Grants and Contributions

This project will advance the digital transformation of the construction industry by developing an integrated, data-driven decision support system for sustainable supply chain management. It combines Building Information Modeling (BIM), Geographic Information Systems (GIS), real-time sensor data, and logistics information into a graph-based integration framework, visualized through interactive Power BI dashboards. The system will provide real-time information on material flows, logistics, and project performance. Multi-criteria optimization and decision-making methods will be applied to design strategies for the decarbonization of procurement, transport and waste management, in a way that minimizes costs, emissions and material waste, while supporting the principles of the circular economy. A user-centric design approach will ensure stakeholders have intuitive, out-of-the-box dashboards that enable interactive analysis, route visualization, and sustainability assessment. Overall, the project will support informed and timely decision-making, reduce delays and waste, and contribute to a more resilient, low-carbon construction sector.

Quantum dots (QDs) are tiny semiconductor particles with remarkable optical properties, holding enormous promise for applications in medicine, diagnostics, and sensing technologies. However, a major barrier to their widespread use is the difficulty of producing them reliably and at scale — current methods suffer from batch-to-batch inconsistency, degradation over time, and the release of toxic heavy metal by-products. The Project addresses these challenges by shifting from traditional single-batch production to a continuous flow reactor system, enabling the scalable, reproducible manufacture of stable QDs. A key innovation is the development of novel encapsulating layers that protect the particles, lock in toxic components, and provide a versatile platform for attaching targeting or sensing molecules. The Project outcome will be safer, more reliable quantum dots that can be precisely engineered for applications ranging from early disease detection to environmental sensing — bringing transformative QD-enabled technologies meaningfully closer to real-world use.

The Project, a collaboration between the NRC and NORAM Electrolysis Systems Inc. (NESI) includes three work scopes. The NRC will provide NESI with a Lithium rich brine produced via two lithium extraction processes, a solvent-based approach and a Lithium leaching approach. NESI will process the Lithium streams using NESI's proprietary electrolysis and pre-treatment technologies. Then, NESI will take the electrochemical cell products and further process them by evaporation crystallization and other concentration and purification methods to form a battery grade lithium hydroxide monohydrate (LHM), a sulfuric acid stream, and a salt recycle stream. NESI will analyze the LHM and acid product and provide an impurity profile. Based on dialogue with NESI and results produced throughout the Project, the NRC will provide novel membrane composite materials for use in the NESI electrolysis cells. NESI will perform electrolysis with the novel membrane composites to quantify process improvements resulting from the novel membranes.

The Project will support the development of polarisation resolved single photon sensors over a broad range of photon energies (THz - telecom - visible) using photoexcitation of trions in gate defined quantum circuits in two-dimensional (2D) quantum materials. Unique advantages of 2D quantum materials include bandgap tunability, photon polarisation locked with valley optical transitions, high quantum coherence and trion binding energy exceeding room temperature. The Project will support the development of quantum sensors in that quantum transport of a single electron from source to drain through a quantum dot (QD) proceeds at a specific value of back-gate voltage. When a single photon is absorbed in a QD, an exciton is created. An electron coherently interacts with the exciton forming a trion, changing the required back-gate voltage and quenching transport. Hence, the presence of a single exciton absorbed by a single photon can be electrically detected at a single electron level. The team will also explore methods to enhance the photon-electron interaction such as coupling to an optical cavity. This Project will advance the state-of-the art technologies for single photon sensing by the development of 2D materials-based quantum sensors.

Electric vehicle adoption and battery second-life applications require efficient tools for battery degradation monitoring and state-of-health estimation. Electrochemical Impedance Spectroscopy (EIS) is widely used for this purpose; however, University of Windsor research has identified a critical limitation in conventional measurements: open?circuit-voltage (OCV) induced bias can distort impedance estimates, particularly at low frequencies. The Project will advance a corrected EIS excitation and analysis method that combines a tailored excitation waveform with an observation model to estimate and remove the OCV contribution from the measured response. Initial proof-of-concept experiments have demonstrated feasibility at TRL 3. The Project will advance the technology to TRL 4 through systematic laboratory validation, benchmarking against conventional EIS, and evaluation across different cells, chemistries, states of charge, and measurement conditions. The NRC will conduct parallel validation and contribute to documenting the methodology and results.

The SENSE-APP-II project will support the move towards commercialization for an AI-powered driving safety app for mature drivers (65+). The app will deliver timely feedback, longitudinal driving reports, and personalized risk assessments, along with proactive safety recommendations to help mature drivers maintain their independence. Advanced machine learning algorithms will be developed based on a new massive longitudinal telematics dataset provided by the industry partner, the Canadian Automobile Association (CAA). The Project presents the opportunity to advance the understanding of driver risk by incorporating several years of data from 30,000+ drivers across Canada. In the app, both telematics and wellness data will be analyzed by appropriate algorithms. The results will be interpreted and personalized recommendations and status reports will be created on demand for the driver. Regular wellness inputs also keep the driver's safety profile "real time" and holistic in nature. The SENSE-APP will leverage the analysis of individual telematics and wellness data over time for SENSE-APP drivers. This approach constantly adds live streamed telematics and self-reported wellness data to the analysis of the individual driver's risk profile.

The Project aims to integrate NRC’s VitalSeer technology, a contactless, camera-based system that uses computer vision and AI to monitor vital signs into Baüne’s Autonomous Care Unit (ACU™). The ACU™ system delivers real-time, personalized care through a modular and scalable digital health ecosystem that integrates Point-of-Care (PoC) technologies and Internet of Medical Things (IoMT) devices. It supports autonomous, contactless health assessments and predictive analytics, enabling early detection of health
deterioration and enhancing primary care delivery across urban, rural, and remote setting. Designed to improve accessibility and inclusivity, the upgraded system supports preventative care in community and home-based settings. It features real-time, contactless vital sign monitoring, predictive analytics, and streamlined data collection to enable early intervention, particularly addressing fall risks among seniors aged 65+, which cost Canada $5.6 billion in 2018. Deployment at Baüne’s ACU™ system has already supported
proactive home-based health management in community settings (CLSC Pierrefonds and CIUSSS-ODIM partner sites), and the addition of the NRC’s VitalSeer technology will improve senior’s safety at home by using validated, with optional minimally invasive modes for enhanced precision or continuous data collection.

The project focuses on graph minor-embedding as it relates to commercialscale annealing quantum computers. Graph minor-embedding is a necessary step when running problems on annealing quantum computers and, in general, is an NP-hard problem. Efficient heuristics designed specifically to map user problems to D-Wave’s Quantum Processing Unit (QPU) topologies (the 6-degree Chimera™ and the 15-degree Pegasus™) are currently leveraged by most users, despite the current availability of a more connected 20-degree topology (Zephyr™) released on D-Wave’s latest-generation quantum computers. This project aims to design improved heuristic algorithms for graph minor-embedding onto the Zephyr topology. In particular, the Zephyr topology features a type of graph edge known as the “odd coupler” that allows for more versatility in embeddings. One of its applications is generating additional routing options when processor graphs have low yield. Another application is generating qubit chains with shapes other than “L” (a series of vertical qubits connected to a series of horizontal qubits by an “internal coupler” type), resulting in new types of logical connectivity. These algorithmic improvements will directly impact areas where quantum annealing is expected to provide important acceleration compared to classical techniques. One of them is increasing robustness to graph yield differences in distributed quantum computing scenarios. Another is a greater support of computationally-hard problem classes. Many applications feature densely connected problems with connectivity far beyond what is natively provided, so efficient embedding of dense structures enables quantum annealing to solve larger problems with increased solution quality.

Non-pharmacological interventions centered around lifestyle modifications, especially sleep, constitute promising approaches to delay the onset or progression of neurodegenerative diseases impacting cognition. An important challenge for the implementation of such interventions is the ability to deliver them in an accessible and ecological context. To address these challenges, we will integrate a validated sleep intervention - cognitive-behavioral therapy for insomnia (eCBTi+) - from CRIUGM’s e-SPACE web platform into DigiSensing, a digital phenotyping platform and smartphone application developed at NRC. This integration aims to enhance accessibility, acceptability, engagement, and adherence among older adults, while enabling more personalized treatment and
feedback. The integrated solution will first be tested and refined with selected participants before broader deployment across Canada to assess its acceptability and usability among older adults living in their communities.Additionally, effectiveness of the intervention will be assessed using objective and subjective sleep-related outcomes.

With the transition to a low-carbon economy and the uncertainty of complex international relationships, it is required to secure and enhance the supply of critical battery metals in Canada. The project will develop an innovative process that can produce next generation battery materials precursors through selective pressure leaching and precipitation. Leveraging the fundamental research strengths of Queen's University and the NRC's applied research and scale-up capabilities, this project will advance knowledge in selective battery metal production while semi-automated high pressure high temperature (HPHT) reactor systems for critical metal extraction will reduce human intervention. This project will strength chain, reduce GHG emissions, provide HQP training, and promote sustainable resource utilization. By boosting domestic production capacity and accelerating innovation, the collaboration will help drive economic growth and reinforce lobal battery sector. These technological advancements will improve research efficiency and accuracy, supporting a sustainable, low-carbon economy.