Projets novateurs réalisés

Layer-by-layer Nanofiltration Membranes for the Recovery of Rare Earth Elements

This project aims to develop and evaluate layer-by-layer nanofiltration (LbL-NF) membranes for the recovery of rare earth elements (REEs) from acidic solutions of secondary waste. The research will involve fabricating charged membrane layers on commercially available ultrafiltration supports and testing their ability to concentrate solutions of REEs while maintaining high permeability under acidic conditions. Building on membrane designs previously demonstrated by the host group for scandium recovery, the project will investigate whether similar membrane compositions can be extended to other rare earth elements. The collaboration will benefit the home institution by providing new insights into membrane technology for critical materials separation, while the host institution will expand their study of LbL membranes toward a variety of other REEs.

Conversion des résidus agricoles en nanocatalyseurs pour la valorisation du biogaz en syngaz riche en H2

The proposed project represents a strategic expansion within a larger, multi-phase research initiative aimed at advancing biogas utilization into renewable energy solutions. Currently, the first phase, funded by MITACS, is actively developing advanced purification technology for biogas using Metal-Organic Framework (MOF) filtration materials. The second phase, which includes the current project, builds upon this foundation by focusing specifically on practical applications of the purified biogas. This phase encompasses two complementary research directions: one is the proposed catalytic approach to upgrading biogas into H2-rich fuels, while the other involves developing an innovative, high-efficiency biogas-to-electricity system based on Solid Oxide Fuel Cell (SOFC) technology, funded by the Alliance-NSERC program. The present project is expected to enhance further the overall scope and impact of this larger integrated effort, demonstrating a targeted yet crucial additional step toward realizing comprehensive and sustainable biogas-to-energy solutions.

LA STRATÉGIE DE DIVERSIFICATION DES PARTENAIRES COMMERCIAUX DU QUÉBEC ET DU CANADA EN ASIE

Face aux bouleversements actuels de l’économie mondiale et aux tensions géopolitiques, le Canada et le Québec ont fait de la diversification de leurs partenaires commerciaux une priorité stratégique. L’Asie du Sud-Est, région en forte croissance et au cœur des échanges mondiaux, occupe désormais une place centrale dans cette orientation. Si les stratégies canadiennes vers l’Indo-Pacifique sont bien connues au Canada, on sait en revanche peu comment elles sont perçues par les partenaires asiatiques eux-mêmes.
Ces recherches visent à combler cette lacune en analysant, sur le terrain, la manière dont les initiatives commerciales du Canada et du Québec sont reçues, interprétées et intégrées par les institutions et les acteurs de l’Asie du Sud-Est. Plutôt que d’évaluer ces stratégies uniquement du point de vue canadien, le projet adopte une perspective décentrée, en s’intéressant au regard porté sur le Canada depuis l’Asie.
Mené principalement à Hanoï, le projet permettra de mieux comprendre la visibilité réelle du Canada, les domaines où il est perçu comme un partenaire pertinent, ainsi que les limites de sa présence institutionnelle. Il contribue ainsi à renforcer la compréhension des enjeux de la diversification commerciale canadienne et à améliorer l’impact des stratégies du Canada dans une région clé de l’économie mondiale.

Quantitative Lung Ultrasound Spectroscopy for the Characterization of Lung Injury in a Preclinical Porcine Model

Quantitative LUS (Q-LUS) is a safe, real-time imaging tool that exploits the frequency dependency of imaging artifacts to extract biomarkers that characterize the lung disease. However, the direct relationship between Q-LUS biomarkers and the severity of lung injury remains insufficiently characterized. Without this grounding, Q-LUS metrics remain difficult to interpret and cannot be linked to damage severity or disease evolution.

This project investigates the correlation between Q-LUS biomarkers and the severity of the lung injury. Ultrasound data from preclinical porcine models with healthy lung and induced lung injury will be acquired. Data will be processed to extract Q-LUS biomarkers to statistically associate with different levels of injured severity.

University of Toronto will benefit of advance expertise in Q-LUS, including study design, ultrasound platform, and data analysis. A unique lung ultrasound dataset on controlled large animal models will be generated, positioning Canada as an active and important contributor to the development of Q-LUS.

The university of Trento will benefits of preclinical porcine models presenting an induced, controlled lung injury to acquire data. The outcome of this project will fill a knowledge gap on the conducted research line. Furthermore, this work represent an important component of the intern’s doctoral thesis.

Prédiction de la durée de vie des matrices d’extrusion d’aluminium par modélisation mécanique et apprentissage automatique

Ce projet propose une approche intégrée, combinant modélisation mécanique et apprentissage automatique, afin d’estimer quantitativement la durée de vie en fatigue et de prédire la durée de vie restante (RUL) des matrices. La démarche repose d’abord sur le développement d’un modèle numérique par éléments finis (FEM) de la matrice, intégrant des chargements représentatifs des conditions d’exploitation, afin de calculer les champs de contraintes et de déformations et d’identifier les zones critiques d’amorçage. Un plan d’expériences (DoE) est ensuite défini pour faire varier de manière structurée la géométrie, la position et la taille des défauts, permettant de générer, via des calculs paramétriques automatisés, une base de données traçable regroupant les paramètres de simulation, les indicateurs mécaniques extraits et les estimations de durée de vie obtenues à partir de modèles de fatigue et de propagation de fissures.

Recovering the properties of the globular cluster population in earliest phases of the Milky Way

Globular star clusters are massive, ancient, spherical collections of stars which orbit, in the hundreds, within nearly every galaxy in the universe, including our own Milky Way. These old systems provide a cosmic fossil record of the earliest epochs of galaxies, about 12 billion years ago, and they are key for understanding how our own galaxy assembled. However, the precise history and origin of these clusters remains very uncertain. These fossils have not been left unperturbed, but instead represent the survivors of billions of years of dynamical evolution and interaction with the galaxy. In order to study the initial properties of clusters, we must thus rewind their evolution starting with what we see at the present day. Utilizing fast computational models which capture the evolution of star clusters from their initial conditions and the gravitational influence of a galaxy on the clusters over time, we will learn about the population of globular clusters in the Milky Way as a whole and its properties in earliest phases of the galaxy. This promises to place constraints on the formation of star clusters and the assembly of galaxies like our own.

Multiscale Thermomechanical Analysis of Phase Change Phenomena in Polymer-Based Solvents

This project aims to improve how polymer-based solvents are modeled during freezing and solidification processes that are critical to modern semiconductor manufacturing. Building on prior experimental and theoretical studies (i.e., the collaborative work from participated institutions), the project will develop an accurate and reliable computational framework that captures how heat transfer and mechanical stresses interact across multiple length scales, from the device level down to micro- and meso-scales. The project will help explain how phase changes in solvents affect surface quality, structural stability, and reliability of nanoscale semiconductor devices by strengthening and integrating advanced numerical modeling capabilities. The expected benefits include enhanced predictive tools for semiconductor fabrication, improved alignment between experiments and simulations, and stronger research collaboration and technical expertise at the participating institutions, particularly by expanding simulation-based evaluation capabilities at the University of Toronto and advancing complementary computational mechanics expertise at the Kyoto Institute of Technology.

Deciphering the molecular mechanisms by which bacteria hijack the neutrophil inhibitory receptor LILRB3 to evade their destruction.

Bacterial infections continue to cause a large number of deaths around the world, and the problem is getting worse as many bacteria become resistant to the antibiotics we rely on. New medications are urgently needed to successfully treat these infections. This project aims to address this need by discovering how certain bacteria are able to “trick” our immune system so that they are not easily destroyed. In particular, we are looking at how they interfere with neutrophils, immune cells that are the first responders in fighting infections. Some bacteria can shut down the signals that neutrophils use to work properly, allowing the infection to spread. By understanding exactly how bacteria do this, we can identify ways to restore these signals and neutrophil antimicrobial defences. This knowledge will help scientists develop new treatments that restore the neutrophil’s ability to kill harmful bacteria and help save lives. This research can only be accomplished by the sharing of expertise between our two research teams which will greatly accelerate discovery by both teams in this field.

Mechanistic characterization of mixture toxicity of different cannabinoids

This project will investigate how mixtures of select cannabinoids affect different types of human and animal cell lines. As the use of cannabinoid-based products continues to rise in both Canada and Germany, there is a growing need to understand potential health impacts, especially when multiple cannabinoids are present together. By studying mixture toxicity rather than single compounds alone, the project will generate new insights into how these chemicals interact within biological systems, helping to fill an important knowledge gap for regulators, researchers, and industry.
The collaboration brings together complementary strengths from the University of Bonn and the University of Saskatchewan, both of which have established expertise in toxicology, cell-based assays, and chemical or human health risk assessment. The exchange will provide the MSc student from Bonn with hands-on research experience using advanced cell-culture and toxicity-testing approaches available at the University of Saskatchewan, while also fostering knowledge transfer between the two institutions. The results of this work will support ongoing research programs at both universities and help build shared scientific capacity in an area of growing public and scientific interest. Ultimately, the project will strengthen international collaboration and contribute to safer and more informed use of cannabinoid-containing products in both countries.

Analyse de l’évolution des programmes d’emploi pour les jeunes en contexte de restrictions budgétaires

Dans un contexte de changements profonds du marché du travail, de perspectives plus incertaines pour la jeunesse et de restrictions budgétaires des programmes de jeunesse au Québec, l’étude proposée vise à observer et comprendre les transformations en cours dans les politiques de jeunesse-emploi sur une décennie (2015-2025), à travers l’étude de cas des Réseaux des carrefours jeunesse-emploi du Québec (RCJEQ). Une attention particulière sera portée sur les relations et tensions entre les acteurs de la mise en œuvre des politiques de jeunesse-emploi — les Carrefours jeunesse-emploi (CJE) — et les acteurs de la planification tant à l’échelle provinciale que fédérale. Il s’agira ainsi d’appréhender comment ces politiques sont planifiées et pensées, en mobilisant des grilles d’analyse des politiques publiques, mais également par l’étude de sources documentaires et institutionnelles et un travail de terrain auprès des acteurs des CJE. L’étude permettra de mettre en lumière les enjeux de financement, la mobilisation des acteurs du secteur jeunesse-emploi et les logiques politiques à l’œuvre, ainsi que leurs impacts sur les trajectoires des jeunes.

Acute Impact of an Extremely Low Frequency Magnetic Stimulus on Human Neurophysiological Function

Power-lines and electric appliances are the main sources of daily human exposure to power-frequency magnetic fields (MF, 60 Hz in North America). In order to protect the public and workers from potential adverse effects, international agencies publish MF exposure guidelines based on the “best estimate” available regarding the levels producing an acute biological effects in humans, which is called magnetophosphenes (flickering lights perceived eyes closed in the dark while exposed to a strong MF). Since 2005, we investigate power-frequency MF exposures and reported effects on human movement, memory, brain activity, postural control and magnetophosphenes. In this project, the interns will be involved in improving the characterization of visual (magnetophosphenes) and postural (standing balance) responses to high-level power-frequency MF exposure, providing insights into interaction mechanisms. These results will improve international guidelines protecting the public and workers from MF adverse effects, and will have potential for translational developments (diagnostic and therapeutic applications).

Instructions as code – instructing agentic AI models for changes in large projects

Software developers and companies are increasingly adopting agentic AI to implement software features. In practice, developers guide AI agents through explicit instructions that specify, for example, how to navigate the project, identify relevant source files, and test the generated code. Without such instructions, agents may blindly perform implementations leading to incomplete or incorrect results.

While instructions are intended to support AI-assisted development, recent work shows that instructions alone are insufficient to effectively guide agentic systems. Instead, instruction quality and level of detail play a critical role in agent performance. Despite a large body of research on the use of AI and large language models (LLMs) in software engineering, most existing studies focus on prompt-engineering techniques. However, recent findings suggest that prompt engineering provides limited benefits for advanced models, particularly those with strong reasoning capabilities. As a result, developers increasingly lack guidance on how to write high-quality instructions, especially in complex software projects involving multiple interacting components and third-party dependencies.

In this project, we aim to study how developers formulate instructions in relation to software system complexity, characterized by interconnected components and external dependencies, with the goal of deriving guidance for writing effective instructions for agentic AI in real-world software projects.