Mechanical and Mechatronics Engineering

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This is the collection for the University of Waterloo's Department of Mechanical and Mechatronics Engineering.

Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Assessment of the viability of VISR: a mid-wavelength infrared (MWIR) multispectral imaging-based approach for remote flare CE quantification
(University of Waterloo, 2025-05-26) Kaveh, Alireza
In the upstream and downstream petrochemical industry, flaring is a common practice to dispose of the redundant by-products of crude oil extractions, including associated gas and highly reactive volatile organic compounds (HRVOCs), for a variety of regulatory, safety and economic purposes. The current consensus among government regulators and industry experts is that flaring typically occurs at a combustion efficiency (CE) higher than 98%. Recent studies, based on real-world observations and computational simulations, have called this into question. For example, a recent study, based on airborne sampling observations and unlit flare prevalence surveys, reported a mean flare CE of 91%, accounting for both inefficient flaring and unlit flares in the three largest basins in the US. This represents a five-fold increase in fugitive hydrocarbon emissions, primarily comprised of methane, compared to the presumed release rates, highlighting an opportunity for developing robust flare CE monitoring techniques to mitigate the adverse health and environmental impacts of the underappreciated flaring emissions. This study presents a numerical assessment of video imaging spectro-radiometry (VISR), a mid-wavelength infrared (MWIR) multispectral technique, proposed for remote flare combustion efficiency quantification applications. The present analysis utilizes a series of computational fluid dynamics (CFD) simulations of a crosswind steam-assisted industrial flare, with a focus on three aspects: how approximations in the radiometric model impact the local “pixel-wise” CE, the validity of the approach for computing flare global CE using inferred local CE values, and the ability and limitations of VISR instrument to capture fuel that may be aerodynamically stripped from the combustion zone under crosswind conditions. The current assessment is conducted on a simplified version of the VISR instrument model using simulated broadband images generated over spectral bands adjusted for the key absorption features of three main by-products of flare combustion reaction: CO2 (4.2–4.4 µm), CO (4.5–4.9 µm), and CH4 (3.2–3.4 µm). The results highlight the accuracy of the proposed simplified VISR approach in predicting local CE within the VISR region-of-interest (ROI) yet flawed in terms of converting these values into a flare global CE, potentially leading to large biases from the actual flare CE. Ultimately, the VISR technique, due to reliance on mid-wavelength infrared imaging, is inherently incapable of quantifying unburned (cold) methane, allegedly stripped from the flare stack, without participating in the combustion process, due to the presence of a high crosswind over the flare stack, leading to a considerable overestimation of the true flare performance. Keywords: Flares, Combustion Efficiency, Remote Sensing, Verification and Validation, Radiometric Measurements, Uncertainty Analysis, Bayesian Inference.
EV Electrical Systems for Student Teams: An Iterative Design Approach with Practical Lessons
(University of Waterloo, 2025-05-22) McAffee, Anthony
The shift toward vehicle electrification has created an urgent need for engineering students to acquire practical knowledge in electric vehicle (EV) systems. While industry and academia offer theoretical instruction, student design teams often lack practical guidance, resulting in preventable errors, safety risks, and incomplete competition vehicles. This thesis addresses that gap by identifying key lessons to learn for building EV electrical systems and offering real-world insights through a case study of the University of Waterloo Alternative Fuels Team (UWAFT) during the EcoCAR 5 competition. The thesis is structured around three primary electrical domains: High Voltage (HV), Low Voltage (LV), and Serial Communication Systems. Each chapter begins with generalized best practices, safety procedures, and design considerations, then explores how UWAFT applied—or struggled to apply— these concepts in practice. In the HV domain, the thesis covers critical safety mechanisms like Isolation Monitoring Devices (IMDs), Emergency Disconnect Systems (EDS), and Lockout/Tagout (LOTO) procedures. It highlights common student pitfalls such as improper cable terminations and the consequences of electromagnetic interference (EMI). The LV section details harness design, schematic development, wire selection, and assembly techniques essential for powering 12V components safely and reliably. Emphasis is placed on documentation, modularity, and physical protection of circuits. In the Serial Communication chapter, the CAN bus protocol is explained in the context of a complex vehicle network involving autonomous driving, propulsion control, and diagnostics. The harness assembly techniques from the LV chapter are expanded upon for the specific case of CAN cables. Finally, UWAFT’s Serial Network Diagram (SND) serves as a practical guide to organizing and troubleshooting communication systems. Through a combination of foundational theory and applied case study, this thesis equips student teams with a framework for developing safe, functional, and competition-ready EV electrical systems. It also highlights the importance of mentorship, iterative learning, and documentation in engineering education.
Cultural Influences on Human-Robot Interaction: Effects of Robot Appearance and Control Modes
(University of Waterloo, 2025-05-05) Hamdi, Amr
This thesis investigates how cultural differences affect human-robot interaction (HRI), with a focus on physical HRI (pHRI) in collaborative tasks. It presents two empirical studies exploring the influence of cultural background on user perceptions, comfort, and acceptance when interacting with robots. Specifically, three cultural groups were considered: Western, Middle Eastern, and Chinese. The first study examines how the robot’s physical form—humanoid (TALOS) versus non-humanoid (MOVO)—shapes user experiences among participants from the three cultural backgrounds. Results indicate that the TALOS robot tended to be perceived as more comfortable and approachable, especially during independent tasks, possibly due to its human-like features and movements. However, cultural background played a significant role in moderating these perceptions: Western and Chinese participants responded more positively to TALOS, while Middle Eastern participants showed more caution and discomfort. Physiological data, including heart rate and galvanic skin response, indicated higher stress levels during collaborative tasks across all groups, with lower stress levels typically observed when interacting with TALOS. The second study explores how cultural background affects user preferences for robot control modes—autonomous versus human-controlled—and their responses to robot errors. Using the Sawyer collaborative robotic arm, participants performed tasks in both modes (users were deceived in believing the modes were different, while in reality, they were the same), with an intentional error introduced during one of the interactions. Most participants preferred the autonomous mode and reported higher comfort when the robot operated independently. However, Western and Chinese participants generally demonstrated higher trust in autonomous systems, whereas Middle Eastern participants tended to exhibit greater caution, particularly following errors in the human-controlled condition. Interestingly, users were generally more forgiving of mistakes in the human-controlled condition, often attributing errors to the human operator. Physiological responses supported these observations, showing increased stress during error conditions, with Western and Chinese participants recovering more quickly than Middle Eastern participants. Together, these studies highlight the importance of considering cultural background in the design and deployment of robots in several applications, like healthcare, rehabilitation, and industrial automation. This work aims to support incorporating cultural awareness into robotic systems, leading to the future development of more inclusive, trustworthy, and user-friendly technologies.
Order Fulfillment Optimization in Automated Warehouses
(University of Waterloo, 2025-04-30) Suh, Jiwoo
In warehousing, order batching is one of the most popular strategies for optimizing order fulfillment as it groups similar orders into the same batch to optimize picking. The order similarity can be determined based on item locations, availability, and order compositions. The objectives include minimizing travel time, maximizing the number of picked items, and maximizing simultaneous multi-order processing. In this thesis, we study the order fulfillment problem in automated warehouses and propose an order fulfillment heuristic method that to minimize the number of required pick-up sequences to fulfill given order lists by integrating various independent order fulfillment techniques. Three independent algorithms are modified and integrated: (1) FP-Growth-based Association Rule Mining, (2) Order Batching using Similarities Between Orders, and 3) A Hybrid of Public and Personal Item Storage. The resulting heuristic approach is capable of finding optimal solutions when compared to exact results based on Integer Programming. Additionally, a custom-built Python simulation platform is created and run to prove the scalability of the devised algorithm. The Python simulation platform has been further developed into an ROS- and Gazebo-communicable simulation platform for more visualized and intuitive simulation results. Based on the simulation results involving 2000 orders and 1000 items, the algorithm reduced the total number of required pick-up sequences by approximately 50% in comparison to traditional First-Come, First-Served approach.
Model Materials and Reactor Systems for Artificial Leaves in Solar Energy Conversion Applications
(University of Waterloo, 2025-04-28) Chen, Zuolong
Artificial leaves mimic natural photosynthesis processes to convert solar energy into chemical products, including fuels and raw materials. The innovation and utilization of artificial leaves require fundamental studies of materials and reaction mechanisms, along with the development and engineering of scalable reaction platforms. To address these aspects, this study adopts two innovative research approaches for the development of artificial leaves: (1) fundamental studies on materials using model heterostructures and (2) applied design and development of reaction systems. The first two research chapters present research using well-defined, high-quality Cu2O thin film prepared via pulsed laser deposition as model metal oxide heterostructures. The effects of lattice interactions on the intrinsic crystalline and electronic structures of Cu2O are systematically investigated via two strategies: 1. different lattice mismatch configurations on distinct substrates, and 2. different domains on the same substrate. The third research chapter investigates the effects of lattice interactions on the structural evolutions of model heterostructures during solar energy conversion reactions. First, advanced in-situ and operando characterization techniques for thin film model structures are developed. Then, the changes in crystalline and electronic structures are monitored during reactions. Lattice interaction-dependent and crystallographic direction-dependent behaviors were revealed to provide insights into fundamental reaction phenomena and propose design strategies for advanced solar conversion materials. The fourth research chapter studies the design and operation strategies of solar concentrators for photocatalysis and develops a scalable, low-cost photoreactor using quartz wool as the 3D photocatalyst support. A reaction system is constructed as the model platform to study the operational parameters of artificial leaves and propose strategies to build and operate net-energy-positive solar-to-fuel conversion systems. These fundamental and applied studies provide innovative research and development strategies for advancing energy conversion materials and engineering scalable reaction systems. The findings and strategies developed in this research can contribute to the future realization of artificial leaves for solar energy conversions.
Improving Pyrometry of Advanced High Strength Steels During Intercritical Annealing
(University of Waterloo, 2025-04-25) Suleiman, Fatima K
Advanced high strength steels (AHSS) play a prominent role in the automotive industry due to their unique combinations of strength and ductility—essential material properties for manufacturing lightweight vehicles that meet global regulations for fuel economy, vehicle emissions, and passenger safety. Precise thermal control during intercritical annealing is crucial to achieving the mechanical properties of AHSS. Unfortunately, steel manufacturers report unacceptably high rejection rates for AHSS due to substandard mechanical properties. This problem is often attributed to temperature excursions during intercritical annealing of the AHSS, which can be caused by errors in the pyrometrically-inferred temperatures used to control the furnaces. Measuring the temperature of the steel strip through pyrometry requires detailed knowledge of the spectral emissivity of the steel strip, which is imperfectly known since it varies with wavelength, direction, temperature, surface roughness, and oxidation, the latter depending on alloy composition and processing conditions. Therefore, wavelength-dependent variations in the spectral emissivity of AHSS lead to errors in pyrometry measurements during intercritical annealing, which in turn affect the mechanical properties of the steel. In pyrometry, a temperature estimate is typically obtained using spectral irradiance measured from the steel strip in combination with an emissivity compensation algorithm that assumes or prescribes an underlying emissivity relationship based on the number of detection wavelengths. While various emissivity compensation approaches have been developed for mitigating pyrometer errors caused by uncertain spectral emissivity, none have been specifically designed for AHSS. Hence, these methods do not adequately capture how the evolving surface state of annealing AHSS affects the spectral emissivity and thus the resulting pyrometrically-inferred measurements. Further, existing pyrometry algorithms only provide a point estimate of the surface temperature, making it impossible to assess the reliability of the estimates. To address these challenges, this dissertation aims to develop robust pyrometry methods for AHSS that provide accurate and precise temperature estimates with its associated uncertainties. This thesis starts by presenting an empirical approach for modelling the spectral emissivity of advanced high strength steel based on response surface methodology (RSM). Using ex situ measurements on annealed dual-phase AHSS samples (DP980), variation in the spectral emissivity with respect to dew point, alloy composition, pre-annealed surface state, and wavelength is analyzed using full factorial designs. The significant main and interaction effects were found to vary across the spectral range, with the ratio of alloy components and pre-annealed surface state dominating at shorter and longer wavelengths, respectively. Furthermore, the factorial design of experiments was used to develop a novel multivariate emissivity model capturing the effects of dew point, alloy composition, pre-annealed surface state, and wavelength on the emissivity of dual-phase AHSS. To extend the investigation to the high-temperature spectral emissivity variations during intercritical annealing, a laboratory-scale annealing simulator representative of the industrial furnace conditions was designed and fabricated to enable in situ pyrometry and emissivity measurements on AHSS samples. With lab-scale experiments that simulate the annealing and temperature control process of an industrial continuous galvanizing line (CGL), the in situ effect of process parameters, such as chemical composition, annealing temperature, and atmosphere dew point, on the radiative properties of AHSS was observed and modelled. Using in situ measurements from DP980 alloys heated in the annealing simulator, the suitability of existing pyrometry models for accurately predicting the surface temperature of AHSS was examined. By comparing the performance of several dual-wavelength (ratio) and multi-wavelength pyrometry algorithms, it was observed that both methods over-predict the surface temperature; however, the predictions of the multi-wavelength algorithm were generally superior. Given that the spectral emissivity varies significantly with the surface properties of the AHSS coil and further evolves with surface oxidation during annealing, this thesis also investigated the effect of the annealing atmosphere on the radiative properties of dual phase AHSS (DP980) using in situ spectral emissivity measurements from samples annealed within a reducing N2-H2 atmosphere at dew points ranging from −45°C to +10°C, with an annealing schedule similar to that of industrial continuous galvanizing lines (CGLs). The analysis further explored the effect of variations in oxidation kinetics by comparing the radiative properties of DP980 replicates annealed with the same heating schedule and dew point. It was also discovered that low-temperature oxidation of the native oxide of AHSS impacts the evolving spectral emissivity during intercritical annealing. In addition, a comparison of evolving spectral emissivity of dual-phase AHSS alloys against that of a standard EDDS grade IF steel showed that in situ emissivity measurements could potentially identify the formation of mixed/ternary oxides during annealing. Furthermore, this thesis also evaluated the ability of ex situ measurements to capture temperature-dependent variations in emissivity due to electron mobility as described by the Drude model and the Hagen-Rubens theory. The ex situ radiative properties were found to underpredict the in situ spectral emissivity, especially at lower temperatures and at shorter wavelengths. This underprediction was also shown to significantly impact the accuracy of pyrometry estimates at those temperatures. The ex situ measurements were also used to validate the annealing simulator against an industry-standard galvanizing simulator. Finally, with the ultimate objective of improving the robustness of pyrometrically-inferred temperature measurements, this dissertation presents a Bayesian pyrometry methodology in which all pyrometry variables including the measured spectral irradiance, spectral emissivity and inferred-temperature are expressed as random variables that obey probability density functions. Additional information about the spectral emissivity from ex situ characterization were first incorporated into the inference through maximum likelihood priors. The prior and measurement densities were propagated through Bayes’ equation to obtain the posterior densities. The posterior densities provide the pyrometrically-inferred temperature and, crucially, its associated uncertainties. Compared to standard pyrometry methods that provide a point estimate of surface temperature, the Bayesian framework infers the measurement uncertainty via the posterior probability density, which will allow galvanizers to better assess the reliability of the pyrometrically-inferred temperature. The credibility interval of the temperature posterior was further narrowed by defining a multivariate in situ emissivity prior conditioned on the annealing dew point and the equivalent blackbody temperature. Overall, by investigating the behaviour of the spectral emissivity of AHSS during processing, specifically how it evolves with material properties and processing parameters, this thesis presents a comprehensive empirical approach to developing emissivity compensation algorithms that improves the accuracy and reliability of pyrometric temperature predictions on AHSS during annealing.
Development of Microwave-Microfluidic Sensors for Microplastic Detection in Environmental Samples
(University of Waterloo, 2025-04-25) ShafieiDarabi, SeyyedMohammadreza
Microplastics (MPs), plastic particles smaller than 5~mm, are emerging as a significant environmental threat due to their widespread presence in ecosystems and potential health impacts. They originate from both primary sources, such as microbeads in personal care products, and secondary sources, like the degradation of larger plastics. MPs can accumulate in aquatic life, pose risks to food chains, and carry toxic pollutants. Despite their environmental significance, detecting MPs in natural settings is challenging due to complex particle characteristics and the limitations of current detection methods. Several well-established methods have been developed for detecting and monitoring MPs in aqueous samples. Fourier-transform infrared and Raman spectroscopy are among the most widely used techniques due to their unique ability to identify chemical compositions at the molecular level. However, these methods generally require bulky, expensive equipment and skilled personnel. Additionally, they are offline techniques that involve time-consuming and labor-intensive sampling processes. As a result, there is growing demand for affordable and user-friendly MP sensing techniques suitable for on-site applications. Electrical sensing methods-including resonance microwave spectroscopy, dielectric spectroscopy, high-frequency impedance spectroscopy, and electrical impedance spectroscopy-offer unique advantages for on-site detection of MPs due to their compact detection systems and scalability for multi-location testing. Each method interacts differently with the electrical properties of the material, offering diverse capabilities for MP detection. Although most electrical sensing methods share similar working principles, resonance microwave spectroscopy stands out as a promising solution due to its broader frequency range (typically 0.1-100 GHz), enabling more versatile and precise detection of various particle types. Microwave sensing differentiates materials based on their permittivity, making it highly sensitive for detecting MPs, which typically exhibit permittivity values (∼2-3.5) distinct from their natural surrounding materials, such as water (∼80), wet sediments (∼10-30), and blood (∼50-60). Furthermore, microwave sensors can be integrated with planar technologies, such as printed circuit boards (PCBs) and microstrip antennas, to create compact, lightweight, durable, and cost-effective systems, offering a practical solution for continuous measurements in real-world applications. This thesis presents the development of microwave-microfluidic sensors for detecting and characterizing MPs in aqueous environmental samples, offering a scalable and cost-effective solution for real-time monitoring. It starts with an exploratory study capable of only concentration monitoring and progresses to an enhanced sensing platform capable of monitoring both size and concentration. Then, continuous flow is added to the sensing platform to enable single-particle monitoring, which leads to MP size, type, and concentration characterization. In the final stage, the application of the microwave-microfluidic sensor extends from environmental to biomedical contexts. The thesis begins by exploring the integration of microwave sensing with microfluidic platforms for detecting MPs in water. Experimental investigations were conducted using polyethylene microspheres of two different sizes (20 μm and 70 μm). The results indicate that the resonance frequency shift depends on particle size, concentration, and temperature. While experimental trends largely align with numerical simulations, the observed shifts were less pronounced than predicted, and the detection limits were higher than MP concentrations typically encountered in freshwater environments. These findings highlight the need for improved sensitivity and expanded applicability. Building on this, a sensitivity-enhanced microwave sensing platform was introduced using coupled planar microwave resonators to characterize both the size and concentration of MPs in real time. The design incorporates an interdigital capacitor (IDC) structure with a traditional split-ring resonator (SRR) to enhance sensitivity. A disposable sample holder enables multiplex testing without cross-contamination, making the system field-deployable. The sensor was optimized through simulation and validated experimentally with MPs of three sizes (20 μm, 70 μm, and 275 μm) at various concentrations (100k, 1000k, and 10,000k particles/L). The results confirmed the sensor's ability to monitor particle size and concentration accurately. However, since experiments were conducted with fixed sample volumes in the microliter scale, continuous flow integration was needed to improve statistical robustness. The next project introduces an innovative AI-powered microwave-microfluidic platform that enables comprehensive analysis of MPs. The system analyzes particle size, concentration, and type using a K-nearest neighbors (KNN) algorithm trained on raw sensor data. Environmental samples are prefiltered into specific size ranges, and single-particle detection enables precise quantification of MP concentration. This approach offers a scalable solution for real-time monitoring of MP contamination across a wide size range (20-300 μm). The final project demonstrates the potential of the microwave-microfluidic sensor in biomedical diagnostics. The platform was adapted to detect biomarkers in biological samples, focusing on monitoring amylase levels in postoperative peritoneal drainage fluid as an indicator of anastomotic leakage. This work highlights the broader utility of the sensor system for cost-effective, non-invasive real-time monitoring in both environmental and clinical settings.
Novel Machine Learning-Driven Platforms for In-Situ Prediction of Vertical and Top Surface Roughness in Laser Powder-Bed Fusion
(University of Waterloo, 2025-04-23) Toorandaz Kenari, Sahar
Controlling and optimizing surface roughness remains a significant challenge in Laser Powder Bed Fusion (LPBF), as roughness profoundly influences fatigue life, mechanical performance, and post-processing (e.g., machining) costs. While in-situ monitoring has emerged as a key approach for real-time defect detection, predicting surface roughness, particularly for both top and vertical surfaces of parts being printed, remains underexplored. Existing studies predominantly rely on camera-based methods, which usually suffer from the limitations such as lack of viewability of vertical surfaces covered by loose powder particles in LPBF, sensitivity to ambient light, resolution constraints, and the need for additional optical equipment. This research pioneers a novel photodiode-based in-situ monitoring framework integrated with machine learning (ML) algorithms to predict surface roughness in real-time for both top and vertical surfaces of LPBF-printed parts. For top surface roughness prediction, the methodology involves capturing light intensity signals from the melt pool using an on-axial photodiode, incorporating additional process parameters, and training multiple ML models to predict surface roughness at a fine spatial resolution (690 µm × 510 µm), including edges and corners. The framework is rigorously evaluated across a wide range of roughness values, demonstrating its robustness in adapting to process parameter variations. For vertical surface roughness prediction, this study introduces the first-ever in-situ framework using photodiode signals to overcome challenges posed by loose powder coverage, which obstructs conventional sensing techniques. Key time-domain and frequency-domain features are extracted from photodiode signals captured near vertical surfaces and combined with essential process parameters to train ML models. Among the five ML models evaluated, Random Forest (RF) and eXtreme Gradient Boosting (XGB) demonstrated the highest accuracy and lowest error rates. Incorporating in-situ data significantly improved RF’s performance, increasing R² from 0.35 (using process parameters alone) to 0.78, confirming the effectiveness of this approach. This research introduces an innovative pathway for real-time surface roughness prediction in LPBF, enabling enhanced quality assurance, process optimization, and defect mitigation. The integration of photodiode signals with advanced ML algorithms enables precise, on-the-fly assessment of both top and vertical surfaces, enhancing the ability to detect and address irregularities as they occur. By addressing the limitations of traditional camera-based methods, this photodiode-ML framework provides a fast, adaptive, and scalable solution for real-time surface monitoring, paving the way for more advanced quality control strategies, and promising greater reliability and consistency in the production of high-performance components.
Laser powder bed fusion of Cu alloys: from process parameter optimization to oxidation analysis
(University of Waterloo, 2025-04-23) Azizi, Nadia
This dissertation investigates two Cu-based alloy systems, Cu–Cr–Zr and Cu–Ag, with distinct research focuses for each alloy. The first part focuses on Cu–Cr–Zr, aiming to optimize laser powder bed fusion (LPBF) processing conditions and evaluate mechanical performances of the alloy under high strain rate loading. The second part focuses on Cu–Ag, with the goal of understanding its oxidation behavior at high temperatures, particularly when processed by LPBF. Through a systematic approach, this work establishes direct links between processing parameters, microstructural evolution, and functional properties, providing critical insights into tailoring these alloys for advanced structural and functional applications. In the first part, a comprehensive multi-stage statistical optimization is applied to identify the optimal LPBF process parameters to maximize relative density and minimize surface roughness of Cu–Cr–Zr. A Plackett-Burman design (PBD) is first employed to screen 23 process parameters and identify the most influential factors. The key parameters, including laser power, scanning speed, hatch spacing, and layer thickness, are then fine-tuned using a central composite design (CCD) to develop predictive models for both relative density and surface roughness. The optimized process achieves near-full densification, with relative density exceeding 99.5％ and surface roughness below 15 μm. To evaluate dynamic mechanical behavior, the optimized Cu–Cr–Zr samples are subjected to high strain rate loading using a split Hopkinson pressure bar (SHPB), with strain rates ranging from 4400 1/s to 11300 1/s. The alloy demonstrates significant strain hardening, followed by thermal softening and adiabatic shear band (ASB) formation. At the highest strain rate of 11300 1/s, the flow stress exceeds 450 MPa, while the alloy maintains considerable strain accommodation due to the dynamic activation of slip systems and localized deformation within ASBs. In the second part, the feasibility of in-situ alloying of Cu–Ag using LPBF is systematically investigated. Single-track experiments show that increasing scanning speed reduces melt pool size, limiting Ag dissolution, while higher laser power promotes homogeneity at the cost of increased keyhole porosity. Through process optimization, the study achieves a high relative density exceeding 99.2％, along with a uniform distribution of Ag throughout the matrix. The high-temperature oxidation behavior of optimized thin-walled and triply periodic minimal surface (TPMS) Cu–Ag components is then examined across temperatures ranging from 300 ℃ to 800 ℃. Thermogravimetric analysis (TGA) reveals a clear transition from sub-parabolic to parabolic oxidation behavior as temperature increases. At lower temperatures (300 ℃ to 600 ℃), Cu–2Ag initially oxidizes faster than pure Cu; however, its oxidation rate decreases significantly over time, ultimately resulting in lower total mass gain than pure Cu. At higher temperatures (700 ℃ to 800 ℃), Cu–2Ag exhibits superior oxidation resistance from the outset, with a slower and more stable oxidation rate throughout the exposure period. The presence of Ag shifts the oxidation mechanism toward a more protective parabolic regime, indicating the formation of a stable, adherent, and refined oxide scale. Together, these findings confirm the viability of in-situ alloying via LPBF and highlight the potential of Cu–Ag alloys for high-performance applications, particularly in environments where thermal stability and oxidation resistance are essential.
Aerosol Jet Printing of Alumina Insulation Layer for High-Temperature Sensing Applications
(University of Waterloo, 2025-04-23) Miclaus, Alex-George
Aerosol Jet Printing (AJP) falls under the material jetting class of additive manufacturing technology. AJP employs an aerosolized stream of particles to deposit material and create precise patterns onto a variety of planar and non-planar surfaces. AJP offers several distinct advantages, including a relatively high degree of material flexibility, high micro-scale precision, and an ability to print onto virtually any exposed surface. This versatility enables diverse fabrication of a variety of micro-scale electronic components from capacitors, traces, resistors, sensors and so on. In this AJP is used to develop an electrically insulative thin-film capable of functioning at high temperatures, addressing a critical need in high performance sensing devices. A key focus of the research is the selection and development of an electrically insulative tailored for high-temperature sensing applications. Aluminium-oxide (Al2O3) was selected due to its ability to maintain reasonably high resistivity, essential for effective insulation. To formulate an ink for AJP, two synthesis pathways were explored, yielding a stable and printable ink. To enhance the ink’s properties in terms of viscosity, adhesion, and thermal stability, several additives were explored, including special particles and other chemical co-solvents. It was noted that environmental conditions had a significant effect on determining the overall final outcome of the deposited ink. The ink composition and AJP printing parameters were systematically optimized maximize printability. Electrical and mechanical testing was conducted at both room temperature and high temperatures. At room temperature, it was found that with the optimized print parameters, the insulation met the minimum requirement. High-temperature testing, however, faced challenges due to limitations in test setup, resulting in a limited dataset with mixed but promising outcomes. Further investigations are needed to confirm high temperature resistance. Mechanically, the deposited insulation met strain requirements with results further improved by refining the ink formulation and machine settings. This thesis lays a foundation for future development of robust, high-temperature insulative coatings printed via AJP.
Reinforcement Learning Based Motion Planner and Trajectory Tracker for Unmanned Aerial Systems
(University of Waterloo, 2025-04-23) Garg, Shaswat
Unmanned aerial vehicles (UAVs) are increasingly used for autonomous tasks such as inspection, maintenance, and search-and-rescue. However, effective trajectory tracking and obstacle avoidance in dynamic environments remain challenging. Traditional optimization-based methods lack adaptability and computational efficiency, motivating the use of reinforcement learning (RL) for UAV control. This thesis explores RL-based UAV trajectory tracking through three key phases: benchmarking RL algorithms, developing a dual-agent RL framework, and leveraging these insights to design a model-free RL approach for aerial continuum manipulators (ACMs). First, off-policy RL algorithms including DDPG, TD3, SAC, and SoftQ, are benchmarked to evaluate their generalization from simulation to real-world UAV path planning. Results showed DDPG excelling in reward maximization while TD3 provided superior collision avoidance. These insights guided the development of a dual-agent RL framework for UAV trajectory tracking in cluttered environments. The system used two RL agents: one for velocity prediction and another for real-time collision avoidance, leveraging 3D point cloud data to eliminate memory-intensive obstacle representations. Simulated and real-world experiments demonstrated improved trajectory tracking, obstacle avoidance, and adaptability over single-agent and optimization-based approaches. Building on this, a model-free RL framework for ACMs is introduced, which integrate UAV mobility with continuum robotic arms for dexterous aerial manipulation. Traditional RL struggles with constraint enforcement, leading to unsafe behaviors. To address this, a health-driven RL architecture is proposed that implicitly incorporates constraints through a secondary health reward, ensuring safe and stable operation. Using 3D point cloud data for navigation and a curriculum learning paradigm for scalability, the framework demonstrated superior performance over state-of-the-art RL and optimization techniques in trajectory tracking and constraint adherence. This thesis advances RL-based aerial control through benchmarking, dual-agent learning, and safe RL integration for complex aerial systems. The findings lay the groundwork for future research in refining metrics, exploring additional algorithms, and incorporating vision-based RL for enhanced perception and decision-making.
Characterization of Online Graphene Oxide and Reduced Graphene Oxide using optical diagnostics
(University of Waterloo, 2025-04-21) Looi, Horace
Graphene is a promising nanomaterial due to its high electron mobility, large specific surface area, high thermal conductivity, high tensile strength, and flexibility. In electrical applications, pristine single-layer graphene (PG) offers superior properties compared to conventional materials in semiconductors, supercapacitors, and batteries. However, since PG is difficult to manufacture at an industrial scale, there is a need for a graphene-like material that is amenable to high-yield production. A promising candidate is the thermal reduction of graphene oxide (GO) into reduced graphene oxide (rGO), which allows for high throughput of material while minimizing human intervention. However, the properties of rGO are heavily dependent on the quality of incoming GO and the resulting morphology and composition of the rGO. Therefore, a method to measure the GO and rGO properties in real time is needed. Time-resolved laser-induced incandescence (TiRe-LII) and line-of-sight attenuation (LOSA) are two absorption-based measurements that are commonly used to characterize soot and other nanomaterials in real time. Consequently, these methods are thought to have high potential in characterizing GO and rGO. In this work, ex situ methods were used to provide morphological and optical characteristics of GO. Additionally, TiRe-LII and LOSA testing were performed to assess their capabilities in measuring GO and rGO in real-time. The results show that TiRe-LII is capable of providing the relative specific surface area (SSA) of rGO, with accurate SSA trends and slight deviations in absolute value compared to ex situ testing. However, GO showed no incandescent signals due to its low absorption characteristics.LOSA testing showed that, by applying the Lorentz-Drude model, the electrical conductivity and degree of reduction of GO and rGO could be derived. The results indicated that the derived electrical conductivity matched expected trends and were similar in magnitude to literature results. This study found that TiRe-LII can be used to derive the SSA of rGO, and LOSA can be used to derive the electrical conductivity of GO and rGO, both in real time.
A Characterization Scheme to Assess the Heating Performance of Developed Biological Solders for Laser Tissue Welding
(University of Waterloo, 2025-04-21) Huynh, Michelle
Laser tissue welding (LTW) is an alternative, suture-less wound closure technique. It involves positioning opposite wound edges in close proximity, followed by near-infrared laser irradiation until a temperature of approximately 60°C is reached. At this temperature, protein interdigitation and tissue coagulation occur, resulting in a re-homogenized continuous tissue matrix. Heat localization at the site of joining is essential to produce strong tissue bonds while minimizing thermal damage to the surrounding region. One method to achieve heat localization is through the administration of photothermally responsive biological solders (biosolders). For the presented work, nanocomposite gels (NCGs) were developed as biosolders. They were prepared by dissolving hyaluronic acid (HA) and guar gum (GG) to a final polymeric concentration of 3% w/v in aqueous solutions with up to 1.4 nM gold nanorods (GNRs). The addition of GG was found to stabilize GNR dispersion through the gel and enhance their heat generation under laser irradiation. To assess the clinical relevance of the gel formulations, a simplified photothermal characterization scheme was developed. The method relies on a custom-built measurement system that can confine gel samples to clinically relevant thicknesses as thin as 100 μm. This setup was used to measure temperature increases (ΔT) of gel specimens with and without GNRs, with a repeatability of ΔT = 0.123°C. A plasmonic heat amplification factor, ξ , is proposed as a new safety metric for assessing the clinical relevance of a biosolder formulation. It is defined as the ratio of ΔT for an NCG by ΔT of its corresponding control gel. A subset of the developed NCG formulations were found to possess ξ >1.3, indicating their thermal suitability for safe LTW. The presented method aims to establish a new standard foundation for the temperature monitoring of laser-irradiated gels in isolation, allowing for the cost-effective screening of preliminary NCG formulations for eventual administration to tissue models. The developed photothermal characterization system was further used to determine the photothermal conversion efficiency, or fraction of heating power per absorbed light, η, of developed NCGs. Although a standardized method for determining η in liquid photothermal solutions has been previously proposed, it is unsuitable for semisolid materials such as viscous gels that are considered for medical applications like LTW. As such, a simple, yet robust approach is proposed for estimating η of viscous photothermal gels via the direct determination of thermal conductance. The method allows for more confident reports of η for optimizing the photothermal response of photoresponsive materials.
Process Parameter Optimization for Crack Mitigation in CM247LC processed by Electron Beam Powder Bed Fusion
(University of Waterloo, 2025-04-21) Soo, Sebastian
Powder bed fusion is a class of additive manufacturing (AM) technology capable of fabricating complex geometrical designs and empowering users with a variety of benefits. Two main examples are laser powder bed fusion (LPBF) and electron beam powder bed fusion (EB-PBF) known as electron beam melting (EBM). Both techniques follow a layer-by-layer fabrication manner whereby each layer is melted according to individual cross-sectional slices of a computer aided design model. Despite its escalating enticement and prominence in both research and industry, the application requires significant research to understand the interplay between process parameters and materials. An example of a group of alloys are nickel superalloys, which can be further classified as weldable and non-weldable nickel superalloys. CM247LC falls in the latter and is associated with a higher volume content of γ' precipitation strengthening phase. The greater γ' (gamma prime) content enhances the mechanical properties for high temperature applications, but also paradoxically contributes to its proclivity for cracking. The processability of CM247LC via LPBF has been investigated by different researchers from academia and industry. A number of traditional and creative strategies were explored to minimize the crack susceptibility of CM247LC but this was at the cost of undesired compromises. Conversely, the differences in the deposition process and conditions primes the less explored EBM to be an appealing processing alternative. The current work explores the processability of CM247LC by EBM with the desired target of mitigation the material susceptibility towards cracking. The process optimization requires a fundamental understanding of process parameters and the solidification process of the deposited material. This study follows a systematic ground up approach to address the knowledge gap. This started with the basic deposition unit of a single-track and later progressed with multi-tracks. An experimental single-track study, structured within a design of experiment (DoE) framework, was conducted to isolate the primary EBM process parameters and evaluate their effects on the track stability, microstructure, and cracking behaviour. A process map was developed to identify parameter combinations that yielded coherent and uniform tracks. Relationships among EBM parameters, melt pool morphology, grain microstructure, and cracking tendencies were established, revealing specific track conditions and inherent characteristics that mitigate cracking. The lack of an integrated characterization system within the build chamber complicates direct analysis of solidification conditions for these single-tracks. To address this, a simplified thermal model by finite element (FEM) method was developed to provide an alternate approach to study the solidification conditions. The thermal profiles for each experimental single-track were simulated, enabling extraction of key solidification parameters, such as thermal gradient, and cooling rate, and analysis of their spatial-temporal evolution. Furthermore, the relationship between process parameters, thermal conditions, and cracking behavior were defined, identifying certain thermal profiles that reduce cracking severity. Building on this foundation, the study progresses to single-layer deposition involving multiple tracks extending the single-track findings. Two distinct single-track process parameter recipes were selected to assess heat accumulation effects originating from the lateral stacking of tracks. The lateral arrangement was modulated using independent variables: line offset and track number. The impacts of these variables on deposited layer quality, multi-track microstructure, and cracking behaviour were assessed. The different single-track parameter recipes indicated contrasting multi-track deposition nature as reflected by surface topography analysis and subsequent assessment of the multi-track cross section. Moreover, a distinct spatial pattern of defects was observed. While these established relationships and findings point toward optimal multi-track conditions, further refinement and follow-up strategies are necessary to fully optimize the process.
Numerical-Based Thermal Analysis of Proton Exchange Membrane Fuel Cells
(University of Waterloo, 2025-04-09) Kaur, Navpreet
With the urgent global concerns of climate change, governments, industry and researchers all now place a top priority on the shift to more sustainable energy technology. Fuel cells have emerged as an alternative to conventional sources of power, due to their ability to generate electricity with high efficiency and low emissions. Fuel cells are electrochemical devices that directly convert the chemical energy from fuels like hydrogen into electrical energy with high conversion efficiencies. Among various fuel cells, proton exchange membrane fuel cells (PEMFCs) have gained popularity and one of the important aspects of PEMFC is to maintain ideal operating conditions, particularly temperature for its effective operation. A numerical simulation has been conducted to analyze the temperature distribution within rectangular cooling plates with active area of 4437mm2. The performance of channels with varying width of channels, rib and header is evaluated in terms of temperature uniformity index, temperature difference and pressure drop for 15 different cases. The results show that the geometrical variations of the cooling plate play a crucial role in maintaining a uniform temperature. It was observed that the temperature uniformity index and the temperature difference decreased by approximately 30% for a higher channel-to-rib (CR) ratio compared to the base case with a smaller CR ratio. Additionally, the pressure drop for a higher CR ratio was lower than that of the other cases. To further improve the temperature distribution, design modifications were made by introducing pins at various locations in the header's inlet and outlet for 9 different cases. Although the impact of the pins was minimal due to the relatively small header size, the maximum temperature recorded was still lower than that of the baseline cases. Consequently, improved cooling performance can be effectively achieved by varying the channel geometry across the plate in parallel straight channels and through further design adjustments, such as strategically incorporating pins.
Development of Novel Surface Finishing Processes for Additively Manufactured Metal Parts
(University of Waterloo, 2025-03-31) Sun, Manyou
Poor surface quality is one of the drawbacks of metal parts made by various additive manufacturing (AM) processes. They normally possess high surface roughness and different types of surface irregularities. Post-processing operations are needed to reduce the surface roughness to have ready-to-use parts. Among all the surface treatment techniques, electrochemical surface finishing methods have the highest finishing efficiency. However, there are challenges with electropolishing in terms of reducing surface roughness of metals parts made via AM. Firstly, parts made with AM have both small-scale surface roughness and large-scale surface waviness. Electropolishing is only suitable for the reduction of micro-scale surface roughness while it is difficult to use the method to remove meso- to macro-scale surface waviness. In addition, it is still challenging to use electropolishing to reduce the surface roughness of internal channels of additively manufactured parts, benefiting from the promising feature of AM to produce parts with complex internal geometries. Finally, how to improve process sustainability is another question that needs to be addressed, since hazardous and corrosive chemicals are always used for the technique. To address the aforementioned problems, novel approaches were developed, incorporating both modeling and experimental investigations. Analytical and numerical models were constructed to explore the mechanisms of electropolishing and to understand the surface evolution during the process. The results offer valuable insights that can guide the design of experiments and foster the development of novel processes. The first experimental study focuses on using hybrid surface finishing technique to reduce meso-/macro- surface waviness. A novel surface finishing technique combining electrochemical polishing, ultrasonic cavitation and abrasive finishing was designed. Experiments were conducted on both electropolishing and hybrid finishing and the results were compared. While similar optimal arithmetic mean height values (Sa ≈ 1 μm) are achieved for both processes, the arithmetic mean waviness values (Wa) obtained from hybrid finishing are much less than those from sole electropolishing after the same processing time. The second experimental investigation aims at electropolishing internal channels. For doing this, a novel cathode tool was invented and fabricated using polymer 3D printing. Electropolishing was conducted on both straight and curved channels with different curvatures. Preliminary experiments demonstrated a maximum surface roughness Sa reduction, from 10.86 ± 0.50 μm to 1.44 ± 0.46 μm. Apart from this, electropolishing failure mechanisms were explained and design optimization was conducted through numerical simulation. The investigations show that the method is promising in reducing surface roughness of internal channels. In addition, experimental trials were also conducted to improve the sustainability of the surface finishing processes, including using greener electrolytes for electropolishing, and developing shear thickening polishing. Both alcohol-salt electrolyte system and deep eutectic solvent electrolyte were investigated, demonstrating effective surface roughness reduction. Shear thickening polishing using the corn starch slurry was also explored. In spite of some promising results, the process was not repeatable due to numerous influencing factors.
Microstructure control and property enhancement of NiTi-stainless steel dissimilar joints
(University of Waterloo, 2025-03-21) Zhang, Kaiping; Zhou, Y. Norman; Peng, Peng
Dissimilar joining between Nickel-Titanium (NiTi) and stainless steel (SS) is of significance in many areas especially biomedical applications, however, achieving reliable NiTi-SS joints is highly challenging due to the formation of brittle intermetallic compounds (IMCs) in the fusion zone (FZ) or the interface. Two strategies can be summarized to address this issue: (1) restricting the mixing of molten metals and (2) replacing the most harmful Laves (Fe,Cr)2Ti with ductile phases. The former one poses large processing complexity and may lead to NiTi plastic deformation degrading the functional properties. The latter struggles to eliminate brittle IMCs entirely in the FZ and may introduce toxic elements. This research investigated both aspects to control the microstructure and properties of NiTi-SS joints by leveraging the flexibility of laser beam and the thermomechanical process of resistance welding. The combination of laser beam defocus and large offset enabled the laser weld-brazing of NiTi and SS wires. This approach successfully eliminated the IMCs network in the FZ, shifting the conventional and complex FZ brittleness issue to a focus on controlling the brazed interface. Additionally, laser welding mode significantly influenced the macrosegregation and porosity in the FZ of NiTi-SS joints. Low laser power density and long welding time mitigated the macrosegregation and porosity by weakening the laser keyhole effect and prolonging the molten pool duration. In NiTi-SS laser weld FZ, large pores were caused by the instability or collapse of the laser keyhole, while small pores originated from the Ni vaporization. Both IMCs control strategies were investigated in resistance spot welding (RSW) of NiTi and SS for the first time. The use of Nb interlayer resulted in a unique sandwich-structured joint, where two FZs were separated by solid-state Nb, suppressing the mixing of dissimilar molten metals. Nb-containing eutectics formed at both interfaces, enhancing the joint strength with a 38% increase in fracture load and a remarkable 460% increase in energy absorption. In another approach, increasing Ni concentration via a melted Ni interlayer effectively replaced Fe2Ti with relatively ductile Ni3Ti in the FZ. However, high Ni content also induced large pores and cracks, limiting the effectiveness of this strategy in NiTi-SS RSW. A novel processing approach leveraging interfacial liquid control was proposed, achieving a solid-state joined interface in NiTi-SS fusion welding (e.g., resistance microwelding) without any additional interlayers. The produced NiTi-SS joints showed superior strength, superelasticity and corrosion resistance compared to NiTi joints or base metal. The ultrathin reaction layer at the solid-state joined interface contributed to a strong metallurgical bonding, while Joule heating effects and interfacial reactions enhanced superelasticity and corrosion resistance of the joint. Notably, a face-centered-cubic (FCC) reorientated layer (ROL) was found between SS and IMC layer at the controlled ultrathin interface. The formation of this ROL was uncovered based on an epitaxial growth model. This ROL introduced a strong crystallographic mismatch with the textured SS, resulting in the fracture at this interface. These phenomenal findings offer valuable insights for studying material interface and controlling dissimilar-metal welding process.
Influence of Absorbency and Additives on Performance of Battery-Free IoT Water Leak Sensors
(University of Waterloo, 2025-03-19) MacGregor, Oluwadamilola Solomon; Zhou, Norman
Leak detection is a reliable solution for controlling the potentially destructive outflow and wastage of water. Several types of devices are used in domestic and industrial spaces; however, most have their power sources run out, and thus require battery change. The associated costs add to overhead expenditure of the user. This necessitates the use of leak detectors that are self-powered, having no use for external sources of power. Integrating water leak detection systems with Internet of Things (IoT) technology such as Bluetooth low energy (BLE) and long-range (LoRa) protocols provides advantages such as real-time monitoring, which informs incidents and ultimately saves huge cost. The use of IoT-enabled sensors and cloud-based data analytics offers pre-emptive control mechanisms for prompt identification and containment of localized leaks. This helps reduce wastage of water and damage to property, both of which reduce costs as remote access through IoT networks guarantee instant notifications for preventative measures. Scalability fosters effortless deployment in residential, commercial, and industrial environments. In a self-powered IoT water leak device, parameters such as capillary action and electrochemical reactions directly impact power generation and beacon activation. Energy generation and harvesting happen as water interacts with active materials within the sensor device. There must be a cathode and an anode, to interact with the leaking water which would be the electrolyte. Therefore, the materials selected to play such roles in the device are crucial for the desirable chemical interactions, once in contact with the leaking water. In a water leak detector where the most crucial feature is sensitivity to water, capillary action is one of the most significant parameters to consider. Both the design of the sensor casing and channels through which the water travels, are to foster a seamless flow. Also, within the sensor chamber, each material in the stack must demonstrate capillarity. Therefore, porosity is key, as their pore sizes determine what material passes through and what might otherwise be trapped to impede the flow of the water being transmitted. Therefore, capillary action is explored for absorbent materials and the sensor casing. Both filter paper (FP) and fabric materials are examined, to ascertain which one gives optimally combined advantages for absorbency and repeatability. FP showed superior performance, due to its pore size. This advantage becomes particularly useful where additives are considered for the powder mixture. Without additives, the stacked materials have only water to interact with. While this is sufficient to power BLE, it is not enough for LoRa technologies which require higher power. To account for this, additives can be included in the materials within the sensor stack. Salts are among such additives that can provide active ions when interacting with water. Subsequently, these ions facilitate electricity generation due to increased current. Therefore, the power output of the device can be increased when additives are introduced. In previous similar works, it was shown that pure materials without any additives produce an open-circuit voltage (OCV) of 2 V and short-circuit current (SCC) of only 10 mA. This combination was able to power the sensor for beacon activation through 7 cycles of wetting-drying rounds of repeatability, but only for the BLE protocol. To solve for this limitation, NaCl was added in varied proportions. 10 wt.% NaCl was found to outperform other samples. After several rounds of repeatability, the values of current and voltage were observed to diminish. A sensor without NaCl typically lasts 7 rounds of repeatability, sensors containing NaCl last only about 3 rounds. The primary concern with the use of such additives may be an imminent trade-off between the increased power generation and possible corrosion which compromises shelf life. One of the downsides of using additives to enhance power generation is the corrosion of metallic materials in the sensor. To study the effect of NaCl on the corrosion of the metallic material, and thus the shelf life of the sensor, electrochemical corrosion tests were performed. As expected, it was observed that higher salt content resulted in higher corrosion rate. Therefore, repeatability was significantly reduced in higher salt contents, thereby limiting the overall shelf life of the sensor. Ultimately, the use of salts should be limited and be specific to the target use case.
Data-Based Modeling of Electrochemical Energy and Thermal Systems: Fuel Cell and Lithium-Ion Battery
(University of Waterloo, 2025-01-24) Legala, Adithya; Li, Xianguo
As a solution to combat climate change and environmental pollution, electrochemical energy systems such as Proton Exchange Membrane Fuel Cell (PEMFC) and Lithium-Ion Battery (LIB) are being developed as the replacement for fossil fuel-powered combustion engines, especially for ground transportation and aviation applications. These electrochemical energy systems must be able to operate independently and in conjunction with each other by complementing their advantages and limitations, such as efficiency, range, thermal behavior, aging, and operating environment. This interoperability requires accurate real-time computational models to control, diagnose, and adapt according to field requirements. A typical electrochemical energy system model needs to incorporate effects related to reactant concentrations, system overpotentials, thermodynamics, porous media mechanics, membrane dynamics, gas diffusion, electrode degradation, electrolyte status, ion transport, and chemical kinetics across various operating conditions, all of which result in complex interactions affecting the accuracy and reliability of the system. Today, both PEMFC and LIB use complex computational physics-based fluid dynamics models in the product development phase, which requires enormous computational power and long lead times for iterative prototype improvements. On the other hand, both PEMFC and LIB rely on simple lookup tables and semi-empirical equations as plant models that require intensive calibration activity to determine the mode of control and diagnosis for automotive applications. However, considering the present-day automotive propulsion systems, which operate in widely varied applications and geographic locations and have short product development cycles, these approaches are not able to comprehend the complexities, hindering the ability of these systems to operate at their full potential and leading to catastrophic failures (e.g., Thermal runaway). Data-based modeling techniques are one of the potential solutions, which is quite in contrast with other empirical or physics-based models where the entire input-output relations of the model are established primarily based on the data. Data-based models use aspects of statistics, probability, and network architecture, avoiding the complexities of physics-based models and intensive calibration, providing better accuracy in most cases, primarily where the complex mechanisms can’t be modeled using specific governing equations, and fast, efficient computation with much less computational resource requirement. This thesis focuses on data acquisition (identifying and collecting the relevant data) and data-based model development by incorporating machine learning algorithms and regressors to predict the system's performance, thermal behavior, aging, and faults in real-time (on-board diagnostics). Data for these models is acquired through two approaches: experimentation by utilizing Fuel Cell and Green Energy Lab facilities such as the Automated Battery Test Station (ABTS), G20 fuel cell automated test station, and by partnering with the relevant industry. In the second approach, data is generated by simulation of physics-based models (CFD, Semi-empirical, equivalent circuit models) that are experimentally validated in the literature and developed within the research groups of UWaterloo. Development of a data-based model includes the identification of feature vectors (inputs), prediction attributes (outputs), state estimates (internal parameters), non-linearity of the systems, correlation factors of various system entities, and application of machine learning techniques such as feed-forward artificial neural network, support vector machine classifier - regressor, along with their respective adaptations and calibration processes. The primary objectives of this study are to develop data-based models for three main application areas: (i) Prediction of PEMFC performance, internal states of the membrane, cell voltage degradation, and system outputs. (ii) Prediction of LIB heat release rate during discharge and thermal dynamics of an open system during an exothermic reaction. (iii) Prediction of fuel cell battery hybrid electric vehicle’s system dynamics and thermal behavior. During this study, various data-based models were developed to tackle the problems encountered in fuel cell-battery hybrid systems, such as predicting the fuel cell performance, fuel cell voltage degradation, PEMFC membrane dynamics, lithium-ion battery thermal dynamics, thermal behavior during exothermic reactions and dynamics of fuel-cell battery hybrid system. The results presented in this study proved the data-based model’s applicability in surrogate modeling, real-time system monitoring, controls, and diagnostics of electrochemical energy systems both at the component level and system level. Additionally, the results implicate that the data-based model can serve as a complement and alternative to the traditional computational fluid dynamics models as well as complex physics-based and empirical models to predict thermal gradients and system internal states during multifaceted reactions.
Modulation Strategies of Cu-based electrocatalysts for Enhancing Electrocatalytic CO2 Conversion
(University of Waterloo, 2025-01-23) Wang, Lei; Wu, Yimin; Tan, Zhongchao
Electrocatalytic CO2 reduction (ECR) into value-added chemicals and fuels using renewable energy contributes to global decarbonization, offering an elegant solution for achieving carbon neutrality and fostering sustainable development of human society. However, this strategy highly relies on the rational design of catalysts to enhance product selectivity and activity. To advance CO2 conversion technology, systematic and comprehensive studies on ECR are urgently needed to demonstrate the origins of catalytic activity, elucidate the relationship between structural defects of catalysts and catalytic activity, and reveal the dynamic evolution of active sites under ECR reaction conditions. In this thesis, mechanistic studies and functional catalyst design are extended from lab scale to large scale. The regulation of grain boundaries structures and local microenvironments is employed to stabilize oxidized copper species, thereby enhancing the selective production of desired products. Firstly, at the lab scale, we introduce oxidation and alloying strategies into grain boundaries systems. Low-loading Ag and water oxidation induce oxygen enrichment at the grain boundaries, leading to a grain boundary oxidation effect. In situ characterizations indicate that the grain boundaries and grain boundary oxidation effects contribute to strengthening resistance of the oxidative Cuδ+ species to the electrochemical reduction. Experimental and theoretical results demonstrate that in intricate grain boundaries assemblies, the oxidation state of copper plays a crucial role in the C2+ product pathway, while the nanoalloy effect tends to the formation of CH4 product. Secondly, to achieve the industrial-scale ECR to multi-carbon products with high selectivity using membrane electrode assembly (MEA) electrolyzers, we introduce activated carbon black with different functional groups to modulate the interfacial microenvironment of Cu nanoparticles, enhancing CO coverage to suppress hydrogen evolution reaction (HER). In situ multimodal characterizations consistently reveal that in situ generated strongly oxidative hydroxyl radicals can create a locally oxidative microenvironment on the catalyst surface, stabilizing the Cuδ+ species and leading to an irreversible and asynchronous change in morphology and valence, yielding high-curvature nanowhiskers. The well-stabilized Cuδ+-OH species serve as active sites during MEA testing. By comprehending this mechanism, we achieve selective ethylene production with a Faradaic efficiency (FE) of 55.6% for C2H4 at a current density of 316 mA cm-2. The insight of these reaction mechanisms bridges the gap between lab-scale studies and industrial-scale implementation, contributing to the development of sustainable and carbon-neutral industries.

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