Chemical Engineering

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

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DESIGN OF ONLINE ESTIMATOR FOR CULTURE MONITORING AND MEDIA DEVELOPMENT FOR BORDETELLA PERTUSSIS
(University of Waterloo, 2024-09-25) Mishra, Abhishek
Whooping cough, or pertussis, is a contagious respiratory infection. Sanofi Pasteur’s pertussis vaccine production involves fermentation using reactors of increasing sizes, where cells from each stage inoculate the next. The upstream product undergoes purification to extract the five antigens needed for the acellular vaccine. A major challenge is the variability in antigen yields, particularly for pertactin, which limits overall productivity. Despite controls for parameters like pH, temperature, agitation, aeration, and nutrient feed rates, there’s no real-time monitoring to confirm growth and productivity during the batch run. This often leads to undetected deviations and potential yield losses, making fault detection methods essential for identifying variations and ensuring productivity. The current work deals with the development of a method of bioprocess monitoring using soft sensors for Bordetella pertussis cultures and studying changes in media composition that impact the yield of antigens. Accordingly, the following objectives were pursued: 1) Development of a real time fluorescence-based monitoring system. 2)Investigation of possible sources of oxidative stress and its impact or correlation with antigen production. 3) Development of a protocol for simultaneous monitoring of oxidative stress and antigen produced. 4) Development of a mechanistic model for use in model-based filtering of an online fluorescence based sensor. This thesis presents the design and implementation of an in-line fluorescence spectroscopy system capable of real-time monitoring of critical bioprocess parameters. By utilizing dual excitation wavelength fluorometry, this method was able to track the dynamics of biomass, amino acids, and antigen production throughout the fermentation process. The fluorescence data, combined with statistical technique such as Partial Least Squares (PLS) regression was used for predicting key state variables. A significant enhancement in the predictive accuracy of the PLS models was observed when the models were calibrated for the bacterial strain and the media composition used in each cultivation process. Previous research by our group (Vitelli et al. [2023c], Zavatti et al. [2016]) indicated a potential link between oxidative stress and antigen yield variability, particularly for pertactin. We hypothesized that glutamate, the most abundant carbon source, could be causing this oxidative stress. To test this, we developed a multi-parametric flow cytometry method to simultaneously monitor intracellular ROS and pertactin surface expression. Given that pertactin is an auto-transporter protein, understanding the relation between expression and secretion was crucial. Our findings showed a negative correlation between oxidative stress and pertactin surface expression. Using a tailored protein quantification method via affinity chromatography, combined with multi-color flow cytometry, we confirmed that higher glutamate concentrations induced higher oxidative stress and result in reduced pertactin secretion. The studies inlcuded both batch and fed batch bioreactor experiments where the latter closely emulated the production environment at Sanofi. Hence, the findings demonstrated that variability in initial glutamate concentrations can have a major impact of productivity and may partially explain the variability observed in the manufacturing process. The model from (Vitelli et al. [2023c]) was adapted to formulate a hybrid model that was used for model-based filtering of an online sensor. This mechanistic model incorporated the interactions between glutamate, ROS, and NADPH in neutralizing ROS. After calibration, it accurately predicted key variables under different oxidative stress conditions. In parallel, PLS regression models were developed using in-line fluorescence spectra which could predict OD, glutamate, and NADPH. By using a hybrid model that combines the mechanistic and PLS regression models via an Extended Kalman Filter, more accurate real-time estimates of key variables were obtained. The Akaike Information Criteria (AIC) confirmed that this hybrid model achieved a superior balance between complexity and accuracy as compared to purely mechanistic or PLS models.
Application of Deep Learning in Pharmaceutical Processes: Monitoring, Diagnosis and Modeling
(University of Waterloo, 2024-09-24) Aghaee Foroushani, Mohammad
In recent years, artificial intelligence (AI) has emerged as a transformative technology in various fields, including pharmaceutical processes. The integration of AI in pharmaceutical manufacturing offers significant potential for enhancing process monitoring, fault detection, and predictive modeling. By leveraging advanced machine learning and deep learning algorithms, AI enables the development of detailed models that can handle the complexities and nonlinearities of biopharmaceutical processes. These models facilitate real-time monitoring, allowing for the early detection of process deviations and faults, which is crucial for maintaining product quality and efficiency. Additionally, AI-driven modeling approaches provide deeper insights into the dynamic behavior of biochemical pathways, offering valuable tools for optimizing production and ensuring regulatory compliance. The current work investigates the application of machine learning/deep learning algorithms in chemical engineering processes, with a particular focus on bio-pharmaceutical processes. Due to tight regulations, pharmaceutical operations are operated within a small range of conditions, resulting in an insufficient amount of faulty data for training a supervised process monitoring model. Furthermore, critical information about faults, such as the type of fault, the time of fault occurrence, and the magnitude of the fault, is often unavailable in bio-processes due to limited availability of informative online sensors. This lack of detailed fault data makes it impractical to train supervised models for fault detection and diagnosis, as these models require comprehensive and accurately labeled datasets to function effectively. Consequently, developing robust and reliable unsupervised models becomes essential for effective process monitoring and fault diagnosis in these scenarios. To this end, this work focuses on unsupervised learning based algorithms that do not require labeled data and instead they identify deviations from normal operating conditions. Within the framework of unsupervised learning the following aspects are tackled: i- An unsupervised PLS-AE (Partial Least Squares-Autoencoder) model is proposed for online batch process monitoring. Detection by this algorithm is based on a novel loss function for training which focuses on maximizing the fault detection rate. This results in more accurate process monitoring models compared to those trained with the classical loss function of minimizing the mean square of reconstruction error. It will be shown that the PLS-AE model trained with the novel loss function and utilizing dynamic upper control limits can surpass other models, including PCA, PLS, and PLS-AE models. ii- Following the development of the PLS-AE model approach this thesis further develops unsupervised deep LSTM-AE (Long Short Term Memory Autoencoder) models which are more suitable for capturing dynamic nonlinear patterns. These models utilize the novel loss function and dynamic upper control limits, enhancing their fault detection capabilities. The effectiveness of these LSTM-AE models is demonstrated through rigorous testing on three case studies: the penicillin batch fermenter simulator, the real-world whooping cough vaccine manufacturing process at Sanofi Toronto, and the lab-scale diphtheria process at Sanofi Toronto. iii- To address the interpretability of deep learning models, this work proposes two novel algorithms for generating contribution plots for $H^2$ and $SPE$ statistics, enabling the identification of the root causes of faults. This advancement significantly improves the interpretability and diagnostic capabilities of autoencoder-based process monitoring models, making them more practical and effective for real-world applications. Furthermore, due to the unavailability of fault information in the real-world Sanofi processes, this work proposes a new metric based on the amount of violation of process monitoring metrics compared to their upper control limits to assess the effectiveness of process monitoring models. The new proposed metric can be utilized to detect low productivity batches (faulty batches) before the end of the fermentation. iv- To address over-fitting, hybrid models are developed for process monitoring and modeling. For the process monitoring task, this work proposes a novel hybrid framework that combines deep LSTM-AE models with exact equations of process controllers. In this framework, manipulated variables are excluded from the input and output layers of the autoencoder. To preserve their data in the process monitoring model, they are reconstructed through the controller equations fed by the reconstructed controlled variables. Removing manipulated variables from the input and output layers of the autoencoder results in a neural network model with fewer parameters to tune compared to non-hybrid autoencoders that include manipulated variables in the input and output layers. The proposed hybrid LSTM-AE models are tested on two case studies: the penicillin batch fermenter simulator and the Tennessee Eastman process under a decentralized control strategy. The proposed hybrid LSTM-AE models not only reduce the size of the model and the risk of overfitting but also achieve higher process monitoring efficacy by utilizing perfect models of controllers. v- To further tackle the problem of over-fitting, this work proposes a novel modeling approach named Metabolic Graph Neural Network (MGNN). This approach imposes a priori knowledge about the metabolic pathways of a microorganism onto the architecture of multi-layer neural network models to represent the dynamic behavior of the cell culture. To capture the nonlinear dynamic behavior of each metabolite, a sub-neural network is considered. The input layer of each sub-neural network model is designed based on the reactions in which the metabolite is involved. This results in a smaller neural network where not all layers and units are connected, thereby reducing the risk of overfitting. The proposed hybrid modeling framework is tested on the metabolic pathway of oxidative stress in Bordetella pertussis bacteria cells. By using the a priori known metabolic network, the proposed MGNN model effectively reduces the overfitting issue compared to a fully connected network that does not use metabolic network knowledge. The MGNN exhibits a superior fit for both training and testing datasets. Additionally, the proposed MGNN is highly interpretable, as it efficiently computes the relevance of each metabolite on any other metabolite by applying gradient computation and back-propagation operations to the neural network. The model is also shown to be useful for fault detection. In summary, by tackling typical aspects of bio-process modelling including lack of measurements, nonlinear process dynamics and data over-fitting, this work advances the application of deep learning algorithms for monitoring of industrial bio-processes.
Mixed-Integer Nonlinear Programming Algorithms Based on Discrete Convex Analysis with Applications to Integrated Decision-Making Problems
(University of Waterloo, 2024-09-24) Linan Romero, David
This PhD thesis presents the development and application of new optimization models and algorithms for the solution of relevant Mixed Integer Nonlinear Programming (MINLP) and Generalized Disjunctive Programming (GDP) optimization problems arising in the fields of process design, simultaneous design and control, and simultaneous scheduling and control. The algorithms proposed throughout this thesis rely on a new optimization paradigm: incorporating concepts from discrete convex analysis (DCA) into the development of efficient MINLP and GDP optimization algorithms, rather than relying solely on conventional or general-purpose MINLP or GDP solvers. The main feature of DCA-based algorithms is that they may trigger a more efficient exploration of the integer and some binary or Boolean variables in the formulation known as ordered discrete decisions, leading to alternative local solutions that may not be attained with traditional local deterministic techniques. The advantages of these algorithms are demonstrated through the solution of a variety of optimization problems relevant in chemical engineering. The first problem considered in this thesis is the rate-based optimal design of catalytic distillation columns. This thesis shows the application of a DCA-based algorithm to address this problem, known as the Discrete-Steepest Descent Algorithm (D-SDA). To the author’s knowledge, this is an optimization problem that has not been attempted to be solved using deterministic MINLP optimization strategies in the past. This thesis also explores the application of the D-SDA as a logic-based solver (LD-SDA) to address GDP problems and introduces a new DCA-based technique called logic-based Discrete-Benders Decomposition (LD-BD). This method was designed following logic-based Benders Decomposition (LBBD) principles, which aim to extend the Benders Decomposition (BD) idea to a broader class of optimization problems, by giving the user freedom on the design of Benders cuts tailored for each application. To the author’s knowledge, LD-BD is the first algorithm that combines DCA theory with LBBD principles. Another variant of the D-SDA proposed in this work is the Discrete-Steepest Descent Algorithm with Variable Bounding (DSDA-VB), which is used as the core method within a hybrid stochastic (i.e., metaheuristic)-deterministic algorithm for optimal design of process flowsheets. Unlike previous studies that propose hybrid deterministic and stochastic algorithms in sequential and nested arrangements, this thesis proposes a parallel configuration to perform the hybridization. This thesis also brings novelty by extending the application of DCA-based optimization tools to the Mixed-Integer Dynamic Optimization (MIDO) domain. The first MIDO problem considered in this work is the optimal design and operation of CD units considering discrete and continuous design and operation variables combined with rigorous non-linear dynamic process models. The problem is solved with an enhanced modular D-SDA framework. The key novelty in this study is that optimal process design and dynamic transitions between different product grades in CD units are simultaneously optimized using a deterministic optimization framework. Another MIDO problem considered in this thesis arises in the field of simultaneous scheduling and control. This work proposes a general discrete-time simultaneous scheduling and dynamic optimization (SSDO) formulation based on the State Task Network (STN) representation. This formulation explicitly considers variable processing times, which is a key aspect in the integration of scheduling and control decisions. The resulting problem is solved using LD-SDA. This thesis also presents the first application of a LBBD technique in the field of SSDO applied to STN processes with a discrete-time scheduling formulation, through the herein proposed Multicut LD-BD (MLD-BD) algorithm. MLD-BD builds upon LD-BD and improves its performance by incorporating multiple cuts per iteration, a pruning strategy, and a cut-off technique. Different case studies show that the herein proposed algorithms explore the feasible region of ordered discrete decisions more efficiently than general purpose MINLP solvers, leading to more profitable solutions in shorter computational times. Overall, this thesis brings advances to the field of MINLP optimization by 1) introducing and demonstrating the advantages of new DCA-based algorithms when solving challenging problems in the fields of optimal design, and 2) extending these algorithms to the field of MIDO optimization, with optimal process integration applications.
Fabrication and Characterization of Nanoparticle Microporous Layers on Platinized Titanium Fiber Felt for Electrolyzer Anodes
(University of Waterloo, 2024-09-20) Jamali, Nooruddin
This study is concerned with the incorporation of various nanoparticles in the microporous layers (MPL) on titanium fiber felts for use at the anode in proton exchange membrane (PEM) water electrolyzers. The nanoparticle MPLs were coated onto Ti fiber felt using various methods. Three types of nanoparticles were utilized: indium tin oxide (ITO), tin (Sn) and titanium (Ti). The ITO and Sn nanoparticles were applied using an electrospraying technique, with Nafion as a binder (in the case of ITO) to ensure adhesion to the felt substrate and polyvinylpyrrolidone (PVP) as a surfactant to prevent nanoparticle sedimentation. This method resulted in uniformly smooth coatings. In contrast, Ti nanoparticles were deposited via a solvent evaporation method without a binder. This was followed by sintering of the nanoparticle-coated Ti felt at 750°C for 1 hour under an argon atmosphere. The resulting MPLs underwent comprehensive characterization, including surface imaging via scanning electron microscopy (SEM), assessments of permeability and porosity and measurements of electrical conductivity. The final and critical phase of characterization involved testing the samples in a laboratory-scale water electrolyzer. The electrolyzer setup included titanium bipolar plates with a once-through 2.1 x 2.1 cm – flow field leading to the membrane electrode assembly with an active area of 0.9 x 2.0 cm. All cells used to characterize performance consisted of a commercial carbon fiber cathode coated with an MPL (SGL 22BB) and a Hydrion N-115 catalyst-coated membrane. The tests revealed that the performance using sintered MPLs was superior to that of the electrosprayed MPLs and surpassed that of the baseline case (Ti felt with no coating). The sintered Ti coating with the lowest loading operated the best indicating that the rougher and thinner MPL was the best choice. The poor performance of the electrosprayed MPLs is attributed to the higher interparticle resistance due to the presence of non-conducting materials (dispersant and binder) as reflected in the lower conductivity of these MPL.
The Effects of Temperature on Lithium-Ion Battery Cells and Packs
(University of Waterloo, 2024-09-19) Mevawalla, Anosh
In the United States, transportation accounts for 28% of total greenhouse gas emissions. Electric vehicles are a significant step toward lowering emissions. Lithium-ion batteries are critical to the commercialization of electric vehicles; nevertheless, batteries are temperature sensitive, and sub-optimal temperatures can cause degradation, loss of power, loss of voltage, and thermal runaway. A lightweight, safe, and effective heat management system improves the vehicle's mileage, speed, safety, and longevity. The necessity for research into the effects of temperature on lithium-ion batteries and battery packs is obvious and important in order to develop electric vehicles that are widely adopted by the public. Models that quickly and accurately forecast the temperature and voltage depending on operational parameters can avoid thermal runaway, increase charging speed, prevent lithium plating, and increase cycle life.
The work consists of a thorough investigation of the temperature effect at both the cell and pack level on various battery parameters such as state of health, internal resistance, capacity and performance. Battery models based on both equivalent circuits and physiochemical models are produced and various battery pack designs are investigated. The effect of temperature on overpotential, current density, capacity and cycle life are also modeled. The writing is divided into four parts:

Part 1:
This section presents mathematical models for quick calculation that can be used in battery management systems (BMS) and battery thermal management systems (BTMS). This paper introduces two distinct models: an internal resistance (Rint) model and a physiological-chemical diffusion/Butler-Volmer-based partial differential 1-D model. The Rint model incorporates a relationship between internal resistance, state of charge (SOC), and C-rate. The investigations use thermocouples on both the battery's surface and tabs. At 4C, the battery temperature rose from 22.00°C to 47.40°C, while the tab temperature went from 22°C to 52.94°C. Simulation results are compared to experimental data at various C-rates (1, 2, 3, and 4C) at 22°C. Simulation findings indicate accurate temperature prediction using a simple Rint model. The reduced physio-chemical model with only three partial differential equations (PDEs) achieves comparable accuracy to the Rint model. The Rint model accurately predicts battery internal resistance using a Pearson curve and hyperbolic sine function, based on current and state of charge.

Part 2:
This section show cases three electrothermal equivalent circuit models with multiple input parameters (SOH, SOC, current, and temperature). The model allows us to estimate parameters like internal impedance using practical inputs, unlike traditional physiochemical models that rely on experimentally unavailable quantities like porosity and tortuosity. The study simulates the internal impedance resistance of a LiFePO4 battery at various ambient temperatures (5, 15, 25, 35, 45 °C), discharge rates (1, 2, 3C), and SOHs (90%, 83%, 65%). The internal impedance surface fit experimental observations with a Pearson coefficient of 0.945. Three thermal models incorporated the internal resistance surface model. The first two thermal models were 0D and did not account for the battery's thermal conductivity. The first model assumed simple heating from internal resistance and convective energy loss, while the second incorporated the Bernardi Equation Reversible heat term. The third model was a 2D model that retained the earlier heat source terms while adding a tab junction heating source term. The 2D model was solved with a basic Euler approach and finite center difference method. The 0D thermal models had R2 values of 0.9964 for simple internal resistance and 0.9962 for reversible heating. The R2 for the 2D thermal model was 0.996.

Part 3:
This paper reported experimental data and model results for a LiFePO4 cell at C-rates of 1C, 2C, 3C, and 4C and at an ambient temperature of approximately 23°C. During the experiment, thermocouples were installed on the battery's surface. Experiments were carried out at continuous current discharge. Temperature increased with C-rates on both the surface and tabs. At 4C, the battery temperature climbed from 22 °C to 47.40 °C, while the tab temperature increased from 22 °C to 52.94°C. Simulation results indicate that the cathode generates more heat than the anode, with electrolyte resistance being the dominant source of heat. Battery temperature was highest near tabs and within the battery’s internal space. The simulation of lithium concentration in the battery revealed that the anode had a more uniform concentration than the cathode. These findings can aid in the precise design and control of Li-ion batteries.

Part 4:
The experimental setup consisted of 7 Panasonic NCA cells connected in parallel, with each cell rated at 3.2Ah capacity. Individual cell capacities were measured and averaged, and the experimentally determined value was 3.11Ah. The arrangement has no BMS, and the batteries were allowed to equilibrate to a steady voltage at the end of discharge. The limiting current of the cells was low, posing less safety issues. The cooling method tested was ambient air cooling, with all trials taking place at an ambient temperature of around 25°C. The battery's thermal behavior was measured at six different discharge rates (constant current): 0.5C, 0.75C, 1C, 1.25C, 1.5C, and 1.75C.
Development and Characterization of an Antimicrobial Coating for Medical Textiles
(University of Waterloo, 2024-09-18) Chen, Ivy
hydrogel antimicrobial e.coli CuS AgNO3 ZnCl bacteria adhesion substrate fabric coating fabric gel
Optimal Strategic Deployment and Novel Biomass Fly-ash Derived Solid Adsorbent Modelling for Carbon Capture Applications
(University of Waterloo, 2024-09-17) Usas, Samantha, Ann
This thesis aims to advance the fight against global warming by developing modelling tools aimed to determine the optimal location of capture plants, and through the development of new process flowsheets that make use of biomass waste as sorbents for CO2 capture and utilization. The first approach explores different strategies of decarbonization through optimal deployment planning and development of alternative carbon capture technologies. Initially a new framework for national optimal deployment of strategic carbon capture implementation is presented. This framework considers external environmental and social considerations often missing from implementation frameworks, which will aid policy makers in more well-rounded deployment decisions. The Canadian case study utilizing the proposed optimal planning strategy shows that implementation of 58 post-combustion carbon capture (PCC) plants located in seven provinces (Alberta, British Columbia, New Brunswick, Nova Scotia, Ontario, Quebec, and Saskatchewan) would result in Canada meeting the national targets. Through a sensitivity analysis on these targets, it was determined that plant distribution is heavily dependent on provincial energy and CO2 transport prices. Additionally, if Alberta were to reduce their GHG emissions by 50% through alternative sustainable methods, only 35 PCC plants would be required to meet national targets. In the second approach, a new modelled system of a biomass-based lithium orthosilicate solid adsorbent derived from industrial biomass fly-ash used to capture CO2 from power plant flue gas emissions is presented. The model includes pre-treatment of biomass fly-ash, the synthesis of adsorbent which utilizes fly-ash as the silicone source and a laboratory produced lithium source, the adsorption of CO2 from flue gas, and regeneration of adsorbent. The study compares the model results from pre-treated and non-pre-treated biomass fly-ash, with benchmark CO2 capture rates of 87% and 89.7%, respectively. Results display a maximum CO2 capture rate of 93.23%. Key insights show an increased CO2 flue gas composition requires a higher adsorbent mass and the most effective flue gas volume to adsorbent mass ratio exists between 3.7 – 4.1. Additionally, higher regeneration temperatures result in improved CO2 capture while pre-treatment of fly-ash does not impact the kinetics of regeneration. Energy analysis show that the pre-treated fly-ash adsorbent is more efficient than the non-pretreated adsorbent, and both could be an improvement over amine-based post-combustion carbon capture with the incorporation of heat integration. However, the cost and water consumption of the pre-treatment process were high compared to that of the industry standard. As such, this model was improved and further examined by incorporating additional wastewater treatment units to recycle used water back to the pre-treatment water washing step and elemental extraction as a waste management tactic for the water treatment waste stream. The enhanced system recycles 85% of the used water and the benchmark results show a 4.2% cost reduction compared to the original process. Additionally, results determine that the cost of resource consumption (CRC) can be reduced by 14.5 % compared to the existing benchmark system. The key insights show that the amount of DI water and acid have the largest impact on process cost and CRC and that both straw and grass show potential as a silicone source for Li4SiO4 synthesis. Furthermore, an analysis on further utilization of CO2 and waste streams through elemental extraction of iron (III) hydroxide and calcium carbonate extraction were considered using different flowsheet configurations. Results from these tests showed that selling captured CO2 and waste streams to cement production is a suitable and sustainable alternative. A cost analysis from this strategy resulted in a 1.35% decrease in process costs from the baseline results and a 1.61% decrease in the CRC from the benchmark results thus promoting a circular carbon economy.
Novel Wide Bandgap Polymer Donors Containing Benzodithiophene and Substituted-Thiophene as Donating and Accepting Units for High Performance Non-Fullerene Acceptor Based Organic Solar Cells
(University of Waterloo, 2024-09-17) Yuan, Yi
Organic solar cells (OSCs), or organic photovoltaics (OPVs), have attracted widespread attention as a promising technology for converting solar energy to electricity owing to their advantages of good mechanical flexibility, lightweight, low-cost and large-area fabrication durability. Over the decades, OSCs have improved rapidly since Heeger’s group reported the first bulk heterojunction (BHJ) OSC which is composed of a blend active layer containing a p-type conjugated polymer as a donor and an n-type small molecule organic semiconductor as an acceptor. Recently, significant progress has been achieved with the highest power conversion efficiency (PCE) over 19%, ascribed to the rational design and matching of the conjugated polymer donors with the novel non-fullerene acceptors (NFAs). By reviewing the remarkable progress achieved for OSCs in the past three decades, it is noteworthy that the benzodithiophene (BDT) and thiophene units showed much potential for constructing polymer donors which showed great photovoltaic performance. However, several challenges remained with further development of OSCs including cost-efficiency. It is important to develop new wide bandgap (WBG) conjugated polymer donor materials to balance material cost and device efficiency for high performance NFA-based solar cell towards commercialization.
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.

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