Theses

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The theses in UWSpace are publicly accessible unless restricted due to publication or patent pending.

This collection includes a subset of theses submitted by graduates of the University of Waterloo as a partial requirement of a degree program at the Master's or PhD level. It includes all electronically submitted theses. (Electronic submission was optional from 1996 through 2006. Electronic submission became the default submission format in October 2006.)

This collection also includes a subset of UW theses that were scanned through the Theses Canada program. (The subset includes UW PhD theses from 1998 - 2002.)

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Polymeric Gas Sensing Materials for Detection of Toxic Analytes
(University of Waterloo, 2026-07-10) Mavani, Bhoomi
Toxic volatile organic compounds (VOCs), including formaldehyde, are pervasive in both industrial and indoor environments and pose serious health risks at even low chronic exposure levels, yet the tools available for real-time monitoring remain bulky, expensive, and ill-suited for portable or wearable deployment. At its core, this is majorly a materials problem and no potential transducer architecture in a micro gas sensor can compensate enough for a sensing layer that lacks the chemical specificity to distinguish formaldehyde from structurally similar interferents like ethanol, acetone, and benzene. This thesis approaches this materials challenge directly, tracing a systematic path from backbone chemistry through composite engineering and regeneration characterization to device-level integration, with the goal of understanding not just which materials work but why they work and how that understanding can guide rational design of gas sensing polymeric materials for any gas analyte. Four pristine conducting polymer backbones were first screened under single-gas and multi-component gas exposures. Polyaniline (PANI) and poly(2,5-dimethylaniline) (P25DMA) emerged as the most promising candidates. Under quaternary mixture conditions (formaldehyde, ethanol, acetone, and benzene at 2 ppm each), P25DMA was the only pristine material to exceed a mixture selectivity index F/(E+A+B) value of ~1.02 compared to ~0.59 for PANI, attributable to the steric and electronic influence of the dimethyl ring substitution reducing non-selective hydrogen-bonding with competing oxygenates. However, even P25DMA's selectivity advantage has limits and neither backbone alone provides the sensitivity-selectivity combination required for robust real-world deployment. This established the case for composite modification: not as an incremental improvement, but as a necessary step to access adsorption environments that pristine polymer chemistry alone cannot deliver. To further improve performance, metal oxides (In2O3, NiO, SnO2, TiO2) were incorporated into both backbones. The central finding is that metal oxide identity governs the mode of incorporation, which in turn dictates pore architecture and ultimately drives sensitivity and selectivity outcomes. Because APS-initiated polymerization occurs in a strongly acidic medium, each metal oxide's resistance to dissolution during synthesis determines whether it survives as a surface-accessible particle or becomes buried within the polymer matrix. In2O3 and SnO2, being acid-stable, remain particulate and expose hydroxylated, defect-rich surfaces whose polar adsorption sites interact preferentially with formaldehyde's carbonyl group, simultaneously enhancing sensitivity and maintaining selectivity. NiO, being acid-labile, becomes polymer-encased during synthesis and contributes no chemically differentiated surface, instead blocking active amine sites at higher loadings and degrading selectivity precisely where sensitivity appears to peak. This acid stability explains why metal oxide identity matters relatively more than metal oxide loading as a first design variable. Backbone architecture then determines how effectively that oxide can be dispersed: P25DMA's lamellar structure anchors 3.6 times more In2O3 than PANI's fibrillar network under the same recipe, generating a denser polymer-metal oxide interfacial perimeter where selective adsorption events occur. Pore accessibility completes the picture by controlling whether analyte molecules can physically reach those interfacial sites and whether the pore environment favors selective or non-selective uptake. At the optimal 5% In2O3 loading in P25DMA, confinement-dominated transport gives way to open, externally accessible surface where formaldehyde's smaller kinetic diameter and stronger affinity for oxide sites gives it a systematic competitive advantage over interferents, driving sensitivity and selectivity simultaneously. This hierarchy, from metal oxide acid stability to embedding mode to backbone morphology to pore accessibility to competitive adsorption selectivity, is not specific to formaldehyde and provides a transferable framework for designing polymer-oxide composites toward any analyte where backbone-analyte affinity and metal oxide surface chemistry can be deliberately matched. Finally, both pristine P25DMA and P25DMA with 5% In2O3 composite were deposited onto a MEMS resonant mass sensor. Formaldehyde exposure of the functionalized MEMS sensor displayed device-level transduction of polymer sorption, with P25DMA with 5% In2O3 composite exhibiting larger and faster frequency shifts than the pristine P25DMA. This is in agreement with and reinforces the observation of improved sensing performance of the composite material. For a sensing material to be practically deployable, sensitivity and selectivity alone are not enough: it must also recover reliably after exposure and survive repeated use without structural degradation. PANI regenerates through mild thermal assistance up to 80°C enhances desorption kinetics without compromising backbone integrity. Beyond this point, the onset of glass transition induces segmental mobility that shortens analyte residence time and reduces retained analyte in polymer matrix leading to desorption. The post-regeneration characterization confirms the backbone remains chemically intact throughout, establishing that the material can be returned to baseline under mild conditions without sacrificing the chemical properties that drive its sensing performance. Additional parametric studies on synthesis temperature, ageing, sensing layer mass, physical form, and carbon-based fillers collectively confirmed that composition-driven effects from oxide identity and loading are the dominant levers for tuning selectivity, while processing variables impose secondary but measurable influences. Underpinning all of this materials work is a detailed mechanistic and kinetic study of PANI polymerization itself, tracing the initiation, chain growth, and termination steps and developing a numerical kinetic model that predicts monomer conversion and molecular weight evolution, ensuring that the sensing materials produced in this thesis are understood at the level of their synthesis chemistry rather than treated as empirical outputs. Taken together, this thesis demonstrates that the path to high-performance polymer-based formaldehyde sensing runs through a hierarchy of materials decisions, from backbone chemistry through oxide acid stability and embedding mode to pore architecture, and that mechanistic understanding of each step is what enables co-optimization of sensitivity, selectivity, and renderability rather than trading one against another.
Scalable Program Analysis: Abstract Interpretation Techniques and Practical Applications
(University of Waterloo, 2026-07-10) Su, Yusen
Static program analysis aims to infer how a program behaves by reasoning about its source code under a formal semantics, without executing it. It is a core technique for software verification, compiler optimization, and security analysis; key applications include establishing memory safety guarantees, inferring numerical invariants, and detecting unsafe information flows. A central challenge is the precision--scalability trade-off: coarse abstractions scale but generate many false alarms, while highly precise methods often struggle on large codebases. This thesis develops sound and automated static analyses in the context of abstract interpretation, to achieve sufficient precision for formal verification while maintaining scalability on practical verification tasks. The thesis presents three contributions. First, for memory safety verification, which requires inferring invariants to prove the validity of spatial memory accesses (i.e., that accessed buffer offsets remain within the bounds of their allocated buffer), we build a new abstract domain for reasoning about object invariants, where memory objects sharing common properties are abstracted relationally (i.e., summarized into a single abstract representation). However, such summarization causes precision loss when the properties of the summarized objects are temporarily violated during updates. To mitigate this, we introduce a new memory abstraction that separates recently manipulated objects from unchanged objects (i.e., summary objects). The new domain combines simpler subdomains for objects and scalars, so each part stays small and the whole analysis scales; the parts then exchange information through domain reduction to recover the precision that splitting them would otherwise lose. Second, for inferring numerical invariants, we propose a new numerical domain, Template Difference-Bounded Matrices (tDBM), to capture a useful subset of Two Variable Per Inequality (TVPI) constraints needed for bounds checking. By extending the matrix with additional dimensions via ghost variables that represent variables with scaled coefficients, tDBM provides a practical middle ground between lightweight weakly relational domains and expensive fully relational domains. Third, for information-flow security, we present a field-sensitive taint analysis that combines pointer analysis and data-flow analysis, and further integrates abstract-interpretation-based value reasoning to prune infeasible flows and reduce false positives. To improve precision in heap reasoning, we also introduce a refined variant of pointer analysis that better preserves field sensitivity while retaining scalability. Overall, this thesis develops abstract domains tailored to the property being verified and kept lightweight without sacrificing the precision each task needs. Together, they prove memory safety and information-flow security while scaling to real-world programs.
Inverse Design and Lithographic Pattern Transfer of a Thin Near-Infrared All-Silicon Absorber
(University of Waterloo, 2026-07-09) Roy, Lucas
All-silicon photodetectors are seldom used for Near-Infrared (NIR) Light Detection and Ranging (LiDAR) due to the low absorption coefficient of silicon in the NIR wavelength range. The absorptance of the absorption region is typically increased by increasing the depth of the absorption region, however, this approach adds timing jitter to a photode- tector that can severely limit the ranging resolution of the LiDAR system. This work maximized the absorptance of a silicon absorber in a fixed-depth 2.475 μm absorption re- gion from a theoretical baseline value of 2.58% to a simulated value of 8.33% for 950 nm wavelength incident light, a more than 3-fold improvement. The simulated absorptance enhancement compared to the baseline value was 3.2, whereas perfect black silicon only displays an absorptance enhancement of less than 1.5 at the 950 nm wavelength, giving the structure designed and simulated in this work a more than 2-fold absorptance enhancement improvement over perfect black silicon. This work applied topological optimization and inverse design methodologies to gen- erate the unique all-silicon 2 μm by 2 μm meta-atom absorber with vertical sidewalls. A pattern transfer using electron-beam lithography on a planar silicon sample with 340 nm of ZEP520A resist was conducted as a proof of concept that the pattern can be transferred faithfully into a real silicon sample. At the time that this work was conducted, the author was unaware of any such studies that applied topological optimization to maximize the absorptance of a thin all-silicon NIR absorber. This work showed that topological opti- mization can be effectively utilized to automatically design photonic devices where classical device architectures and structures designed by humans have poor performance. This work also proposes an initialization procedure for generating initial parameters for the optimization procedure. The structure optimized in this work was seeded using the initialization procedure showcasing its validity and effectiveness.
Emerging Contaminants in Groundwater and Surface Water at a Mine Reclamation Site: Occurrence, Fate and Remediation
(University of Waterloo, 2026-07-09) Yuan, Yizhi
Mine operations can generate large volumes of sulfide-rich waste rock and tailings. When exposed to atmospheric O2 and water, sulfide minerals can oxidize, producing low-quality drainage that contains high concentrations of dissolved metal(loid)s, sulfate, and acid. To limit the release of these toxic substances and promote landscape revegetation, municipal biosolids are increasingly utilized in mine rehabilitation programs. A multilayer cover system, consisting of a 0.5 m biosolids amended organic carbon (OC) layer and a 2 m desulfurized tailings (DST) layer, was applied on the high-sulfide tailings area in northern Ontario to retain moisture in the underlying tailings at high degrees of saturation, and promote revegetation of the landscape. Water samples from the vadose and saturated zones in the covered tailings system were collected versus depth using discrete sampling, whereas adjacent surface water was monitored using both discrete sampling and two passive samplers (diffusive gradients in thin films (DGT) and polar organic chemical integrative samplers (POCIS)) to assess spatial and temporal variations in major ions, trace elements, pharmaceutical and personal care products (PPCPs), artificial sweeteners, and per- and polyfluoroalkyl substances (PFASs) over a two-year period. Field investigations showed improved geochemical conditions below the cover system. In the vadose zone, the composite cover maintained circumneutral porewater pH through O2 consumption by OC, the enhanced neutralization capacity provided by added CaO, and the restriction of O2 infiltration in the high-moisture content DST layer, which effectively limited sulfide mineral oxidation compared to the acidic conditions (pH 3–4) observed in uncovered areas. While elevated concentrations of SO₄²⁻ and trace metals (e.g., Ni, Co, Mn, Cu, Zn) were observed in the subsurface groundwater due to historical oxidation, lower Fe concentrations beneath the biosolids layer are attributed to precipitation of goethite favoured under the circumneutral pH conditions. Concentrations of major ions and trace elements peaked adjacent to the tailings area (Ca: 152–298 mg L-1; SO42-: 347–745 mg L-1, Fe: 122–715 µg L-1; Ni: 810–1960 µg L-1) then decreased downstream within the alkaline treatment system decreasing critical trace element concentrations to well below regulatory limits (e.g., 6.0–426 µg L⁻¹ for Fe; 15.5–663 µg L⁻¹ for Ni). Porewater and groundwater monitoring indicated leaching of five pharmaceutical and personal care products (PPCPs) (95% within 480 hours), which followed a double first-order in parallel (DFOP) kinetic mechanism. The defluorination process approached 44.7±0.5% at 40 minutes and was accompanied by the formation of various shorter-chained PFASs and possible generation of organic acids (e.g., formic and acetic acid), indicating concurrent decarboxylation and defluorination pathways. Fluoride recovery, as the sum of the total inorganic and organic fluorine, reached a maximum of 67.4±1.5% at 40 min and decreased to 37.6±1.1% at 240 h. Given the near-complete removal of both PFOA and PFOS and low masses of generated short-chained PFASs (C4-C6), it is likely that PFOA and PFOS were transformed into PFBA, PFPeS and/or ultrashort-chain PFASs (C<4) that were not analyzed in the study. Alternatively, geochemical analysis indicated a potential fluoride sink via precipitation of fluorapatite associated with Ca and phosphate release from biochar. These outcomes demonstrate the feasibility of nZnNi-BC as an effective material for PFAS degradation and highlights the importance of geochemical sinks in controlling fluoride mass balance.
Keeping Others at Arm’s Length: Examining the Contribution of Fears of Receiving Compassion to Safety Behaviour Use and Positivity Deficits in Social Anxiety
(University of Waterloo, 2026-07-08) Ho, Jolie Tsoi Kan
For many people, social relationships are a source of valued support, closeness, and positivity, motivating them to seek out and repeatedly enter social situations to fulfill their fundamental need for social connection (e.g., Baumeister & Leary, 1995). However, despite yearning for social connection, individuals with social anxiety disorder (SAD) struggle to capitalize on social opportunities to foster interpersonal closeness, and experience “positivity deficits” that are reflected in their failure to benefit emotionally from social contexts that others find rewarding (Kashdan, 2004; Kashdan, 2011). Rather than viewing social encounters as pleasurable, they may tend to appraise potentially rewarding social contexts as threatening due to intense fears of publicly exposing their self-perceived flaws and inviting judgment and criticism from others (Moscovitch, 2009). While self-portrayal fears have been shown to drive the use of maladaptive safety behaviours in individuals with SAD (Moscovitch et al., 2013), whether and how they may also contribute to positivity deficits remains unclear. Alongside self-portrayal fears, emerging research suggests that people with SAD also experience elevated fears of receiving compassion from others, eliciting feelings of threat or unfamiliarity when others offer support in the form of kindness, warmth, and affection (Gilbert et al., 2011; Merritt & Purdon, 2020). Like self-portrayal concerns, fears of receiving compassion may function to keep others at arm’s length, which may contribute in important ways to safety behaviour use and positivity deficits in SAD. However, the nature of the relationship between fears of receiving compassion, on one hand, and safety behaviour use and positivity deficits, on the other, has not yet been empirically examined. Furthermore, whether deficits in different subtypes of positivity (i.e., activated positive affect vs. soothing/ safe positive affect) exist amongst socially anxious individuals and their predictors remain unknown. To address these critical gaps in the literature, my doctoral dissertation presents a series of three novel, methodologically diverse studies to help us gain new insights into the role of fears of receiving compassion in the behavioural and emotional experiences of individuals with symptoms of social anxiety across social contexts. First, I present the results of a cross-sectional, correlational study in which I analyzed the role of fears of receiving compassion on safety behaviour use in participants with SAD, over and above the well-established contribution of fears of negative self-portrayal (Study 1). Next, I extend these findings to investigate immediate emotional and cognitive responses in the moment of receiving compassion from others: in Study 2, I present results from an online study using an experimental written vignette paradigm to examine the effects of trait social anxiety symptoms, fears of negative self-portrayal, and fears of receiving compassion on responses to imagined compassionate feedback after an imagined shame-based social scenario (i.e., being socially rejected or committing a social blunder). Finally, to replicate and extend these findings to naturalistic social contexts, I employed ecological momentary assessment (EMA) via a smartphone app to examine how momentary safety behaviour use in naturalistic social situations impacts different types of momentary positive affect (i.e., activated positive affect vs. soothing/ safe positive affect), and how relations between momentary safety behaviour use and momentary affect across time are moderated by fears of negative self-portrayal and fears of receiving compassion (Study 3). Primary findings across the three studies demonstrated, first, that trait fears of receiving compassion predicted trait safety behaviour use over and above trait fears of negative self-portrayal. Second, trait fears of receiving compassion also influenced immediate, momentary emotional and cognitive responses to imagining receiving expressions of compassion from others after a socially distressing event. Third, momentary levels of both fears of receiving compassion and negative self-portrayal predicted increased safety behaviour use in naturalistic social interactions, which then predicted increased negative affect and decreased feelings of social safeness. Theoretical and clinical implications of the program of research are discussed, including the role of compassion fears in the conceptualization of safety behaviour use in social anxiety, considerations for improving therapeutic procedures of safety behaviour reduction in CBT protocols, and the importance of targeting specific types of positive affect deficits in treatment of SAD, with an emphasis on devoting special therapeutic attention to social safeness enhancement. I conclude by identifying key questions for future research.
Multiple Motivations Underlying Children’s Affiliative Behaviour: The Functional Role of Affiliative Language
(University of Waterloo, 2026-07-08) English, Sarah
Social connection is one of the most fundamental human needs, shaping behaviour across the entire lifespan. As children develop, language becomes an increasingly central means through which they express their desire to connect with others – their social motivations. Yet relatively little is known about how and why children use language to signal their social motivation, or what individual and contextual factors shape its use. The three studies presented in this dissertation address these gaps by introducing a validated measure of children’s affiliative language use, examining how trait-level individual differences shape communicative patterns across the early stages of a developing peer relationship, and directly testing how experiences of social exclusion influence affiliative language use and the affective processes that may underlie it. Chapter 2 introduces and validates the Child Affiliative Language (CAL) dictionary – a novel, LIWC-based tool for quantifying children’s affiliative language use during naturalistic peer interactions. Affiliative language, as captured by the CAL dictionary, was meaningfully distinct from general talkativeness and broader linguistic ability, but was associated with positive social outcomes including greater peer-reported liking, higher observer-rated social engagement, and increased prosocial sharing. Children also used significantly more affiliative language during an unstructured get-to-know-you task than during a goal-oriented brainstorming task, consistent with the interpretation that the measure captures shifts in social motivation across contexts rather than stable individual differences in communicative style. Chapter 3 uses a longitudinal dyadic approach to examine how temperamental shyness shapes children’s verbal communication across three sessions with the same previously unfamiliar peer. Despite speaking less than their less shy peers, shy children used a greater proportion of affiliative language during their initial interaction, suggesting that when shy children do speak, they are more likely to do so in ways that signal warmth and the desire to connect. Verbal reticence also longitudinally mediated the association between shyness and outside observers’ ratings of social engagement; yet shy children were liked just as much by their own interaction partners, highlighting a meaningful discrepancy between how children’s peer relationships appear to outside observers and how those relationships are experienced. Chapter 4 uses an experimental paradigm to examine how experiences of social exclusion shape affiliative language use in children and adolescents. Excluded youth spoke significantly less than those in the inclusion condition during a subsequent social interaction. Contrary to expectations, social exclusion did not directly predict greater affiliative language use. Age was also unexpectedly negatively associated with affiliative language use, such that older adolescents used less affiliative language than younger children across conditions. Importantly, individual differences in perceived physiological reactivity to the exclusion task uniquely predicted greater affiliative language use, suggesting that it may be the subjective affective experience of social stress, rather than the objective experience of exclusion itself, that shapes how youth use language to signal their social motivations. Together, these findings suggest that affiliative language is a sensitive, dynamic index of children’s social motivation – one shaped by both stable individual differences and the social situations that children find themselves in. The results further suggest that affiliative language may serve not only an interpersonal function, but an intrapersonal, regulatory function as well. These studies lay the groundwork for a broader program of research into the motivated, context-sensitive, and multiply determined nature of children’s social communication, and offers a new lens through which children’s social development can be understood and supported.
Parallel Efficient Secure DBSCAN Approximation
(University of Waterloo, 2026-07-07) Shehata, Mohannad
Machine learning has permeated every part of our data lives. With the prevalence of machine learning comes an insatiable need for data, including sensitive personal data. As a result, the need arose to develop techniques for machine learning tasks that preserve individual privacy while providing high utility by learning from private data somehow. An important class of machine learning tasks is clustering, which can potentially be used to study diseases by identifying clusters of patients. As patient information is private, private clustering algorithms would help us infer patterns among patients while protecting their data. DBSCAN is a clustering algorithm that is widely used to detect clusters of arbitrary shape among the data points. Existing private implementations of DBSCAN either exhibit significant leakage, are highly sequential, or are asymptotically inefficient both in runtime and communication cost. In this thesis, we present an efficient approximation of DBSCAN that takes O(log²n) parallel time and O(nlog²n) total work, breaking the quadratic barrier in Secure Multiparty implementations of DBSCAN algorithms and reducing the communication rounds asymptotically from O(n²) to O(log²n).
The role of environmental gradients in the shift of wood traits from seedlings to adult trees
(University of Waterloo, 2026-07-07) Hickey, Hanna
Tree development, or ontogeny, involves concurrent changes in plant size and environmental conditions, both of which can influence wood structure and function. Disentangling the relative roles of intrinsic (height) and extrinsic (environment) drivers of wood trait variation remains a major challenge. This is important because wood underpins hydraulic efficiency, hydraulic safety, and mechanical support—functions critical for whole-tree performance. In this study, I sampled twigs at a fixed distance from the apex from sugar maple (Acer saccharum) and yellow birch (Betula alleghaniensis) to quantify how wood traits, light availability, and water availability change across seedling, sapling, and adult developmental stages. Across both species, wood structure shifted toward greater hydraulic safety with ontogeny, demonstrated through decreased vessel diameter (Dh), increased vessel number (VN), and increased vessel reinforcement ((t/b)²) in adult trees. Despite shifts towards greater hydraulic safety, hydraulic efficiency (Ks) was maintained across developmental stages, indicating that increases in VN and lumen fraction (F) compensated for reductions in efficiency typically associated with smaller Dh. Wood trait covariation was structured by a hydraulic efficiency–safety trade-off, however traits such as F varied more independently, enabling compensations such that this trade-off did not constrain tissue-level hydraulic function. Contrary to expectations, I did not detect differences in water availability across developmental stages. Although light availability increased from seedlings to adults, it did not explain changes in wood traits with ontogeny. Instead, tree height emerged as the dominant driver of trait values, reflecting increasing hydraulic constraints associated with longer water transport distance. The shift toward smaller Dh and maintained Ks in adult apices contrasts with expectations of increasing transport efficiency in taller trees but is consistent with selection for resistance to freeze–thaw embolism in temperate environments.
UniMaia: Steering Chess Policies with Language for Human-like Play
(University of Waterloo, 2026-07-07) Siu, Sherman
Recent advances in large language models have enabled natural language to serve as a flexible interface for controlling complex systems, but often require large-scale multimodal training or sacrifice domain-specific inductive biases. In structured decision-making domains such as chess, specialized models achieve strong performance but lack high-level semantic controllability, while prompt-conditioned approaches are more flexible but typically exhibit weaker domain grounding. In this thesis, we study prompt-conditioned policy modulation for chess by adapting a pretrained neural policy network using natural language prompts. We propose UniMaia, a framework that combines a frozen Lc0-based chess policy network with a LoRA-adapted text encoder and a ControlNet-style conditioning mechanism. This design enables semantic conditioning over gameplay, providing a more expressive alternative to discrete metadata for modeling human play while preserving the underlying representations of the base model. We further introduce UniMaia-Aux, an extension that incorporates auxiliary temporal conditioning and behavioral prediction objectives. To support this work, we construct a large-scale, metadata-augmented version of the Lichess dataset, introduce a semi-automated pipeline for generating natural language prompt templates, and propose evaluation benchmarks spanning both prompt-conditioned and metadata-conditioned settings. Empirically, UniMaia achieves competitive or superior performance relative to prior work across multiple benchmarks. It attains the highest top-move accuracy on prompt-conditioned benchmarks while remaining competitive with metadata-conditioned models on human move prediction tasks. Prompt-conditioned models perform strongly in frequency-dominated regimes, such as common openings and highly active player behavior, whereas metadata-conditioned models generally achieve stronger expected accuracy. UniMaia bridges these approaches by combining strong domain-specific inductive biases with flexible prompt-based control. UniMaia-Aux further demonstrates that auxiliary temporal conditioning can improve expected accuracy and behavioral modeling across several evaluation settings, although this introduces trade-offs between top-move accuracy and dependence on temporally structured information. Overall, this work demonstrates that prompt-conditioned control of domain-specific policy networks is feasible without end-to-end multimodal training. At the same time, the results highlight ongoing challenges related to prompt sensitivity, policy calibration, robustness, and the trade-offs between controllability and predictive performance in prompt-conditioned decision-making systems.
Trade-offs in Generic Programming: A Cross-Language Performance Study
(University of Waterloo, 2026-07-03) Pang, Daniel
Generic programming enables abstraction and code reuse, but its impact on performance can vary significantly depending on language design and implementation strategy. Although modern programming languages differ substantially in compilation model, runtime system, and type system design, many share a common foundation of parametric abstraction and constrained generic programming, allowing scientific algorithms to be expressed in a largely portable manner across platforms. This work investigates the trade-offs between generic and specialized implementations across multiple programming languages, with a focus on how generic realization strategies interact with runtime representation and compiler behaviour to determine performance characteristics. To evaluate these trade-offs, several arithmetic-intensive algorithms, including numerical and symbolic kernels, as well as a symbolic Gröbner basis computation, were implemented in both generic and specialized forms across a selected set of languages. These implementations were benchmarked to measure performance differences while also considering factors such as binary size and development experience. The results show that the cost of generic programming is not inherent to abstraction itself, but instead depends on how generic abstractions are realized and optimized by the language and compiler. Languages employing compile-time specialization through monomorphization, such as C++ and Rust, often approached the performance of specialized implementations in the evaluated workloads, while approaches relying on boxed representations or runtime polymorphism often exhibited measurable overhead associated with dynamic dispatch, allocation, and additional levels of indirection. Runtime specialization approaches occupy an intermediate position, trading predictable compilation behaviour for adaptive optimization. Overall, this work demonstrates that the relationship between abstraction and performance is shaped by language design, compiler strategy, and application context. These findings provide insight into both the shared foundations and differing realization strategies of modern generic systems, offering guidance for practitioners and language designers evaluating generics in performance-critical computing environments.
The Effect of Turfgrass-to-Meadow Restoration on Carbon Storage and Sequestration in an Urban Environment
(University of Waterloo, 2026-07-02) Epp, Hayden
Since 2013, Toronto and Region Conservation Authority has been restoring a 16 km hydro corridor in Scarborough, Ontario, actively removing turfgrass and replacing it with meadow. The primary intention of this project, known as The Meadoway, is to improve biodiversity and habitat connectivity in the city, but there is the potential for other co-benefits such as enhanced carbon storage and sequestration. We made use of the section-by-section restoration timeline in a space-for-time substitution to evaluate changes with time since restoration in three categories: unrestored turfgrass, recently restored meadow (restored in 2020-2024), and older restored meadow (restored in 2013-2016). First, we measured CO2 and CH4 fluxes biweekly to make estimates of daily net ecosystem exchange, gross primary production, and ecosystem respiration. We found that, cumulatively, both recent (−12.5 ± 19.1 mol m⁻² season⁻¹) and old meadow (−12.3 ± 6.44 mol m⁻² season⁻¹) acted as carbon sinks while unrestored turfgrass (+14.1 ± 17.0 mol m⁻² season⁻¹) acted as a net carbon source over the course of our sampling season. Next, we conducted a 366-day plant litter transplant experiment to investigate whether decomposition rates differ due to litter quality, site conditions, or a combination of both. We found that meadow litter decomposes 33.6% more slowly than turfgrass litter regardless of site, but all litter decomposed 34.7% more at recent meadow sites compared to turfgrass sites. We measured biomass production over the growing season by clipping aboveground biomass and using a modified soil ingrowth core method belowground. We found 272% greater aboveground biomass standing stock, and 213% higher belowground plant production in old meadow compared to turfgrass in 2025. Finally, we measured belowground carbon stocks, using a loss-on-ignition method to measure soil organic matter, soil carbonate content, and root organic matter. We found largely similar belowground carbon stocks in restored meadow as in turfgrass, with the important exception that the old meadow category had 22.4% lower surface soil organic matter compared to turfgrass. We lack baseline data on pre-restoration soil organic matter and suspect this is attributable to antecedent differences between the sections. In total, we provide convincing evidence that urban turfgrass-to-meadow restoration provides valuable climate mitigation co-benefits and could be considered a nature-based solution to climate change.
Assessment of Seat Belt Anchor Position on Rear Seat Occupant Kinematics in Frontal Impact Using a Validated THOR-5F Finite Element Model
(University of Waterloo, 2026-07-02) Thangarajah, Patrick
Rear seat occupant safety has been identified as an area of concern in frontal impacts, with small stature females experiencing severe injuries more frequently, including a higher rate of thoracic injuries. Seat belts, the primary restraint for rear seat occupants, have been identified as contributors to thoracic injury, with studies finding that belt geometry and belt anchor locations influence occupant response. A new small stature female Anthropometric Testing Device (ATD) to be used in vehicle testing is the THOR-5F ATD, designed with improved anthropometry and updated measurement capabilities, including four chest deflection sensors. In this study, a Finite Element (FE) model of the THOR-5F was validated against physical testing of the THOR-5F ATD in a Rigid Bench test configuration with a 23 g frontal crash pulse, by comparing belt interaction, iliac forces, pelvis accelerations, chest deflections, and head accelerations. The THOR-5F FE model was also assessed on a simplified version of an existing bench with a pivoting seat (Semi-Rigid Bench) and a 4-Point 2-Belt system, and the results were compared with those from a 9 g sled test reported in the literature. In both bench configurations, the overall THOR-5F FE response matched the reported physical response well, with an average ISO 18571 rating of 0.71, corresponding to a “Fair” grade. Physical testing of the THOR-5F ATD also showed bottoming out of the lower-right chest deflection sensor, which is currently under investigation as the THOR-5F design is being refined. Parametric studies were conducted to explore the effects of shoulder and lap belt anchor positioning in the fore/aft (X), lateral (Y), and vertical (Z) directions on THOR-5F FE peak chest deflections and peak resultant pelvis accelerations. The shoulder belt anchor X and Y-positions had a large effect on the overall peak chest deflection in both bench configurations, with rearward and inboard anchor locations reducing deflection, and the lowest deflections approximately 16% lower than the average. The lap belt anchor position had a smaller effect than the shoulder belt anchor on chest deflections. Based on preliminary chest deflection injury risk curves, rearward and inboard shoulder belt anchor positions resulted in lower corresponding AIS 3+ injury risks. However, the risks remained very high across all anchor configurations in both benches, indicating a need to implement restraint technologies, such as load limiters, in addition to favorable anchor positions. The lap belt anchor position had a larger effect on peak pelvis acceleration than the shoulder belt anchor position. The X, Y, and Z-positions of the lap belt anchor influenced the peak resultant pelvis acceleration in the Rigid Bench, with rearward, upward, and inboard locations reducing pelvis accelerations. The Y-position of the lap belt anchors and the YZ interaction had the largest effect on pelvis acceleration, indicating a complex interaction between the anchors and the Semi-Rigid Bench components. This study demonstrated that the Rigid and Semi-Rigid Bench FE models could replicate physical occupant and restraint responses, and that anchor position affected the chest deflection and pelvis acceleration of the THOR-5F FE. Future research should focus on belt material properties, vehicle fleet-specific anchor positions, the implementation of load limiters, and the combined interactions between shoulder and lap belt anchor positions.
Efficient High-precision Monitoring of Network Slices for 5G and Beyond Networks
(University of Waterloo, 2026-07-02) Saha, Niloy
Next-generation mobile networks are undergoing a fundamental transformation to support a variety of services such as Augmented Reality (AR), vehicular automation, and smart cities, each with very distinct performance requirements. The primary enabler for this transformation is network slicing, which enables partitioning of the shared physical infrastructure into isolated logical networks, each tailored to the performance requirements of its target service through slice-specific Service Level Agreement (SLA) guarantees. Realizing such network slices in practice depends critically on automated closed-loop management and orchestration mechanisms, where monitoring data drives analytical and control decisions. However, monitoring is a key bottleneck in achieving this goal: control logic can only optimize what it can observe, and observation is constrained by strict overhead, latency, and deployability limits. Existing monitoring solutions are domain-centric, statically configured, and not sufficiently expressive to capture the transient, end-to-end (e2e) performance signals that slice SLA assurance demands. The overarching goal of this dissertation is to build the foundation for efficient, high-precision monitoring for network slices in 5G and beyond networks. Towards achieving this goal, we address three distinct but coupled challenges across the monitoring stack. First, we introduce Monarch, a cloud-native architecture that abstracts slices as first-class monitored entities and enables direct computation of e2e slice Key Performance Indicators (KPIs) across heterogeneous domains. Next, we present SliceScope, a principled framework for dynamically allocating limited monitoring budget across slices with heterogeneous SLAs. Finally, we develop Kestrel, a detectability-driven, sketch-based telemetry mechanism for detecting and attributing transient Quality of Service (QoS) anomalies in shared user-plane resources without per-packet overhead. Together, these contributions demonstrate that efficiency and precision in slice monitoring are jointly achievable through cross-layer design spanning architectural abstractions, control plane intelligence, and data plane telemetry.
Effect of Modifying Visual and Sensory Feedback on Neural Error Processing During Sensorimotor Tasks
(University of Waterloo, 2026-07-02) Du, Rachel
The error-related negativity (ERN) is a frontocentral EEG component reflecting neural error monitoring during motor tasks, and has been identified as a promising input signal for brain-computer interface (BCI) systems. Although the ERN has been reliably observed across a range of motor and cognitive tasks, significant gaps remain in characterizing how sensory signals contribute to its generation and modulation. This thesis investigates how the central nervous system (CNS) integrates visual and proprioceptive feedback during motor error monitoring, as reflected in the ERN, across three experiments in which participants performed upper limb reaching tasks while EEG was recorded. The first experiment examined how the availability and fidelity of visual feedback influenced the ERN during a reaching task performed in a velocity-dependent curl force field. Three visual feedback conditions were applied in alternating blocks: a veridical cursor, a hidden cursor in which visual feedback was removed during the reach, and a cursor cloud that replaced the true cursor with a diffuse cluster of cursors to obscure its precise location. Trials were binned by three kinematic error metrics — integral error at 100 ms, signed maximum perpendicular deviation, and absolute maximum perpendicular deviation — and ERPs were generated for each bin. An ERN was elicited across all visual feedback conditions when trials were binned by integral error at 100 ms, and no significant difference in ERN amplitude was found between conditions. This result suggests that the ERN during reaching is driven primarily by early proprioceptive feedback rather than online visual feedback, consistent with the theory that the ERN arises from an internal error prediction mechanism using the efferent copy of the motor command. Binning by integral error at 100 ms was also found to be a more reliable method for eliciting consistent ERNs compared to maximum perpendicular deviation, highlighting the importance of selecting kinematic metrics that are temporally aligned with the ERN effect window. Additionally, ERNs were found to be elicited by trajectory deviations in both directions relative to the force field, with no significant difference in amplitude between error directions. The second experiment investigated how perturbing proprioceptive feedback through tendon vibration affected the ERN during a reaching task. Vibration was applied to the biceps brachii tendon at 90 Hz to induce an illusory perception of elbow extension, and participants performed reaches under conditions of full visual feedback, hidden cursor with no task feedback, and a transitional condition in which the vibration state was switched during hidden cursor trials. Kinematic results confirmed that the vibration successfully induced a proprioceptive shift: endpoint reach displacement differed significantly between vibration conditions when visual feedback was removed, with participants overextending when vibration was turned off following adapted reaches under vibration. When visual feedback was available, participants corrected for the proprioceptive shift, consistent with visual dominance over proprioception in multisensory integration. An ERN was elicited only when participants performed reaches with full visual feedback and without applied vibration. The absence of the ERN under vibration, despite comparable task performance, suggests that the ERN depends on the integrity of the proprioceptive re-afferent signal rather than task outcome, providing causal evidence that reliable proprioceptive feedback is a necessary condition for the error prediction process underlying the ERN. The third experiment examined how introducing a shift to the visuomotor mapping affected the ERN during reaching, using pilot data collected from five participants. A 2 cm spatial shift was introduced gradually to the on-screen cursor position along the direction of reach, creating a mismatch between the perceived and true end-effector positions that fell below the threshold of conscious detection. Participants adapted rapidly following each mapping swap, with endpoint reach displacement returning to within target bounds within the first few trials of each new mapping. Preliminary inspection of ERN amplitudes suggested that a negative deflection consistent with an ERN was present across all cursor feedback and visual shift conditions, with the largest mean amplitude observed in the first trials following a mapping swap to the shifted cursor condition. This is consistent with the finding from the second experiment that the ERN is preserved when proprioceptive feedback remains intact, and further suggests that the error prediction mechanism is sensitive to the introduction of a novel visuomotor mismatch. These trends also confirm that the absence of the ERN under tendon vibration in the second experiment was a consequence of the corrupted proprioceptive signal rather than the visuomotor mismatch it produced. Full statistical analysis awaits data collection from a sufficient number of participants. Taken together, the findings of this thesis support a model in which the ERN during reaching is generated through an internal predictive mechanism that depends on reliable proprioceptive re-afferent feedback, and in which visual feedback plays a reinforcing rather than primary role. These results advance our understanding of how the CNS integrates multisensory feedback for motor error monitoring, with implications for the development of more robust ERN-based BCI systems.
Adaptive Reuse of Federal Office Buildings into Housing: Leveraging Federal Office Downsizing Initiatives
(University of Waterloo, 2026-07-02) Lai, Julian Jeun Yin
As part of the August 2024 budget announcement, Canada’s federal government outlined a plan to optimize their real estate holdings by downsizing its office portfolio by 50%. In Ottawa, home to 42.2% of the federal workforce, this move would release 8.8 million sqft. The downtown core would be hit the hardest as widespread vacancies would force local businesses to close. This would harm not only the vitality of the area, but also the overall health and vibrancy of the city. This projected oversupply of office buildings lies in contrast to the Canada’s ongoing housing crisis. The country is desperately in need of affordable purpose-built rental housing. If it was to set a needs-based target for affordable rental housing it is estimated that the country would need 2 million affordable rental housing units at and below a minimum-wage income, which would take 30 years to build. Despite the federal government’s repeated commitments to addressing the issue, it has failed to dismantle the systemic profit-driven structures perpetuating residential alienation. This thesis builds upon existing research suggesting that converting federal offices into housing can address the housing crisis and repurpose soon-to-be vacant federal properties. It critiques current housing and adaptive reuse practices which are driven by profit and approach the crisis as a supply issue which perpetuates profit-driven commodification of housing which fails to disalienate housing. Ottawa presents opportunity to challenge the status quo of for-profit housing through federal-office-to-residential conversions. It imagines utilizing existing public assets - federal public land and vacant federal offices - for the public’s benefit by converting vacant federal offices in Ottawa into non-market housing. This can simultaneously lower the environmental impact of construction and construction costs while also creating decommodified, affordable, and good quality housing. Aside from revitalizing vacant buildings and building nonmarket housing, this strategy also has the added benefit of being environmentally beneficial too. Repurposing existing surplus buildings presents significant opportunity to mitigate building emissions through limiting construction and improving building performance which lowers operational energy-use and emissions. Research is grounded in axiological thought and seeks to critically examine, challenge, and redefine the underlying capitalist values embedded within Canada’s housing to support an expanded spectrum of what housing, living, and domesticity should and could be. Research methodologies consist of literature review, case studies, and speculative design as research. A literature review will act as the thesis’ theoretical framework by examining critical housing research challenging existing capitalist understandings of housing and propose nonmarket housing as the solution. Building on this will be a series of case studies, literature review, and speculative design to develop a successful and implementable nonmarket housing strategy. Case studies will examine successful nonmarket housing programs locally and internationally and measure both their successes and ease of implementation to outline an Ottawa-specific nonmarket housing strategy. This will be followed by a literature review and further case studies examining adaptive reuse strategies and office-to-residential conversions architectural, technical, and financial considerations to inform proper adaptive reuse practices. Finally, all research findings will be synthesized into a speculative design proposal for the conversion of the L’Esplanade Laurier federal office building in downtown Ottawa. The goal of the design proposal is to illustrate the viability and potential for impact of non-profit federal office-to-residential conversions. This thesis is intended to function both as a document demonstrating the potential for change within Canada’s current housing system and as a guide to federal office-to-residential conversions. It will demonstrate what is possible if all levels of government, housing developers, stakeholders, and community groups commit to systemic changes for housing disalienation. Housing within Canada can get better. Federal office-to-residential conversions can be part of the solution to the Canada’s housing system if the right steps are taken.
Multistroke Character Recognition Using Orthogonal Polynomial Representations
(University of Waterloo, 2026-06-30) Cheriakara Joseph, Arun
This thesis studies stroke grouping for online word-level handwriting recognition of Latin letters and digits using orthogonal polynomial representations of pen strokes. A word arrives as an ordered sequence of pen-down strokes, and the system has to decide which strokes belong to which character before it can decide what each character is. At the word level the problem is harder than for isolated characters: the right grouping of strokes depends on what the characters turn out to be, and the right characters depend on how the strokes are grouped. Most existing systems commit to one segmentation and use whatever that segmentation outputs, which can lead to wrong results. The difficulty is sharpened by characters drawn with multiple strokes, by variation in stroke order between writers, and by several letter pairs and letter/digit pairs that share the same shape. This thesis describes an online word-level recognition pipeline built on orthogonal polynomial representations of multistroke characters. Each pen stroke is re-parameterized by arc length, and its coefficients are projected onto an orthogonal Legendre basis of degree eleven, giving a fixed-length coefficient vector per stroke. For multistroke characters, the per-stroke vectors are concatenated into a single feature vector. Because all strokes in a character are normalized together against a shared bounding box, this block-concatenated representation captures the relative position and scale of the strokes within the character, but it does not directly encode every pairwise relationship between strokes. A probabilistic gap model generates up to six candidate groupings per word, and each candidate character group is normalized in a common bounding box before projection. The resulting vectors are matched against a reference database of 76{,}428 samples across 62 character labels, organized into 3{,}237 classes. Classification runs in two stages: a centroid-and-radius heuristic prunes the candidate pool to fifty classes, and a label-pooled $k$-nearest-neighbour stage then ranks the seven closest samples per label by distance to the convex hull of those samples. The pipeline is evaluated on the UniPen word collection drawn from the 62-character Latin-plus-digits alphabet.
Risk Sharing with Distortion Risk Measures Beyond Risk Aversion
(University of Waterloo, 2026-06-30) Ren, Qinghua
This thesis studies optimal risk sharing among multiple agents whose preferences are represented by distortion risk measures, equivalently Yaari dual utilities. The central question is how to characterize Pareto-optimal risk allocations when agents may have heterogeneous risk preferences, including risk-averse, risk-seeking, and behavioral attitudes toward risk. Particular attention is paid to the dependence structure of optimal allocations and to the geometry of the Pareto frontier. The first part of the thesis develops counter-monotonic risk sharing as a counterpart to the classical comonotonic theory. Comonotonicity represents positive dependence and is fundamental in risk sharing among risk-averse agents, while counter-monotonicity represents an extreme form of negative dependence and arises naturally for risk-seeking agents. Chapters 2 and 3 analyze this counter-monotonic structure for distortion risk measures, moving from a homogeneous setting with a common distortion function to a heterogeneous setting with different distortion functions. Inf-convolution is an important tool for studying Pareto optimality. Using this tool, Chapters 2 and 3 compare the usual formulation with variants that restrict allocations to be comonotonic or counter-monotonic, derive explicit formulas for risk-seeking agents, and illustrate the formulas through a portfolio manager’s problem. Chapter 4 studies markets with mixed risk attitudes, where risk-averse and risk-seeking agents coexist. This setting is more challenging because neither the usual comonotonic arguments for risk-averse agents nor the counter-monotonic arguments for risk-seeking agents apply directly to the whole market. The chapter establishes a reduction theorem showing that the general multi-agent problem can be reduced to a two-agent problem between representative risk-averse and risk-seeking agents. Based on this reduction, the chapter further studies the existence of optimal allocations, identifies cases in which the inf-convolution is unbounded, and derives explicit solutions for piecewise linear distortion functions and Bernoulli-type aggregate risks. Chapter 5 studies Pareto optimality beyond universal risk aversion, with emphasis on constrained allocation problems. Feasibility constraints, such as nonnegative allocations, are natural in insurance and reinsurance, but they change the geometry of the risk-sharing problem and may limit the applicability of weighted-sum methods. The chapter reduces the two-agent Pareto problem to a one-parameter family of constrained optimization problems. For Bernoulli aggregate risks, the Pareto frontier admits a convex-envelope characterization and can be attained by three-atom allocations. For two-point aggregate risks, finite-atom structural results are developed, showing that efficient allocations can be represented by a bounded number of payment levels. These results provide both theoretical insight into non-risk-averse risk sharing and a tractable framework for numerical computation.
LLM-Based Frameworks for Information Retrieval Evaluation
(University of Waterloo, 2026-06-29) Upadhyay, Shivani Jayantkumar
Evaluating information retrieval (IR) systems requires a reference that captures what correct or relevant output looks like, as well as a mechanism for determining whether a system’s output matches that reference. For lexical retrieval systems, both requirements are relatively straightforward. Systems rank documents by term overlap, pooling produces a judgment file that covers most documents any system is likely to return, and determining relevance reduces to a simple membership test against that file. This evaluation paradigm relies on the assumption that relevance can be detected through surface-form overlap. When retrieval moves beyond that assumption, the framework begins to break down. Retrieval-augmented generation (RAG) systems strain this setup by synthesising free-form natural language responses from retrieved evidence. A gold answer set constructed before system execution cannot anticipate every correct phrasing, so even semantically correct outputs can fail under lexical matching. Dense retrieval systems encode queries and documents as vectors, retrieving relevant documents that might not share vocabulary with the query. Under pooling-based evaluation, these documents never receive human judgments and are instead assigned a default relevance grade of zero. Together, these failures highlight the limits of surface-form evaluation and point to the need for judgment mechanisms that reason directly about meaning. This thesis investigates whether large language models (LLMs) can fill this gap by contributing three frameworks across successive layers of the evaluation pipeline. The first contribution is an open-source QA evaluation framework that combines chain-of-thought (CoT) prompting with self-consistency decoding using instruction-tuned LLMs. When evaluated across 12 systems on NQ-open, it matches zero-shot GPT‑4 in rank correlation with human judgments while using a model more than an order of magnitude smaller, demonstrating that prompting strategy can matter as much as scale. The second contribution is a framework for patching incomplete relevance judgment sets by assigning four-level TREC-style labels to unjudged query-passage pairs via few-shot prompting. When evaluated across five TREC Deep Learning Track collections at removal rates varying from 10 to 90%, it substantially improves system ranking fidelity over the standard practice of treating unjudged documents as non-relevant. The third contribution is UMBRELA, which is a fully automated open-source relevance assessment framework deployed in the TREC 2024 RAG Track across 301 topics, achieving run-level Kendall's tau >= 0.86 against fully manual assessment. All frameworks are released as open-source tools to support reproducible and scalable IR evaluation.
Investigation of Thermal Behavior in Combustion of Al/Fe3O4 Nanothermite Film
(University of Waterloo, 2026-06-29) Liu, Jingtian
Aluminum-based nanothermites are promising energetic materials for rapid heat release and microscale combustion applications, but the mechanisms governing their combustion morphology and flame propagation remain incompletely understood. This thesis investigates Al/Fe3O4 nanothermite thin films, focusing on two controlling factors: equivalence ratio (ER) and particle morphology. Al/Fe3O4 thin films provide a suitable nanothermite platform for studying the transition between destructive multiphase burning and near-gasless condensed-phase propagation due to their high energy release and relatively limited gas production. Specifically, the work examines how ER drives the transition between destructive particle-ejection combustion and structurally preserved near-gasless propagation, and how core–shell (CS) and physically mixed (PM) architectures modify the combustion event. Polymer-assisted Al/Fe3O4 thin films were fabricated with controlled ER values and particle morphologies. Their combustion behavior was characterized using synchronized high-speed optical imaging and infrared thermography, combined with post-combustion SEM/EDS analysis and inverse thermal modeling. For PM films, ER < 3 produced destructive combustion with particle ejection, localized hot spots, and partial removal of the energetic layer. In contrast, ER ≥ 3 led to preserved combustion, where the reacted layer remained attached to the substrate and formed a measurable cooling zone. This transition indicates that increasing ER improves condensed-phase continuity and shifts the combustion mechanism from reactive sintering to diffusion-driven reaction. Particle morphology also strongly affected flame propagation. At ER = 3, CS films propagated faster than PM films, with velocities of 11.93 cm/s and 6.11 cm/s, respectively. The inverse-modeled reaction rate of CS films was also more than twice that of PM films, indicating stronger reaction–transport coupling due to improved fuel–oxidizer contact and shorter transport distances. Cooling-zone analysis showed that preserved reacted layers act as thermal reservoirs, redistributing heat toward the reaction and preheating zones and contributing to flame-front stability. Overall, this thesis demonstrates that both ER and particle morphology can be used to tune combustion mechanism, propagation behavior, and post-combustion structure in Al/Fe3O4 nanothermite thin films.
A STUDY OF THE CLASSIFICATION AND QUANTIFICATION OF MICROPLASTICS THROUGH RAMAN SPECTROSCOPY AND MACHINE LEARNING
(University of Waterloo, 2026-06-29) Hogan, Úna Elizabeth
Synthetic polymers, or ‘plastics’, have become an unavoidable, necessary and ubiquitous part of modern human life. Their low cost, tunable properties, durability and ease of manufacture have led to plastics use in almost every part of day-to-day life including food packaging, car manufacturing and single-use sterile medical equipment. The durability of these plastics, while an advantage in their operational life, results in substantial longevity upon their disposal. After they have been discarded, many plastic particles can exist for up to 1000 years in the environment before their eventual breakdown. Their continued use and disposal as the global population increases have led to a large accumulation of discarded plastics throughout the world. A substantial amount of these exist in sizes of 5 mm or less, and are classified as ‘microplastics’, small pervasive pollutants that have been detected in food, drink, and inside human bodies. It is necessary for researchers to determine suitable ways of characterising and quantifying microplastic particles to increase understanding of their behavior and makeup. Expanding knowledge of the sources, abundance and variety of these particles within the environment can lead to a more comprehensive understanding of the issue of microplastics pollution. The use of Raman spectroscopy as an analytical technique for classification of microplastics particles has emerged as an efficient and accurate tool for characterisation. Traditional validation of Raman spectra using library searches and comparison to reference spectra, however, is often inadequate when the plastics have faced significant environmental degradation, which can alter their Raman spectrum. Alternative validation methods such as machine learning are becoming more widely used as superior techniques to traditional library searches, and have proven fast, effective and cheap ways to identify microplastic particles from their Raman spectra. This thesis describes iterative development of machine learning based methods to semi-automate identification of microplastic particles using Raman spectroscopy.

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