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    Investigating Pulse-Type Glaciers in the Canadian Arctic: A Case Study of the Characteristics of Dobbin Bay Glacier, Parrish Glacier, and Eugenie Glacier within the Agassiz Ice Cap
    (University of Waterloo, 2026-08-14) Nowitsky, Owen
    Characterizing irregular glacier dynamics is crucial for understanding the drivers of variations in ice motion, improving glacier dynamic modelling, and projecting future contributions to sea-level rise. The majority of glaciers exhibiting irregular dynamics have been classified as ‘surge-type’ glaciers, even though some glaciers display dynamic behaviour that falls outside the classical definition of surging. One such category is ‘pulse-type’ glaciers, which have been identified as a subset type of glacier surging within the Canadian Arctic. Similar to surge-type glaciers, ‘pulsing glaciers’ experience quiescent and active phases but experience variations of ice motion on shorter timescales, with fluctuations in glacier flow restricted to the terminus region where the bed lies below sea level. Due to the recency of this identification (i.e., first reported in Van Wychen et al., 2016), few studies of ‘pulse-type’ glaciers have been conducted, leaving a gap in the understanding their processes and characteristics. In this study, three previously identified pulse-type glaciers, Dobbin Bay Glacier, Parrish Glacier and Eugenie Glacier, located on Agassiz Ice Cap within the Canadian High Arctic, are studied using measurements of surface elevation, ice flow, terminus position and subglacial topography and are used to investigate and distinguish pulse characteristics from other forms of dynamic instability. Pulse events were identified on Dobbin Bay Glacier from 2004 to 2008, Parrish Glacier from 2004 to 2010, and Eugenie Glacier from before 2000 to 2008. Across all three glaciers, a surface elevation rise of 2-5 m a-1 was observed at regions where subglacial sills or sharp valley turns were present, while downglacier regions experienced surface elevation loss of 2- 4 m a-1. During pulse events, velocities within the pulse region ranged from 500 to 1100 m a-1, compared to upglacier regions, which experienced velocities from 50 - 110 m a-1 during both quiescence and pulse events. These findings refine the current definitions of pulse-type glaciers and provide further insight into irregular glacier dynamics within the Canadian Arctic Archipelago.
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    Sense of belonging and well-being among Indigenous Peoples of Canada: The role of community involvement
    (University of Waterloo, 2026-08-14) Cantafio, Alexis
    Well-being is a multidimensional construct whose conceptualizations vary across disciplines, cultures, and contexts. Belonging is a fundamental human need that plays a key role in fostering well-being with extensive research showing that stronger sense of belonging is linked to better mental and physical health. Indigenous conceptualizations of well-being and belonging reflect a holistic, relational understanding of health as balance among mental, physical, emotional, and spiritual domains, that are cultivated through reciprocal relationships with family, community, natural world, ancestors, and culture. Community involvement represents an avenue through which belonging and well-being are cultivated. While there is extensive research exploring the association between well-being, belonging, and community involvement there remains a lack of research exploring these associations within Indigenous populations in Canada. The present study used data from the 2017 Aboriginal Peoples Survey (N = 18,679) to examine whether two types of community involvement (organizational volunteering and helping others) moderated the association between belonging and well-being among Indigenous Peoples living off reserve in Canada. By running two separate moderation regression analysis, we hypothesized that belonging (H1) and both forms of community involvement (H2) would be positively associated with well-being, and that both types of community involvement would moderate this association (H3). In Model 1 (organizational volunteering), did not reveal a main effect for belonging. Community involvement significantly predicted well-being (b = .05, p < .001), and no significant interaction was found. In Model 2 (helping others), we found a significant main effect for both belonging (b = .03, p = .006), and community involvement in (b = .01, p = .028) Model 2 also revealed a significant interaction between belonging and helping others (b = .02, p = .028), indicating that the positive association between belonging and well-being was stronger at higher levels of helping others. Our findings highlight that different forms of community involvement may have unique impacts on the belonging-well-being relationship, and that informal participation in community may be meaningful target of focus to promote well-being among Indigenous Peoples.
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    Leveraging Interpretable Counterfactuals to Inform the Design of AI-Enabled Technologies for Human-Centered Automation in Healthcare
    (University of Waterloo, 2026-08-14) Ramirez Batista, Raul
    Hypertension is a prevalent cardiovascular condition characterized by blood pressure exceeding 130/80 mmHg, which often remains asymptomatic and undiagnosed. In Canada, 1 in 5 people with hypertension are unaware that they have the condition. The increasing integration of artificial intelligence (AI) into digital health technologies (DHTs), such as wearables, offers opportunities to support hypertension screening and monitoring; however, high levels of automation in DHTs may provide users with limited information about the conditions that influence the measurements or limitations of underlying algorithms. This is particularly important in systems that decide when to involve the user in the decision-making and action selection process, where limited visibility into the system’s reasoning may affect how users interpret an unsuccessful measurement or the absence of a notification. This thesis investigates how explainable AI (XAI), model counterfactual explanations, large language models (LLMs), and behavior-informed interface design can be integrated to support more transparent and actionable communication between highly automated health systems and their users. The research first examined the use of explainable AI to evaluate binary photoplethysmography (PPG) signal quality classifiers beyond predictive performance alone. Statistical analysis identified the acquisition and contextual conditions associated with poor signal quality, and multiple binary classification approaches were evaluated using both performance and interpretability criteria. SHAP and LIME were used to examine the features associated with individual model classifications, while DiCE was used to generate model counterfactuals presenting feasible input changes capable of shifting predicted signal quality. This approach supported the identification of models and features that were not only predictive but also more suitable for developing actionable, user-facing guidance. The resulting model counterfactuals were subsequently incorporated into an LLM-assisted prompt-generation pipeline informed by the Fogg Behavior Model. Technical model outputs were translated into short, model-informed facilitator prompts that linked potentially modifiable measurement conditions associated with bad-quality predictions to practical corrective actions. A single reviewer then refined the generated messages, removing unsupported interpretations, improving alignment between model-identified conditions and recommended actions, and ensuring that the final prompts were appropriate for presentation on a wearable interface under the relevant movement conditions. To evaluate the transferability of this corrective feedback approach to an interactive wearable measurement context, the resulting prompts were implemented in a Figma prototype of the wearable interface using unsuccessful smartwatch electrocardiogram (ECG) measurement scenarios. Twenty-eight participants interacted with prototype smartwatch interfaces and evaluated the clarity, usefulness, confidence, information needs, and intended actions associated with the proposed and existing interfaces. Participants generally valued the proposed interface’s ability to explain a measurement condition likely contributing to unsuccessful measurements, provide corrective action, and communicate clear next steps. Reported retry intention varied across scenarios, and the proposed interface did not produce a statistically significant overall increase in reported retry intention relative to the existing interface. These findings indicate that the value of explanatory prompts extends beyond encouraging immediate repetition of a measurement and includes supporting users’ understanding, confidence, and context-sensitive decision-making about whether to retry, wait, adjust the device, or seek additional information. Based on the technical and human-centered findings, this thesis proposes ACT-AI, a design framework for integrating explainability, actionable counterfactual guidance, LLM-assisted interpretation, and behavior-informed interface design into highly automated digital health technologies. The framework advances the design of AI-enabled systems by contributing an approach for making system limitations and model-identified factors more accessible to users while supporting calibrated human involvement when corrective action is possible.
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    Low-Temperature Spatial Atomic Layer Deposition of Metal Oxides and Their Integration
    (University of Waterloo, 2026-08-14) Vidish, Denys
    Metal oxide thin films are central to several emerging technologies, including sustainable flexible packaging, perovskite solar cells (PSCs), and next-generation optoelectronic devices that combine two-dimensional (2D) semiconductors with three-dimensional (3D) substrates. The translation of these technologies from laboratory demonstrations to industrial manufacturing requires deposition methods that simultaneously deliver high film quality, atomic-scale thickness control, compatibility with temperature-sensitive substrates, and the throughput needed for continuous large-area production. Atmospheric-pressure spatial atomic layer deposition (AP-SALD) can meet these combined requirements. By physically separating the precursor zones in space, and by operating without vacuum infrastructure, AP-SALD preserves the conformality and pinhole-free coverage of conventional ALD while achieving deposition rates up to two orders of magnitude higher and full compatibility with roll-to-roll processing. However, the low-temperature operation of AP-SALD (50-200 °C) that makes it attractive for thermally sensitive substrates also introduces challenges, including incomplete precursor decomposition, residual carbon and hydroxyl impurities, restricted grain growth, and defect-rich microstructures whose consequences propagate directly into device performance. This thesis establishes the process-property-performance relationships that link AP-SALD growth conditions to the structural, optical, electrical, and mechanical characteristics of the resulting metal oxide films, and to the performance of the devices into which they are integrated. Four interconnected studies are presented. First, scalable Al2O3-ZnO nanolaminate (NL) barrier coatings are demonstrated on compostable polylactic acid (PLA) and recyclable polyethylene terephthalate (PET) packaging films using a sheet-to-sheet AP-SALD system operating at 50 °C. An 8-stack NL of approximately 96 nm total thickness achieves a water vapor transmission rate (WVTR) below 0.5 g·m-2·24hr-1 on PLA and below the 0.005 g·m-2·24hr-1 measurement limit on PET, while retaining a WVTR below 2 g·m-2·24hr-1 when laminated between PET films and subjected to the harshest industry-standard Gelbo flex-durability tests. The continuity of individual nanolaminate layers is identified as essential for full barrier performance, defining an optimal design window for the coating, and the less-than-0.4 wt% additional material contributed by the nanolaminate is shown to be compatible with composting and recycling pathways. Second, a systematic study of how the AP-SALD deposition temperature (50-200 °C) controls the structural, compositional, optical, and electronic properties of SnO2 and ZnO thin films is reported. ZnO is found to crystallize in the wurtzite phase across the entire temperature range, with the grain size increasing from 30 nm at 50 °C to 150 nm at 200 °C, while SnO2 remains amorphous at low temperatures and develops nanocrystalline rutile character at 200 °C. The optical bandgap narrows with increasing deposition temperature (3.40 to 3.29 eV for ZnO; 4.01 to 3.79 eV for SnO2), the bulk carbon and hydroxyl content decrease, and the resistivity decreases by approximately nine orders of magnitude, reaching conductivities of 181 S/cm for SnO2 and 5.41 S/cm for ZnO at 200 °C with closely aligned conduction-band positions suitable for electron-selective contacts. These results provide a quantitative framework for tuning the properties of AP-SALD metal oxides for specific optoelectronic applications. Third, this fundamental understanding is translated into the practical optimization of AP-SALD SnO2 as the electron transport layer (ETL) in perovskite solar cells. By systematically identifying the impact of post-deposition annealing and substrate roughness on perovskite crystallization, a champion power conversion efficiency of 20.2 % is achieved in a simplified n-i-p architecture using AP-SALD SnO2 as the sole ETL, matching the performance of spin-coated SnO2 nanoparticle reference devices. To the best of the author’s knowledge, this represents the first high-efficiency n-i-p PSC employing AP-SALD SnO2 as the sole ETL without additional buffer or surface-modification layers, and advances AP-SALD toward industrial-scale, solvent-free, and cost-effective ETL deposition for perovskite photovoltaics. Fourth, the thesis investigates how the surface morphology, defect density, and hydroxylation of AP-SALD-grown ZnO govern exciton dynamics in mixed-dimensional WS2/ZnO heterostructures. By comparing AP-SALD polycrystalline ZnO with single-crystalline m-plane ZnO references using multi-modal spectroscopy, microscopy, and density functional theory, the rough, defect-rich AP-SALD surface is shown to create a gapped, discontinuous interface that produces approximately threefold slower exciton dissociation, spatially inhomogeneous charge transfer, enhanced electron-phonon coupling, and a unique photoinduced absorption feature at 2.03 eV that is proposed as a spectroscopic diagnostic for AP-SALD ZnO interface quality. Specific surface properties (hydroxylation, defect density, and roughness) are identified as design levers for tailoring exciton dynamics in mixed-dimensional optoelectronic devices. Together, these studies establish AP-SALD as a versatile and scalable low-temperature platform whose process parameters can be tailored to the very different requirements of sustainable packaging, photovoltaics, and 2D-material-based optoelectronics. The fundamental insights and practical demonstrations reported in this thesis lay a foundation for the advancement of low-temperature AP-SALD of metal oxides as a cornerstone technology for the scalable fabrication of functional thin films and their integration into devices that address pressing societal and technological challenges.
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    Brown Seaweed as Biosorbent for the Removal of Divalent Heavy Metal Ions from Synthetic Mining Wastewaters
    (University of Waterloo, 2026-08-14) Castillo Hernandez, Rosa Maria
    Heavy metal contamination in mining-impacted water remains a treatment challenge. Conventional treatment methods can be effective, but they may require high chemical consumption, generate secondary waste, or become less practical. For this reason, biosorption has been studied as a possible alternative or complementary treatment. Brown seaweed is of interest because its cell wall contains functional groups that can interact with metal ions, while also being relatively low-cost and available. This thesis evaluated the brown seaweed biomass as a low-cost biosorbent for the removal of selected heavy metals from mining-relevant aqueous solutions under batch and fixed-bed column conditions. Batch sorption experiments were conducted using copper, lead, cadmium, nickel, and zinc salts to evaluate the effects of metal type, initial concentration, and solution chemistry on adsorption performance. The results showed that adsorption was strongly influenced by the metal ion and the counter-ion present in the solution. Copper nitrate showed higher uptake than copper chloride and copper sulfate, suggesting that the anions affected metal interaction with the seaweed surface. Lead and copper showed the highest overall adsorption performance, while Cadmium presented a limited uptake under the tested conditions. The equilibrium sorption data were described using Langmuir, Freundlich, and Sips models, while the sorption kinetic data were evaluated using the pseudo-first-order, pseudo-second-order, and Elovich models. The pseudo-second-order model provided the best overall description of kinetic behavior. Fixed-bed column sorption experiments were used to evaluate dynamic adsorption performance under continuous-flow conditions. The sorption breakthrough curves showed delayed breakthrough and greater treated volume for lead compared with cadmium, which reached exhaustion more rapidly. The modified dose-response model provided the strongest empirical description of the breakthrough curves. Overall, this study shows that the brown seaweed biomass has potential for the removal of selected heavy metals, particularly lead and copper, while further work with real mining wastewater, regeneration, and scale-up evaluation are needed for practical implementation.