Innovative Projects Realized

Computational and Experimental Study of Molecular Transformation Routes in BHT-Quinone, Para-Benzoquinone, PAN, and PAN2

This project focuses on the combined quantum computational and experimental investigation of molecular transformation routes in antioxidant systems, specifically BHT-quinone, para-benzoquinone, PAN, and PAN2. These compounds are critical intermediates in the degradation of antioxidants commonly used in lubricants, polymers, and other organic systems exposed to oxidative stress. The study integrates quantum-chemical modeling, including density functional theory (DFT) and multiconfigurational approaches, with Raman and UV-Vis spectroscopy to establish transformation pathways and spectral fingerprints of the molecules and their degraded forms. The experimental component will provide reference spectra under controlled conditions, while the computational analysis will predict electronic structure changes, vibrational characteristics, and thermodynamic stability of products. By combining both approaches, the project will generate a detailed molecular-level understanding of antioxidant degradation, enabling predictive models for monitoring material stability.

The results have direct industrial relevance for sectors where lubricant lifetime, polymer durability, and oxidative resistance are crucial, such as aerospace, automotive, and energy. The project will also serve as a platform for training students in interdisciplinary physical methods, spanning spectroscopy, computational quantum chemistry, and materials diagnostics. This research contributes to both fundamental science, by clarifying molecular transformation routes and practical applications, by informing the design of sensors and monitoring systems for oxidative degradation.

Applications of Two-Eyed Critical Sensemaking: Reviewing Academic Research Processes and Ethics from a lens of Indigenous Governance

The purpose of this project is to understand the tensions between Indigenous-led research projects and the standard academic processes that are intended to maintain ethical standards. By identifying the tensions, strategies used to navigate them effectively will also be identified. The project will be adopt Two-Eyed Critical Sensemaking as a lens to analyze research ethics processes. The Two-Eyed Critical Sensemaking Approach was designed to provide guidance for Canadian administrators who wish to respond to Commission’s Calls to Action. This research will support extension of the approach and its applicability in other settings. It will also support development of additional procedural guidelines that can adapt to the geographically specific context of Indigenous reconciliation efforts. It will also provide a unique opportunity for cross collaboration and network building for the student and Indigenous-led research efforts in Australia.

Optimisation des simulations haute-fidélité du champ de vent dans un parc éolien par l’apprentissage automatique

Wind energy plays a crucial role in the global transition to clean and sustainable energy. Maximizing wind farm efficiency requires accurate prediction of wind behavior. This project aims to enhance wind field simulations by combining high-fidelity computational fluid dynamics (CFD) models with machine learning techniques. While CFD simulations provide detailed insights into wind-turbine-terrain interactions, they are computationally intensive, limiting their use for real-time decision-making.
The project uses machine learning to learn from CFD data and build surrogate models that predict wind conditions much faster without losing accuracy. This approach enables rapid assessment of wind patterns, supports optimal turbine placement, improves operational strategies, and enhances maintenance planning.

Conducted at École de technologie supérieure (ÉTS) in Montreal, the research involves a collaborative team led by Dr. Reda Snaiki, with access to high-performance computing resources and expert guidance from PhD students. The intern will develop machine learning models and validate them against CFD simulations and, when possible, real wind data.
The expected outcomes include more informed operational decisions, increased energy production efficiency, and reduced costs. This methodology could be applied to various renewable energy systems, advancing sustainable and resilient clean energy technologies worldwide.

Évaluation multidimensionnelle de l’intégration technologique dans l’industrie minière

Cette recherche évalue l’impact des technologies émergentes (telles que l’automatisation, l’intelligence artificielle) sur les opérations minières souterraines, en considérant leurs dimensions environnementales, sociales, techniques et économiques. En développant des outils d’aide à la décision et des modèles analytiques, elle vise à optimiser la sélection des technologies, leur calendrier de déploiement et leur intégration avec les processus existants.

Le projet se concentre sur l’amélioration de l’efficacité énergétique, la gestion des flux de matières et la performance opérationnelle, tout en garantissant la durabilité et la conformité réglementaire. Pour les institutions partenaires—l’École de technologie supérieure (ÉTS, Canada) et l’Université Mohammed VI Polytechnique (UM6P, Maroc), cette collaboration renforce leur expertise dans les technologies minières durables, favorise les échanges transfrontaliers de connaissances et élargit leur impact grâce à des solutions adaptées aux besoins industriels.

À terme, ce projet fournira aux entreprises minières des stratégies fondées sur des données pour accroître leur productivité, réduire leur empreinte environnementale et promouvoir des pratiques plus sûres et plus efficaces. Il positionnera également les deux universités comme des leaders dans l’innovation minière.

Furfural production process optimization and co-product development

The proposed project aims to address a critical need for DMT, specifically focusing on the valorization of side streams from furfural production. DMT is currently commissioning a furfural pilot plant and will begin commercial sales soon. However, for the pilot plant to be successful the facility needs to utilize more of the lignocellulosic biomass (miscanthus). Levulinic acid, derived from the cellulosic portion of the biomass, with its versatile applications in various industries, presents a compelling avenue for innovation and economic growth. The ultimate goal of this project is to incorporate levulinic acid production into the current furfural production pilot facility at DMT’s site. The successful results of the proposed project will support establishing a new chemical product that has the potential to reduce the import/export deficit. Additionally, growing the agri-based value chain, which benefits local farmers by using crops and residues from Ontario’s farming community. Furthermore, a 1 ton per day capacity pilot plant would generate additional annual revenue of $350,000 and create 7 new jobs. While a 30 ton per day capacity commercial plant would generate additional annual revenue of $10M and support the creation of 25 new jobs.

Emergency Department (ED) Wait Times at Waterloo Regional Health Network (WRHN) – ED Challenge – PhD Intern

Despite numerous system interventions, emergency department (ED) wait times at Waterloo Regional Health Network (WRHN) remain a critical challenge. As part of an Innovation Partnership Procurement by Co-Design initiative led jointly with the University of Waterloo under the CareNext Coalition, WRHN is now engaging vendors and internal stakeholders to co-develop novel interventions that improve patient flow and care experience.
This project seeks to embed a PhD-level intern with advanced expertise in health systems, policy analysis, and clinical evaluation to assess the impact of pilot innovations. The intern will work closely with WRHN’s Innovation and ED teams to evaluate outcomes from both a clinical efficiency and user experience lens, while also identifying enabling or obstructive policy frameworks. The intern will be instrumental in informing procurement decisions and policy recommendations for scale-up.

Atomistic Modelling of Material and Planetary Surface Processes

This project will study how space weathering, such as solar wind radiation and micrometeoroid impacts, affects planetary surfaces, materials, and biomolecules that are important for future space exploration. Using advanced computer simulations and machine learning, we will model these processes at the atomic level to understand how they alter the chemistry of planetary and material surfaces. The research will also explore how amino acids, the building blocks of life, survive under space conditions, providing new insights into astrobiology. By combining expertise from Memorial University of Newfoundland and the University of Helsinki, the project will strengthen international collaboration, offer knowledge exchange, and support Canada and Finland’s growing roles in space exploration and the Artemis program.

Glycoproteomic analysis of erythrocytes and PBMCs

The healthcare system relies heavily on blood and blood products for a large variety of life-saving treatments. However, blood products are perishable and have short shelf lives. Given the continuous demand for these products, this issue must be addressed. As blood is stored, the profile of cell surface sugars, known as glycans, has been shown to change. However, the mechanism behind the modification of the glycans remains unclear. Red blood cells (RBCs) themselves have been studied from many different perspectives, but a critical analysis of these glycans has not yet been conducted. We hypothesize that such an analysis would reveal important links between the structure and function of these glycans and their role in stabilizing RBCs for storage as well as reducing adverse events following transfusion. This project will focus on analyzing RBCs and platelets through a glycoproteomic lens in order to gain valuable insights into the biological contribution of glycans to blood and blood products. The findings of this project will set the stage for investigating the impact of the glycosylation of blood and blood products on immune regulation and inflammatory response.

Designing cross-species-reactive compact promoters for targeting specific cell types in the body

The interns will first conduct market research to determine the demands of the current life sciences industry, including pharmaceutical companies, gene therapy development companies, and companies that offer transgenic animal models. With this data, the interns will work together using a proprietary machine-learning algorithm from Re:Pair Genomics to design a catalog of 5-10 different compact, synthetic DNA sequences used to target different brain cells, heart cells, and cancer cells. These DNA sequences are predicted as compactible with mice, monkeys, and humans. These synthetic sequences will be tested at a wet lab facility at Re:Pair Genomics, and the data will be used to refine the software model. In addition, the interns will work with legal experts to search for various IP strategies and the patentability of these synthetic DNA sequences. Re:Pair Genomics will then commercialize this catalog of synthetic DNA sequences for research in the life sciences and gene therapy field.

ContactNB/UNB AI Efficiency Project for the NB Contact Centre Industry

The McKenna Institute and ContactNB are collaborating on an initiative to promote AI adoption and responsible use within New Brunswick’s contact centre industry, aiming to enhance productivity, competitiveness, and sustainability by supporting SMEs with AI integration, workforce training, academic collaboration, and attracting investment in the business services sector.

Residual Stress Dynamics: Bridging the Gap Between Manufacturing Heterogeneity and Shakedown Relaxation

This research project investigates how residual stress heterogeneity generated during manufacturing affects the performance and durability of gears, which are critical components in power transmission systems. When properly controlled, residual stresses can improve fatigue resistance and extend the service life of mechanical parts. However, their behavior of relaxation during the early stages of operation is still poorly understood.
The project focuses on understanding how manufacturing-induced residual stress heterogeneity influences stress redistribution during gear operation, a process known as shakedown. By combining experimental techniques such as X-ray diffraction, topography evaluation, mechanical properties, and mechanical testing, the project aims to generate new insights that can improve the design and production of more reliable components.
The research supports advancements in strategic sectors like electric mobility and wind energy, where gears face increasing mechanical demands. Conducted in partnership between the Instituto Tecnológico de Aeronáutica (ITA, Brazil) and ÉTS Montréal (Canada), the project promotes international collaboration and contributes to the global effort to develop sustainable and efficient mechanical systems.

Multi-Modal Data Fusion for Satellite Based Analysis Ready Surface Reflectance

Optical satellite imagery used for monitoring the Earth’s dynamic surface is frequently limited by persistent cloud cover. This creates significant data gaps and inconsistencies in daily surface reflectance products. This project aims to advance how we combine optical data with different data modalities that can see through clouds, like active radar and passive microwave data. By leveraging these multi-modal datasets together, we can fill in the cloudy regions and make Planet’s “Planet Fusion” product — which provides a daily, complete picture of the Earth’s surface — even more accurate and reliable. For Planet, this means providing customers with better quality, uninterrupted data, helping to expand their services, particularly in areas often covered by clouds. Ultimately, this work will improve the quality of information provided by Planet Fusion for monitoring land surface changes irrespective of cloud cover conditions.