Innovative Projects Realized

Model Development to Correlate Pulp Properties with Chip Quality and Online Sensor Data

The proposed research project will investigate how specific pulp fibre properties, such as length, impact the mechanical and optical qualities of paper. Using data from Quesnel River Pulp’s operations, the student will create a detailed profile linking these fibre characteristics to paper strength, surface texture, and brightness, providing insight into how various wood chip blends perform under current refining conditions. This project will support Millar Western in refining its mechanical pulping process to maintain consistent quality, reduce off-grade production, and enable greater adaptability to market demands by correlating fibre properties with final product quality. This approach strengthens QRP’s process control and supports its competitiveness in the pulp and paper industry.

Développement et validation des semelles en lattices imprimées en 3D pour décharger et traiter les ulcères du pied chez des patients diabétiques

Chez les personnes diabétiques, la marche régulière permettrait de réduire la mortalité, aide à contrôler la glycémie et diminue l’insulinorésistance. Cependant, elles risquent davantage de développer des ulcères aux pieds, pouvant conduire à l’amputation. La prévention et le traitement précoce de ces problèmes incluent des chaussures ou semelles optimisant la répartition de la pression plantaire. Pourtant, les paramètres de conception et le choix des matériaux pour les semelles sur mesure restent peu documentés. Ce projet, en partenariat avec Toolkit3D, vise à créer une bibliothèque de structures lattices pour semelles de marche, maximisant la décharge de pression plantaire et réduisant les risques d’ulcères chez les patients diabétiques. Les lattices seront conçues avec un logiciel spécifique, guidé par des données de pression plantaire de participants sains, ciblant des zones de haute pression. Des semelles en lattices seront produites par impression 3D et testées en laboratoire de biomécanique avec des capteurs de pression pour optimiser leur efficacité, assurant ainsi une décharge maximale et un confort adapté à chaque patient.

Simplified technique for measuring ammonia emissions during manure management: A validation study

Manure storage and spreading release various air emissions, including ammonia (NH3), methane (CH4), nitrous oxide (N2O), and particulate matter. Among these, ammonia is the most common gas emitted, which can harm air quality, human health, and the environment. High ammonia levels contribute to air pollution by forming fine particulate matter, which harms human health and wildlife. It can also create acid rain, negatively impacting soil quality, water bodies, and aquatic life, disrupting entire ecosystems, and decreasing biodiversity. Therefore, managing ammonia emissions is crucial for protecting air quality and human health, as well as maintaining healthy ecosystems and promoting sustainable agriculture practices. Current methods for measuring these emissions are often complicated and expensive, creating a need for simpler and more affordable options that can be used on different farms. This research project focuses on testing a low-cost passive flux sampler (PFS) to measure ammonia emissions during manure storage and spreading. The study includes a thorough validation process, featuring laboratory experiments, field tests, and modeling techniques. The project aims to offer a practical solution for monitoring ammonia emissions by developing and validating this easy-to-use tool. This research seeks to address the challenges associated with manure management while supporting sustainable farming efforts.

Mineral Protection of Organic Carbon in Marine Sediments: Isotopic and Molecular Characterization Across Diverse Environmental Regimes in the St. Lawrence Natural Laboratory

This project aims to study how organic carbon (OC) is preserved in marine sediments in the St. Lawrence Estuary and Gulf, a unique natural environment with a range of different conditions. By examining the way OC interacts with minerals in sediments, the research will help us understand how carbon can be stored long-term, rather than released back into the atmosphere. This knowledge is essential for predicting and managing the impact of human activities and environmental changes on coastal ecosystems. The project will strengthen collaboration between Concordia University and ETH Zurich, combining Canadian and Swiss expertise in Biogeochemistry of marine systems, and will produce valuable data for international carbon research initiatives.

Small Green Changes: Understanding the Relationship Between Structure and Light Interactions of Modified Perovskite Supercrystals

This project aims to improve solar energy conversion technology by exploring the self-assembly of cesium lead halide perovskite supercrystals (CsPbX3 SCs). These supercrystals have unique structural and photophysical properties that can be leveraged to make solar cells (and other optoelectronic devices) more efficient and longer-lasting. The project will focus on gaining a fundamental knowledge on these modified supercrystals to help explain why they form certain morphologies and why they exhibit structurally-related photophysical properties. The result are stable and well-understood materials for converting sunlight into electricity. This research will help advance the understanding of how these materials work and how they can be used as active layers in devices. For the participating institutions, this project will also contribute valuable knowledge to the field of renewable energy, foster collaboration between academic researchers, and support the development of cutting-edge research leading to affordable, efficient, and stable light capture and conversion technologies.

Enhancing Wildfire Detection and Prediction with Deep Learning and Quantum Machine Learning

Wildland fires in Canada pose significant risks, causing extensive damage to ecosystems, property, and human life. The record-breaking 2023 wildfire season, which devastated 18 million hectares, underscores the urgent need for improved detection and mitigation strategies. Recent advancements in deep learning have demonstrated strong potential in early wildfire detection and fire spread prediction, providing critical support for mitigation efforts.
Building on this progress, Quantum Machine Learning (QML) offers a promising avenue to further enhance wildfire management. By leveraging quantum computing’s ability to process complex data structures and optimize algorithms, QML can complement deep learning approaches, improving model performance in challenging scenarios. Synthetic datasets like SWIFT, have already enhanced training for real-world applications, and QML can further improve the efficiency of these synthetic data-based approaches.
This project aims to integrate QML with deep learning to develop cutting-edge solutions for wildfire management. Objectives include generating synthetic visible and infrared data, developing real-time detection systems, predicting wildfire spread through spatiotemporal analysis, identifying potential ignition hotspots, and creating comprehensive wildfire risk maps. The integration of QML is expected to significantly improve detection accuracy, prediction capabilities, and overall wildfire management strategies.

Adsorption of Greenhouse Gases from Mining Industries Using Zeolite Membranes Technologies

The mining sector plays a significant role in GHG emissions, contributing to climate change and environmental degradation. This proposal aims to investigate specific emissions associated with mining activities and evaluate the potential of zeolite membrane direct capture technologies as a viable solution for emission reduction. By addressing gaps in the current literature regarding the application of zeolite membranes in mining, this research will provide valuable insights into their effectiveness, economic feasibility, and contributions to sustainability goals. Overall, the findings could inform industry practices and policy decisions, promoting a more sustainable approach to resource extraction

Characterization of the stability of a novel antifungal peptide

Antimicrobial resistant infections are a burgeoning threat to humankind. Unfortunately, antimicrobial resistance (AMR) programs have traditionally focused on bacteria and excluded fungi, which are now considered critical priority pathogens. Candida albicans, a commensal in many sites in the human body, can become pathogenic in immunocompromised patients. It is resistant to almost all classes of clinically-available antifungals. We have recently engineered an antimicrobial peptide from a salivary host defence peptide and confirmed its ability to mitigate antifungal resistance. Although our peptide was degraded by some of the fungal proteases, it retained excellent antifungal activity and further in-depth investigation of this is necessary. To further develop this peptide, the intern will perform in-depth characterization of the effects of fungal proteolytic enzymes and salt conditions on the peptide. Investigations will include high performance liquid chromatography and mass spectrometry to characterize peptide degradation. Antimicrobial and antibiofilm assays will be performed against reference and multi-drug resistant clinical isolates of Candida albicans. The intern will also perform circular dichroism assays to characterize the secondary structure of the peptides. This MITACS Globalink Research Award funded project will take us a big step closer towards the development of a resistance-proof antifungal peptide.

Operational Optimization of PV-B (Photovoltaic-Battery) renewable energy communities with the objectives of minimizing OPEX and peak load

This project focuses on creating a smart, efficient energy management system for local communities powered by solar panels and batteries. By using advanced reinforcement learning (RL) techniques, the system will automatically control energy use, storing excess solar power in batteries to reduce costs and keep the grid stable. This approach helps communities rely more on renewable energy and less on traditional grid power, making them more independent and environmentally friendly. Ultimately, this project offers a flexible, future-ready solution to help communities lower energy costs, extend battery life, and actively contribute to a greener, more resilient energy system.

Optical Frequency Combs in Surface Nanoscale Axial Photonic Microresonators

This project aims to explore the generation of optical frequency combs (OFCs) in a surface nanoscale axial photonic microresonator (SNAPR). A SNAPR is an optical device created by modifying the radius of an optical fibre. This modification enables the trapping of light within the device, enhancing its intensity and leading to more pronounced optical effects.

One such effect is the generation of OFCs. An OFC is a pattern of light made up of multiple, evenly spaced lines across a spectrum. Under very specific conditions the incoming light does not exit the device with the same wavelength that was input. Instead, the device generates a series of evenly spaced spectral lines. These spectral lines can then be used as a hyper-precise ruler. This process of frequency comb generation is complex and not straightforward, and the goal of this project is to perform a detailed study of this phenomenon in SNAPRs.

The collaboration between Dr. Del’Haye’s and Prof. Bianucci’s research groups will strengthen their work in microphotonics and specifically nonlinear optics. This project will also provide valuable hands-on experience for the intern and foster future collaborations between the two research groups, advancing the field of optical technologies.

Do interpersonal relationships affect the impact of verbal encouragement on exercise performance?

Verbal encouragement aims to increase athletes’ performance by mobilizing resources, such as commitment and motivation, self-efficacy and/or the reduction of stress. A large body of literature supports its effectiveness in a variety of settings (e.g., sport, exercise). Yet, most studies suffer from important limitations, including the lack of external validity of the experimental stimulus. In particular, many verbal encouragement studies utilized standardized verbal stimuli, which were provided by research assistants who were strangers to the participants. The present study seeks to explore the interaction of characteristics of the sender and receiver of verbal encouragement, as well as their interaction during a task. The planned research will significantly advance our theoretical understanding of the process of verbal encouragement, which may in turn help to elevate athletic performance.
The present study is a collaborative effort between St. Francis Xavier University and the University of Münster. It is intended to be the first project in a line of studies, which will be conducted collaboratively in Germany and Canada, deepening the ties between both institutions, enriching the experiences of all stakeholders and producing cutting-edge research, which may be fundable by several agencies in both countries.

Arthroscopic Fixation vs. Allograft Replacement in Type Ia Glenoid Fractures: A Comparative Study of Outcomes

Glenoid fractures, which happen in the shoulder, are usually caused by either instability or severe injury. A specific type, called Ideberg-Goss Type Ia, involves a break in the front part of the glenoid (the socket of the shoulder joint) without affecting the neck of the bone or the rest of the shoulder blade. Treating these fractures without surgery doesn’t work well. Surgeons can use either open or minimally invasive (arthroscopic) methods, but there’s no clear agreement on the best option. Open surgery tends to have more complications. One minimally invasive method, called Arthroscopic Anatomic Glenoid Reconstruction (AAGR), uses a bone graft from a donor and is a safe and reliable option for some fractures. This study compares two approaches: fixing the bone arthroscopically or replacing the damaged part with a graft. This study’s results will give surgeons more data on clinical outcomes after different arthroscopic surgeries for Ia glenoid fractures.