Grants and Contributions

Advances exploratory research under the New Beginnings Initiative

The construction and demolition sector in Canada produces about 4 million
tonnes of waste per year. Of this, an estimated 30-60% is wood waste
which is largely discarded in landfills. Wood fibre insulation is currently not
produced in Canada, and is typically made with new lumber or from pulp
and sawmill waste and petrochemical-based adhesives and additives.
The project will develop a method for making wood fibre insulation from
salvaged wood waste from the construction and demolition sector. The
project includes developing methods for making wood fibre from waste
wood products, researching bio-based adhesives and treatments for wood
fibre insulation board production, and testing the performance of the novel
wood fibre products. Upcycling waste wood into new construction products
will cycle wood back into the built environment, extend wood lifespan, add
value and longevity to a valuable material, and reduce GHG emissions and
new resources required for new construction projects.

Automation in chemistry labs expedites experimentation and minimizes errors, facilitating more cost-effective material and drug discovery. The project aims to automate chemistry labs by developing transferable, adaptable, and generalizable robotic systems using Reinforcement Learning (RL). The project is designed to tackle the challenges of integrating robotic arms into chemistry labs, focusing on minimizing data requirements and enhancing real-world applications of RL. Key objectives include developing a model-based RL method that generalizes across different robotic platforms, creating robust and transferable RL agents, and ensuring the real-time adaptability of robots to evolving lab tasks. The project addresses the unique challenge of manipulating transparent objects and aims to implement high frequency control for agile and responsive robot behavior. By extending the Remote-Local Distributed (ReLoD) system to model-based RL, the project aims to refine the real-time learning and control of lab robots, setting the stage for widespread adoption of automated, efficient, and intelligent lab systems.

This study will investigate interfaces and systems for quantum communication satellites, with two main research thrusts. First is to prepare the groundwork for a link from a Quantum Ground Station (QGS) with the anticipated QEYSSat mission at three ground station sites: the University of Waterloo’s QGS (QGS-UW), the DLR-operated QGS (QGS-OGSOP), and the primary QGS at CSA in St-Hubert (QGS-SHUB). At QGS-SHUB and QGS-UW, this work includes the definition of optical interfaces, mode of operations, transmitter beam quality/wavefront analysis, exchange of data, definition of a channel metrology setup, as well as specifications of ground?based sources and networks. Paper studies on channel characterization will be conducted at QGS-OGSOP. The second is to conduct atmospheric channel and site characterization to study light pollution and how the atmosphere affects the quantum channel at QGS-SHUB and QGS-UW. These studies will lay the ground work for potentially connecting any combination of these sites in the future.

The project will support the integration and miniaturization of optically detected magnetic resonance (ODMR) technology on silicon to bring it to a technological maturity level that enables mass production compatible with the semiconductor industry. This one-year quantum sensor project is dedicated to de-risking this challenging technology transfer venture by directly addressing potential major scientific roadblocks, such as the impact of nanofabrication on sensitivity and to assess the technology readiness for a full transfer from the lab to the industry. In addition to miniaturization, the project aims to achieve a significant increase in sensitivity compared to commercially available products. The conclusion of this project will have a significant impact on research partners and the quantum industry in Canada as a whole.

The project aims to increase metallographic laboratory autonomy and facilitate the development and optimization of processes, such as high-pressure vacuum casting (HPVDC) of aluminum, cold spraying of aluminum (CSAM) and steel made by selective laser melting (SLM), by applying generative artificial intelligence (AI) models. The project will focus first on increasing efficiency by quickly and cost-effectively generating machine learning datasets with adjustable features, making it possible to generate aluminum and steel microstructures with variations in process parameters and sample preparation conditions. Next, the application of models trained on generated images to increase the efficiency and autonomy of metallographic laboratories will be investigated, overcoming the scarcity of out-of-distribution image data by reconstruction using generative AI models. Finally, the use of AI models to reconstruct microstructure images and extract new features will be explored, with the aim of improving the prediction of mechanical properties. These developments could eventually lead to a product implemented by companies specializing in computer vision.

This project focuses on the investigation, development, and advancement of enabling technologies related to conformal reconfigurable intelligent surfaces and beamforming antenna technologies to enhance the performance of millimeter-wave wireless links, particularly in non-line-of?sight scenarios. These advancements encompass improvements in radiating surfaces, phase and high efficiency amplitude control for millimeter-wave integrated circuits, flexible substrates, materials, and conductive ink for printable surfaces. These efforts aim to address one of the most crucial and promising emerging wireless technologies. Additionally, the project aims to create technologies and solutions that enable broadband access, particularly in remote and rural areas.

This project is mostly related to road mapping in order to further the adoption of the technology.

The objective of this project is to coordinate and advance wood construction research across Canadian universities to facilitate the use of mass timber beyond its traditional use in building construction and generate the technical information necessary to pave the way for a shift towards performance-based in Canadian building codes.

The objective of this Project is to undertake community engagement, information dissemination activities and clean energy activities that support reducing diesel usage in an Indigenous community.