Grants and Contributions

The main objective of the proposed research is to support the decarbonization of offsite construction by mitigate process waste and carbon footprint without scarifying costs through development of optimal space layout design system This will involve: (1) developing an automated space layout design system; (2) developing automated MOC-LCC and LCA models in accordance with various space layouts; (3) developing KPI-based performance evaluation platform which evaluates the performance of MOC (factory, transportation and onsite operations) in accordance with various layouts; and (4) developing a framework for optimal space layout design in MOC to contribute to decarbonization and minimize project costs.

Quantum optical magnetometers (QOM) are leading candidates for expanding searches for new physics and magnetic sensing applications, e.g., resource exploration, nuclear magnetic resonance chemical analysis and magnetic resonance imaging. The Project will establish a new QOM approach by developing a novel powerful ultraviolet frequency comb laser that coherently addresses (via two-photon excitation) the quantum ground state in xenon-129, which boasts hours-long spin quantum coherence time at room temperature. This method is expected to greatly exceed state-of-the-art cryogenic magnetic sensing sensitivity. The team will develop a prototype, carry out demonstrations of quantum magnetic sensing, and develop foundational theory of quantum coherence dynamics towards quantum sensing beyond the standard quantum limit. Furthermore, the team will apply the experimental system to sensing of environmental xenon isotopes, which serves, e.g., water and natural resource management. The new tabletop system will have higher throughput and better sensitivity than existing methods relying on facility-scale instruments (accelerator mass spectrometers).

Current efforts to design materials computationally face two bottlenecks: accuracy and speed. The workhorse density-functional theory (DFT) computational methods are time-consuming and of limited accuracy for strongly correlated systems like the metal-oxides commonly used for catalysis. (Classical) Machine learning (ML) is showing great promise in reducing the number of expensive DFT calculations in the design process. Quantum computing (focusing on hybrid approaches using Noisy Intermediate Scale Quantum (NISQ) computers for parts of the problem) is showing great promise to contribute to opening both bottlenecks by facilitating the use of more accurate quantum chemical methods and accelerating machine-learning approaches beyond the possibilities of classical ML. The proposed research will be directed along two complementary streams: i) quantum computing for quantum chemistry and ii) quantum machine learning.

This project is to improve energy efficiency in Canada's residential, commercial, and institutional building sector.

This project is mostly related to technology research and development.

This project is to improve energy efficiency in Canada's residential, commercial, and institutional building sector.

The Program will pilot innovative solutions for aggregated deep energy retrofits in community-housing neighbourhoods in Canada, as well as support Market Development Teams in developing regional roadmaps to inform and promote deep energy retrofit projects across the country.

The objective of this Project is community engagement and information dissemination that supports reducing diesel usage in remote Indigenous communities.

This project is to improve energy efficiency in Canada's residential, commercial, and institutional building sector.

The program will pilot innovative solutions for aggregated deep energy retrofits in community-housing neighbourhoods in Canada, as well as support Market Development Teams in developing regional roadmaps to inform and promote deep energy retrofit projects across the country.