CCS-Ag displayed elevated positive charge, reaching a zeta potential of up to +60 mV. Additionally, it exhibited superior capping capabilities resulting in more uniform and smaller AgNPs size, along with exceptional stability. The antibacterial effectiveness was notably enhanced and possessed a MBC ranging from 50-100 μg/mL against S. aureus and 100-200 μg/mL against E. coli. The CNC-CS composite systems can serve as the carrier for loading and encapsulating a model antibacterial compound, triclosan that will enhance the sustainability of the system for practical applications. A comparative analysis was conducted between pristine CNC, CNC-CS coating, and CNC-CS grafting systems, where the stability, loading efficiency, morphological characteristics, antimicrobial efficacy, and the underlying mechanisms of action for each of these systems were examined. The research revealed that the CNC-GCS coating system exhibited the highest loading capacity, successfully accommodating and encapsulating up to 5% of triclosan (TCS) within the nanohybrid. The resulting CCS-TCS displayed a fourfold improvement in antifungal performance, showcasing a MIC ranging from 100-200 μg/mL. Furthermore, it possessed potent antibacterial properties, with a MBC of 50-100 μg/mL against gram-positive bacteria and 100-500 μg/mL against gram-negative bacteria. Additionally, a novel polymer nano-brush based on cellulose nanocrystal (CNC) utilizing a "graft-from" technique incorporating 2-(dimethylamino)ethyl methacrylate (DMAEMA) was prepared and investigated. This polymer-grafted CNC structure features tertiary ammonium groups within its side chains that could be quaternized. The surface charge characteristics, morphological attributes, extent of quaternization, and antimicrobial properties associated with this quaternary ammonium polymer-grafted CNC structure were examined. Subsequently, the PCNC was successfully quaternized with benzyl bromide, achieving a degree of quaternization (DQ) of up to 51%. The resulting QCNC displayed strong antimicrobial efficacy, presenting a MBC of 50-100 μg/mL against gram-positive bacteria and 100-200 μg/mL against gram-negative bacteria. In comparison to CNC-CS, QCNC exhibited improved antimicrobial properties characterized by a smaller and more uniform particle size.
Entry of the Electrolytic Ammonia Industry: Incentives and Effects
(University of Waterloo, 2024-08-19) Cunanan, Carlo
Ammonia is an essential chemical to agriculture because of its tremendous positive impact on plant nitrogen uptake. Furthermore, due to its possible use as a hydrogen energy vector, ammonia is being considered as a method for green energy storage for the hydrogen economy. Ammonia production is traditionally a carbon-intensive process due to using natural gas as a feedstock. However, efforts are being made to reduce its carbon footprint through methods such as electrolysis, which uses water as the feedstock for hydrogen rather than natural gas. This thesis uses engineering and economics techniques to evaluate the viability and economics associated with producing and using electrolytic ammonia in Canada's food and energy sectors.
CO2 conversion through Reverse Water Gas Shift over Molybdenum and Tungsten Carbides: Catalytic Performance Evaluation
(University of Waterloo, 2024-08-19) Ashirwadam, Anik
Mitigating climate change requires reducing CO2 emissions and conserving energy, but current CO2 capture and storage methods are complex and costly. An alternative is the thermocatalytic hydrogenation of CO2 using the Reverse Water–Gas Shift (RWGS) reaction to produce value-added chemicals. Transition metal carbides (TMCs) offer significant potential in this area. This research aims to assess the catalytic performance of Molybdenum carbide (Mo2C) and Tungsten Carbide (WC) to enhance CO2 conversion and CO selectivity in the RWGS reaction. Using the Temperature Programmed Reduction (TPR) method (15% CH4/H2, 600°C), Mo2C, WC, and five mixed carbides (MoWxC with x=0.25, 0.5, 0.75, 1, and 1.5) were synthesized and compared. The catalyst with the highest selectivity and good conversion was chosen to optimize the RWGS reaction. The performance of TMCs was assessed in terms of both conversion and selectivity by varying four parameters such as Temperature (500°C - 350°C), Pressure (1 to 9 bar), GHSV (20,000 to 400,000 mlg-1*h-1), H2:CO2 Feed Ratio (1:1 to 5:1). To evaluate the catalyst resilience, stability tests have been also performed. Moreover, the structure of pre and post reaction catalyst has been investigated. The resulting reaction products were monitored using an in-line Infrared Analyzer to identify the concentration of CO, CH4, and CO2. The results indicated that at 500°C, CO2 conversions approaching equilibrium for most carbide samples, categorized into three main groups. Absence of W (Mo2C) resulted in higher conversion but lower selectivity (Group 1). Higher W concentrations in MoW1.5C and WC led to higher selectivity but lower CO2 conversion (Group 3). In Group 2, MoWxC (x=0.25, 0.5, 0.75, and 1) showed better conversion and selectivity, with MoW0.25C and MoW0.5C exhibiting higher CO2 conversion and CO selectivity than MoW0.75C and MoWC. WC was chosen for its high CO selectivity and good CO2 conversion for optimizing the RWGS reaction. Under optimized conditions (500°C, GHSV = 20,000 ml g⁻¹ h⁻¹, Feed Ratio= 5:1, atmospheric pressure), WC showed 100% selectivity and 33% conversion, maintaining stability for 100 hours with full CO selectivity and stable CO2 conversion. Increasing the feed ratio for WC increased conversion with full CO selectivity, while for Mo2C, more hydrogen led to more methane formation. This study serves as a foundation for the optimization of Mo2C and WC, aiming to convert the global challenge of CO2 into an opportunity by producing renewable value-added chemicals and feedstock.
Engineering Escherichia coli for Carotenoid Biosynthesis
(University of Waterloo, 2024-08-13) Li, Jiaqing
This research integrated the isopentenol utilization pathway (IUP) into the E. coli chromosome and utilized various promoters to optimize lycopene production. Additionally, the research evaluated the effects of overexpressing monoglycosyldiacylglycerol synthase (MGS) and diglucosyldiacylglycerol synthase (DGS) from Acholeplasma laidlawii, to induce the formation of intracellular membrane vesicles, potentially increasing the cell’s capacity to store hydrophobic compounds like carotenoids. In a second strategy, the application of knock-out mutants for Braun's lipoprotein (lpp) in E. coli led to the production of extracellular membrane vesicles to avoid intracellular enzyme accumulations. Results demonstrated that integrating the IUP in the chromosome significantly improved lycopene yields compared to traditional pathways and the plasmid system. The overexpression of MGS and DGS resulted in increased intracellular lipid content but did not significantly enhance carotenoid production beyond IUP-expressing strains. Notably, knocking out lpp yielded a 3.3-fold increase in extracellular lycopene production. Overexpressing MEP pathway enzymes, including IDI, GGPPS, CrtI, CrtB, DXS, IspD, IspF, IspG, and IspH in the leaky strain, further boosted lycopene yields. This research underscores the potential of genetic and metabolic engineering to optimize isoprenoid production in microbial hosts, paving the way for more efficient and scalable production methods for these valuable compounds. The findings highlight the efficacy of the IUP and extracellular production strategies in overcoming traditional pathway limitations and enhancing yields, thereby contributing to the broader application of microbial biosynthesis in industrial and pharmaceutical contexts.
Vanadium-based and Manganese-based Cathode Material for Rechargeable Aqueous Zinc-ion Batteries
(University of Waterloo, 2024-08-13) Han, Mei
Rechargeable batteries offer a feasible solution to storing the intermittent energy supplies associated with renewable energy production. Despite the dominance of lithium-ion batteries (LIBs) in the current battery market, their application is hindered by the scarcity of lithium resources, unaffordable costs, and safety concerns. Consequently, rechargeable aqueous zinc-ion batteries (RAZBs) with mildly acidic electrolytes have garnered attention due to their cost-effectiveness, high safety and environmental friendliness. However, identifying suitable zinc ion intercalation-type cathode materials that meet commercial standards remains a significant challenge, impeding the widespread adoption of RAZBs. Vanadium- and manganese-based compounds, recognized for their unique structures and substantial theoretical capacities, are among the foremost cathode materials for RAZBs. Nonetheless, these materials often suffer from structural degradation during cycling, limited electrical conductivity, and severe side reactions, substantially restricting their practical applications. In this thesis, we introduce strategies to improve the electrochemical performance of vanadium-based and manganese-based cathodes in RAZBs through ionic pre-intercalation techniques and the integration of cathode-electrolyte interface layers, respectively. In particular, the RAZB with an improved vanadium-based cathode maintains 90% capacity after 4000 cycles and achieves a discharge specific capacity of 209 mAh g-1 at 5 C. Furthermore, our in-depth analysis of the reaction mechanisms in vanadium-based cathodes with pre-intercalated ions uncovered a reversible dual-cation (Zn2+ and Na+) intercalation chemistry. This not only stabilizes the vanadium-based material structure, but also facilitates the free access of ions from the electrolyte to the cathode, thus mitigating structural collapse or failure due to ion insertion during cycling. In the study of MnO2 cathodes, we have prioritized the exploration of the intrinsic failure mechanism and disclosed the phenomenon of "ionic crosstalk" between electrodes for the first time. The release of a significant amount of Mn2+ ions from the MnO2 cathode detrimentally impacts the ion concentration on the Zn anode surface, which is detrimental to the uniform deposition of zinc metal and exacerbates the growth of dendrites as well as anode corrosion; simultaneously, the stripping of Zn2+ ions from the zinc anode results in the formation of by-products on the cathode and triggers irreversible phase transitions in the cathode material. These ionic crosstalk effects exacerbate electrode deterioration, culminating in the failure of the Zn-MnO2 battery system. To address this, we apply a hierarchical porous membrane on the MnO2 cathode surface to mitigate ionic crosstalk and promote reversible dissolution/deposition reactions. As a result, the cell demonstrates an exceptional capacity retention of 97% after 1000 cycles at 2 C and an operational lifespan exceeding 500 hours, markedly outperforming previously reported aqueous Zn-MnO2 batteries by over 1.5 times. Furthermore, to explore the commercial potential of MnO2 cathode materials, we combine the liquid-phase in situ encapsulation method with a straightforward heat treatment to cover a Bi2O3 layer on the MnO2 material surface. This approach facilitates a significant increase in the mass loading of the cathode material to 16 mg cm-2. Our findings reveal that these cathodes exhibit exceptional cycling stability and Coulombic efficiency in larger battery configurations, with an exceptional capacity retention of 72.7% over 330 cycles (equivalent to a calendar life of 60 days), showcasing its superior performance and reliability for high-energy-density battery applications. These results not only underscore the significant potential of Bi2O3 coating technology to advance the development of aqueous Zn-MnO2 batteries but also lay a solid foundation for the commercialization of aqueous batteries.
Characterizing Self-assembled Nanostructures via Shapelet Functions
(University of Waterloo, 2024-08-12) Tino, Matthew Peres
Pattern formation is a natural phenomena that occurs at various length-scales. Lattice patterns are a particular type composed of spatially-repeating features with stripe, square, or hexagonal symmetries. They are of particular interest to nanotechnology researchers due to their frequent appearance in self-assembly and lithography processes. Self-assembled nanostructures provide many technological applications but are difficult to characterize due to deformations in local structure (defects, disorder). While image-based characterization techniques for nanostructures are well-known (i.e., scanning electron microscopy), appropriate computational techniques to characterize their structure are seldom developed and are typically without readily available open-source implementations. Characterization of self-assembled nanostructures is important to develop structure-property relationships with potential to advance defect engineering research. Defect engineering corresponds to the regulation of specific defects within nanostructure to manipulate material properties (physical, chemical, magnetic) and improve material functionality. Existing techniques to characterize self-assembled nanostructures, including Voronoi diagrams/entropy, bond-orientational order theory, and Fourier space filtering are well-known but contain inherent limitations. A more recent and promising approach uses a set of localized basis functions called shapelets, originally designed for the compression and reconstruction of images of galaxies. This approach uses polar shapelets, providing unique rotational/radial symmetry properties beneficial for analysis on non-Euclidean geometries. This response distance method is a supervised learning technique that quantifies local deformations in structure (defects, disorder) apart from regions displaying uniform pattern order. This work presents extensions to the existing response distance method, including a decrease in computational runtime, along with the inclusion of higher-order shapelet functions to improve order quantification in areas with topological defects. New shapelet-based methods are also presented, such as quantification of local pattern orientation and a technique to directly identify topological defects and defect structures. These methods are validated against both simulated and experimental surface images of self-assembled nanostructures containing stripe, square, and hexagonal patterns and demonstrates their effectiveness in the presence of measurement noise. Furthermore, they are made available to the community as part of an open-source Python library, along with reference implementation of other shapelet functions and applications to promote collaboration and transparency in shapelet research. The mathematical framework for a higher-order shapelet class with radial symmetry is also provided, laying the foundation for future pattern analysis.
Modelling and Performance of a Hydrogel-Based Photobioreactor
(University of Waterloo, 2024-07-05) Rasmussen, Nicholas
This work is motivated by the need for in situ food production with respect to future space activities due to the technical and economic in-feasibility of long-term earth-based resupply. The unique size constraints of space have prevented conventional food systems from demonstrating feasibility. Owing to their high growth rates and phototropic activity, microalgae are a promising candidate to meet the caloric and respiratory needs of astronauts as part of a biological life support systems (BLSS). However, the gravity dependence and size of transitional photobioreactors poses a challenged to their utilization in space. As such, a solid-state hydrogel-based photobioreactor (hPBR) is proposed to achieve inherent phase separation allowing for extra-terrestial use. Initially proposed for the Canadian Space Agency (CSA) Deep Space Food Challenge (DSFC) (Design A), this design was further iterated to improve productivity and reactor performance (Design B). Using Chlorella vulgaris, Design B achieved a biomass productivity of 2.4 and 3.2 g m−2d−1 when using physically (pPVA) and chemically (cPVA) crosslinked poly(vinyl) alcohol (PVA) respectively with a water demand of 0.44 kg g−1 biomass. Over 23 days of growth, the lipid content increased from 18.9% to 56.6% and 13.8% to 43.2% for pPVA and cPVA respectively, and the chlorophyll content also decreased. However, cell viability remained high at over 97% and surface coverage analysis showed good coverage within a few days. Observations made with the prototype suggested mass transport limitations were impacting growth, and that poor humidity control led to the hydrogels drying out. To this end, a continuum model of the hydrogel was proposed to better understand mass transfer and to inform future design iterations. Hydrogels are two phase systems where the polymer is fixed due to crosslinking leading to a moving boundary with changes in water content. The proposed model did not require any parameter fitting as values were determined with independent experiments. The model enabled the prediction of the transient material response to changing relative humidity. This helped to explain why humidity control was critical in maintaining cell viability. Humidity impacted the water content of the gel’s surface which needed to be high enough to support algae growth. Using the steady-state solution to the model, the solute transport through the system was also modelled. The solute profile suggested that nutrient concentrations throughout the hydrogel were similar to that in the media tank. This suggests nutrient supply was not the cause of the diminishing biomass quality and that other factors such as photo-inhibition, and mechanical stresses from solid-state cultivation may be issues to address in future work.
Rational Design of Engineered Porous Transition Metal-based Electrocatalysts for Rechargeable Zinc-air Batteries
(University of Waterloo, 2024-06-27) Zhang, Yatian
The adoption of primary zinc-air batteries (ZABs) for telecommunication and medical applications underscores their commercial viability. However, the progress of ZABs have been hindered by challenges associated with air electrodes. The substantial electrode polarizations of the Oxygen Reduction Reaction (ORR) and the Oxygen Evolution Reaction (OER) pose significant energy barriers, impeding the efficiency of charge and discharge processes. Hence, there's an urgent need to develop bifunctional electrocatalysts with superior performance, energy efficiency, and long-term stability for ZABs. In the first work, three-dimensional interconnected and ordered mesoporous Fe2Nx decorated on TiOy with the introduced nitrogen vacancies was constructed (Fe2Nx@TiOy). By creating defects in ordered porous materials, the increased surface area, pore volume, and active sites boost the kinetics of the ORR. Fe2Nx@TiOy with created nitrogen defects reveals a superior ORR performance, including a high half-wave potential (0.88 V vs reversible hydrogen electrode) and high current density (71 mA cm-2 at 0.8 V). The zinc-air battery assembled with Fe2Nx@TiOy catalysts presents a high specific capacity of 809 mAh g-1. Density functional theory (DFT) analysis and X-ray absorption spectroscopy further confirm that the engineering of nitrogen vacancies modulates the electronic environment of Fe and regulates the adsorption and desorption of intermediates to facilitate the ORR activity. The Fe d-band center moving toward the Fermi energy level strengthens the interaction between the adsorbate and substrate, allowing oxygen species to be favorably stabilized onto Fe2Nx@TiOy, while significantly reducing the kinetic barrier. This work serves as a guideline for developing effective defect engineering and ordered porous materials for efficient energy conversion and storage. In the second work, among a series of ternary Cu-Ti-O electrocatalysts, a hierarchical macroporous Cu0.3Ti0.7O2 catalyst achieves a balance between structural stability and active sites exposure, showing an electron density reconfiguration in the Cu-Ti-O system. X-ray absorption fine structure analyses reveal the partial electron density reconfiguration presented among Cu, Ti, and O atoms can be the dominant reason for the peaks shift. It was demonstrated that Ti atoms tended to delocalize maximum charge by releasing it to the Cu atoms in the compositions of Cu0.3Ti0.7O2, which lower the thermodynamic barrier of the total reaction, and hence contributes to a remarkable enhancement in zinc-air battery. This work offers an attractive approach to developing the nonprecious transitional metal-based ORR/OER catalysts, and zinc-air battery for the design of performance-oriented electrocatalysts for wider electrochemical energy applications. In the last work, a unique Mg-decorated three-dimensionally ordered mesoporous (3DOM) Co3O4 electrocatalyst is engineered and evaluated as cathodic material for zinc-air batteries. The modulation of electronic structure and bonding configuration of Co sites through coordination with substituted Mg atoms effectively enhance the interaction with oxygen species and, therefore, the ORR/OER activity. Meanwhile, the substitution of Co2+ with Mg2+ creates abundant, more catalytically active octahedral sites (Co3+) in 3DOM-MgxCo3-xO4. Moreover, the tailored 3D interpenetrating porous structure endows the electrocatalyst with large diffusion channels for oxygen species and highly accessible active sites. The as-prepared catalyst retains 99% and 98% of its initial ORR and OER current, respectively, after 16 h under chronoamperometric measurement. The zinc-air battery assembled with 3DOM-MgxCo3-xO4 exhibits a high power density of 253 mW cm-2 and long-term cyclability over 236 h, outperforming the commercial noble-metal-based catalysts in terms of performance and stability. This work offers a straightforward and promising design strategy for the development of robust bifunctional electrocatalysts toward practical applications of zinc-air batteries. In summary, this thesis exhibits three types of transition metal-based materials with hierarchical three-dimensional porous structures applied in rechargeable zinc-air batteries. The main emphasis is focused on the synthesis and electrocatalytic activity as well as the underlying mechanisms for these materials in zinc-air batteries. It gives a prospect that is expected to engineer and synthesize porous transition metal-based materials for zinc-air batteries.
Synthesis and Characterization of Polymeric Sensing Materials for Detection of Gases in Energy Storage Devices
(University of Waterloo, 2024-06-21) Ghodrati, Shahrzad
The increasing popularity of portable electronic devices, electric vehicles, and smart grids has created a need for energy storage systems including battery technology with lithium-ion batteries (LIBs) being one of the most common battery types. However, enhancing the safety of these LIBs remains a prominent aspect that requires advancements in battery technology as it has been shown that gas evolution occurs in LIBs. The identification and detection of these gases (which can be hazardous in different ways) are critical to protecting human and environmental health. Hence, there is an urgent need for gas-sensing devices (i.e., gas sensors) to minimize concerns regarding health, safety, and the environment. This thesis presents an investigation on the design, evaluation, and characterization of polymeric gas sensing materials for the room-temperature detection of harmful gases (in ppm levels) generated in energy storage devices (e.g., lithium-ion batteries). The importance of gas sensing materials is well recognized as the sensing material is the ‘heart’ of a sensor that interacts with the target analyte, leading to a detection signal generated by the sensor. Four gases, namely, hydrogen (H2), ethylene (C2H4), carbon monoxide (CO), and carbon dioxide (CO2), were found to be the main gases released in LIBs and identified as target gases for detection. Polymers modified/doped with metal oxides have displayed reasonable sensing behavior making them promising sensing materials in gas sensor applications. Polyaniline (PANI) doped with various concentrations of different metal oxide nanoparticles were synthesized and evaluated as sensing materials for target analytes, along with other polymeric materials like polypyrrole (PPy), polythiophene (PTh), and polyvinylpyrrolidone (PVP). Gas sorption characteristics were evaluated using formaldehyde as a "simulant" or "surrogate" due to safety concerns associated with testing target analytes in an academic environment. The doped PANI materials, in particular, exhibited enhanced gas sorption properties, attributed to the synergistic effects of the dopants, which improved the interaction between the polymer matrix and gas molecules. The effect of environmental factors (e.g., ageing), on the sensing performance, related to the sensing material stability, was also evaluated for selected sensing materials. Other property characteristics of the sensing materials were also determined using different techniques such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-rays (EDX), dynamic light scattering (DLS), and Brunauer-Emmett-Teller (BET) tests, to provide a more detailed explanation and additional confirmation of the sorption trends. In the final step, optimal sensing materials were deposited on a MEMS (micro-electro-mechanical system) sensor, which is efficient, inexpensive, and of small size. The sensor as a whole was then evaluated for its sensing performance towards 50 ppm ethylene.
Robust NMPC of Large-Scale Systems and Surrogate Embedding Strategies for NMPC
(University of Waterloo, 2024-06-20) Elorza Casas, Carlos Andrés
Non-linear model predictive control (NMPC) is a promising control algorithm due to its ability to deal with constrained multivariable problems. However, NMPC can be computationally expensive to solve due to its non-linear nature, multiple interacting process units and the presence of model uncertainty. Real-world NMPC applications also necessitate state estimation for feedback control. While robust NMPC and state estimators have been studied individually for large-scale problems, understanding their combined impact is crucial for wider NMPC adoption. Integrating tractable Machine Learning (ML) surrogates, particularly Neural Networks (NNs), into NMPC to reduce the computational load is an emerging strategy. However, embedding NN surrogates in NMPC, in a form amenable to simultaneous solution approaches, remains unresolved. This thesis aims to address two major NMPC implementation issues. First, this work analyses the combined impact of uncertainty and state estimation on the performance of NMPC on large-scale systems. Two scenario-based robust approaches to NMPC, multi-scenario NMPC (MSc-NMPC) and multi-stage NMPC (MS-NMPC), are implemented on the benchmark Tennessee-Eastman (TE) process in closed-loop using two standard state estimation algorithms, Extended Kalman Filter (EKF) and Moving Horizon Estimation (MHE). Robust NMPC with MHE is shown to prevent constraint violation while closely tracking the set-points under process uncertainty where traditional NMPC failed. The additional computational time required to solve the robust NMPC and MHE does not cause significant delays for the sampling time considered, demonstrating their applicability to challenging large-scale industrial chemical and manufacturing processes. This work also aims to benchmark various strategies for embedding NN surrogates in NMPC. One strategy embeds NN models as explicit algebraic constraints within the optimization framework, leveraging the auto differentiation (AD) of algebraic modelling languages (AMLs) to evaluate the derivatives. Alternatively, the surrogate can be evaluated externally from the optimization framework, using the efficient AD of ML environments. Physics-informed NNs (PINNs) and Physics-informed Convolutional NNs (PICNNs) are used as NN surrogates due to their ability to maintain fidelity to fundamental physics laws while reducing the need for historical/process data. The study reveals that replacing mechanistic models with NN surrogates may not always offer computational advantages, even with highly nonlinear systems. Smooth activation functions provide little to no advantage over the mechanistic equations when a local non-linear program (NLP) solver is used. Moreover, the external evaluation of the NN surrogates often outperforms the embedding as algebraic constraints, likely due, to the difficulty in initializing the auxiliary variables and constraints introduced with the explicit algebraic reformulations.
Rheology of Suspensions of Solid Particles in Liquids Thickened by Starch Nanoparticles
(University of Waterloo, 2024-05-24) Ghanaatpishehsanaei, Ghazaleh
This study explores the rheological characteristics of suspensions containing solid particles dispersed in aqueous matrix phase thickened with starch nanoparticles (SNP). The SNP concentration ranged from 5 to 35 wt% relative to the aqueous matrix phase, while the solids concentration of the suspensions varied from 0 to 57 vol%. Two different size solid particles were used in the experiments. Observations revealed that suspensions at constant SNP concentrations exhibited Newtonian behavior at low solids concentrations but transitioned to non-Newtonian shear-thinning behavior at higher solids concentrations. Notably, an increase in SNP concentration led to an earlier onset of non-Newtonian behavior at lower solids concentrations. The rheological properties of non-Newtonian suspensions were effectively characterized using a power-law model, with the consistency index showing a positive correlation with suspension solids concentration at any given SNP level. Furthermore, the flow behavior index, indicative of shear-thinning behavior, decreased with increasing solids concentration, suggesting an amplification of shear-thinning tendencies in the suspensions. The effect of particle size on the rheological behavior of suspensions was found to be insignificant. Experimental viscosity and consistency data for both Newtonian and non-Newtonian suspensions aligned well with predictions from the Pal model.
Modification Strategy for Mn-based Layered Transition Metal Oxide as Sodium-ion Battery Cathodes
(University of Waterloo, 2024-05-23) Wong, Ka Ho
Sodium-ion batteries (SIBs) are being touted as the future of energy storage. However, the lackluster performance of current cathode technology is a major roadblock to their widespread use. Among the promising candidates for cathodes, layered sodium manganese oxide stands out due to its low cost and higher energy density. However, its cycling performance is limited due to structural and surface instabilities. To overcome these challenges, researchers are exploring various strategies, such as doping, coating, and heterostructure design, to enhance the performance of manganese-based oxide. Doping involves introducing foreign atoms to enhance structural stability and electrochemical performance. Coating is a surface protection method, while heterostructure design involves developing a composite material composed of different crystal phases of sodium manganese oxide to leverage the intrinsic advantage of each phase. By analyzing the latest research, a novel coating approach of utilizing functionalized polymer (polyamic acid) as an encapsulation layer for P2-Na0.7MnO2 cathode is demonstrated. The polymer is equipped with abundant functional groups such as hydroxyl, carboxyl, amide, fluoromethyl, and aromatic, that endow a high oxidative stability and high toughness, thereby mitigating structural transition and electrolyte decomposition. Additionally, a high percentage of polar groups enable ionic conduction of Na+ through the polymer coating, as well as reducing active material dissolution through a chelation mechanism. Hence, the encapsulated cathode exhibits significant improvement in its cycling performance, maintaining stable discharge capacity for 500 cycles at 1000 mA g-1.
Recovery of Volatile Aroma Compounds by Membranes
(University of Waterloo, 2024-05-03) Davari, Susan
This research investigates the potential application of poly(ether block amide) (PEBA) membranes for the separation of volatile aroma compounds from wine and the effect of non-volatile components on the separation performance using the pervaporation process. The study examined the selective retrieval of two aroma compounds (4-ethyl guaiacol and 4-ethyl phenol) from binary dilute aqueous solutions through pervaporation utilizing the PEBA 2533 membrane. It was observed that this membrane effectively recovers hydrophobic aroma compounds. The influence of feed concentration and temperature on aroma recovery was also analyzed. The performance of PEBA 2533 for aroma recovery was assessed, and experimental data were analyzed using a batch pervaporation model. It was discovered that both the flux of aroma compounds and their selectivity were notably influenced by the concentration of aroma compounds in the feed. The permeation flux and their selectivity in separating the volatile aroma compound in a binary solution followed the sequence of 4-ethyl phenol > 4-ethyl guaiacol, showing an inverse relationship with their molecular size. Generally, the permeation flux of aroma was found to be directly proportional to the concentration of aroma compounds in the solution within the tested concentration range (10-110 ppm). The impact of temperature on permeation flux followed an Arrhenius-type relationship and 4-EG with larger molecular size showed higher apparent activation energy than 4-EP and water. It was observed that the recovery of 4-Ethyl guaiacol from its dilute aqueous solution was affected by non-volatile wine components (sugar, yeast, and salt) and alcohol. Specifically, the presence of glucose as a model sugar and NaCl as a model salt in the feed solution did not notably affect the pervaporative performance of 4-EG, maybe because of their low contents in the feed mixture and low interactions with aroma. The addition of agar initially increased the permeate flux of 4-EG due to its insolubility and ability to absorb water molecules, boosting the concentration of 4-EG and enhancing the driving force. However, at higher agar concentrations, precipitation formed a thick layer of swollen agar in the tank, trapping 4-EG molecules and reducing their concentration in the solution. This led to a peak flux followed by a decline, reaching a maximum turning point at a specific agar concentration. Finally, the presence of ethanol as a model alcohol in the binary solution of 4-ethyl guaiacol was found to significantly reduce the permeation of 4-ethyl guaiacol. However, the total flux of the mixture considerably increased. The presence of ethanol affected the partitioning and activity coefficients of the components in the mixture as well as membrane swelling and plasticization, which ultimately affected the solubility and diffusivity properties of the membrane.

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