Grants and Contributions

Metal additive manufacturing (AM), and more particularly the laser powder bed fusion (LPBF) process, is a very fast-growing technological field. An integral part of the Industry 4.0 concept, AM technology fully embraces the digital manufacturing vision. The development and implementation of AM on new applications are generally slowed down due to the complexity of the physical phenomena and the lack of high-fidelity reliable models. The objective of this project is to develop a reliable digital modeling platform faithfully representing the entire process to reduce the cost of development of new parts (or new materials) which currently relies on a laborious and expensive experimental approach. This project is based on the complementarity of strong expertise shared between the NRC and the academic partners and the expected results should enable industrial partners who are members of the METALTec industrial research group to increase their competitiveness in the field of AM or to accelerate the adoption of the LPBF process.

Bioreactors play a major role in manufacturing processes of biologics. While their design has been well documented for traditional fermentations for production of simple biomolecules, their use for production of complex biologics using eukaryotic systems such as HEK-293 human cells remains very empirical. Biologics production in bioreactors is performed in a high dimensional design space requiring a high and broad level of expert knowledge not necessary captured systematically in the prevailing scientific and technical literature. Additionally, biological products such as exosomes and viral vectors (AAV) are complex biological structures that have demonstrated recently significant value in the treatment of a number of cancers and hereditary diseases. Their manufacturing in bioreactor at scale in quantity and quality that meets the preclinical and clinical needs significantly limits their use and growth potential as therapeutic tools. This creates an opportunity to integrate the expert knowledge accumulated in the databases and exploit the advances in AI to accelerate the design and optimization of the production of these two key biologics (exosomes and AAV) through the implementation and operation of a digital-twin bioreactor connected to a physical bioreactor using a series of sensors in constant communication and feed-back.

Novel optimization techniques like nanophotonic inverse design are promising tools to significantly reduce the size of passive Silicon photonic components while maintaining their functionality and performance. Although published results demonstrate various proof of concept miniaturized devices with pre-determined size and aspect ratio, their performances as of now are inferior to the state of the art and the designs are highly non-interpretable. In this respect, AI tools can help identify patterns in the high-dimensional design space through the analysis of a dataset of simulated designs that guide the search for better performing designs and shed light on the behavior of the design space, revealing its specificities and limitations. Ultimately, the use of AI tools will bring the miniaturization of Silicon integrated photonic components to the next level, without compromising on their performance and manufacturability.

This Project aims to address the development and application of microelectrochemical techniques as contributors to the in-service corrosion behavior of specimens that are sensitive to filiform corrosion. The Project consists of the following 4 research areas: • Micro-electrochemical investigation of aluminum alloy samples; • Post-mortem investigation of samples following in-service exposure; • Numerical simulation of micro-electrochemical corrosion processes; and • Corrosion metrics correlation across testing scales. The Project will study a selection of 10 aluminum alloys / surface finish / conversion coating combinations to be chosen from the bank of materials available and already tested in-service at NRC. The smart selection of materials will be based on their behavior in service, the mismatches observed and the relevance of these for members of the METALTec industrial research group whose individual activities cover the entire supply chain of the automotive and surface transportation industry.

Chitosan nanocrystals, a new family of biomaterials sourced from seafood shell waste, have outstanding properties that favor their use in the fabrication of tissue adhesive for the biomedical industry. The project aims to achieve the two goals of creating a scalable, sustainable process for the production of ChsNC from seafood waste, and consolidating research on these highly functional biomaterials for consumer applications in the biomedical sector. This project has the potential to unlock key barriers in the valorisation of an under-utilized biowaste produced in large scale in Quebec and Canada towards high value added products improving the health of
Canadians.

Microplastic pollution is adversely affecting the Canadian aquatic systems. Yet, the lack of physicochemical processes precludes scientists to accurately forecast the fate of microplastics in the Earth’s ecosystem. This three-year Project aims at understanding selected physicochemical transformation of microplastic particles (e.g., sedimentation velocity and aggregation), in Canadian aquatic systems. McGill’s research team will perform experiments in stationary and dynamic manners to mimic rivers. This Project strives to track microplastics and nano plastics particles individually and in groups, in Canadian waters, using two McGill innovations, for specific river water setting developed at NRC, in close collaboration with Dr. Pilechi at NRC. Using deep-learning and automation, this Project strives to further improve two technologies to perform in-situ and real-time measurement of both high-density and low-density microplastics at trace levels in water. Through NRC-McGill collaboration, this Project translates the academic innovations into governmental modelling to develop sound policy based on sound science, specifically NRC’s Ocean program.

SBQuantum (SBQ) is introducing ‘Magnetic Intelligence’ (MI), combining multiple innovations in magnetometry to enhance operational teams’ ability to ‘see’ underground, underwater or in other obscured environments. The Project will involve collaboration between SBQuantum, McGill University, Institut Quantique at Université de Sherbrooke and the National Research Council to explore the use of a novel quantum sensor, a magnetometer based on quantum impurities in diamonds, to enhance the understanding of the magnetic environment compared to conventional magnetometers. It will improve users understanding of magnetic data and open novel market opportunities, beyond the traditional defense and geophysical applications. SBQ intends to create a new system which will leverage the strengths of the quantum sensor to enhance MI, to be known as the ‘Sherloq’ product. Centrally it will remove the limits to current alternatives and unlock new deployment methods, such as on autonomous submarines and unmanned aerial vehicles.

One of the major barriers to studies focussed on adapting engineering systems to a changing climate is the lack of information at the spatial and temporal scales required for engineering applications. Planning and adapting marine transportation and infrastructure in the St. Lawrence Seaway is highly dependent on the availability of such information. This Project, through high-resolution regional climate model simulations of current and future climates, and advanced and targeted diagnostics to understand hot spots from vulnerability and opportunity view-points, will generate new and relevant information of importance to marine transportation in the St. Lawrence corridor. Furthermore, as an integral part of NRC-OCRE’s master project focussed at developing a new climate risk information system for the St. Lawrence River and Gulf, this Project will provide critical climate change information required to drive numerical modelling investigations of climate change effects on waves, storm surges and ice.

Scientists and public health authorities worldwide are making an unprecedented collaborative effort to understand and develop effective interventions for the control and prevention of SARS-CoV-2. Vaccination remains the most efficient medical intervention to counteract the pandemic. Viral vaccines have been the most effective in protecting against viral infections. Vectored-vaccine candidates are among the most advanced SARS-CoV-2 in the 38 clinical evaluations (WHO, Draft landscape of COVID-19 candidate vaccines, Sept. 24, 2020). One such platform is using the recombinant Vesicular Stomatitis Virus (rVSV), which is replication competent and is known to induce both cellular and humoral host immune response against foreign antigens. VSV-based vaccine vectors, which, as enveloped viruses, are designed to incorporate glycoprotein antigens into their viral lipid membrane and thus display the antigen on the virus surface, in addition to expressing it upon entry into the target cell. Another important viral vector platform that has been extensively evaluated in preclinical and clinical trials as an onco-therapeutic agent is the Newcastle disease virus (NDV), an avian virus that has several well-suited properties for development of a safe vector vaccine against SARS-CoV-2. Both vectored-vaccine platforms demonstrated good safety profiles and in the case of VSV it has been successfully used for vaccination in emergency situations such as Ebola outbreaks. This Project focus on accelerating vaccine manufacture processes by using a producing cell line compatible with cGMP operations and industrialization to address the challenges posed by large scale manufacturing. The accelerated development the proposed robust technology platform will enable higher and faster accessibility to these class of vectored vaccines in situations of pandemic and contribute to building long lasting capacities in Canada.

This project proposes the development of AI-based tools for the identification and monitoring of environment dynamics, their effect on a remote robotic platform behavior, and the adaptation of the latter to maintain stability and performance. The AI tools that will be developed during this project will contribute to increasing the level of autonomy of aerial cargo systems by providing adaptability to unknown and changing environment conditions (related to both remote site and human operator). By monitoring system behavior, these AI tools will also contribute to optimizing the dynamic response of these cargo systems during operation, thereby maximizing safety and reliability. The implementation and validation of these tools using digital twins and virtual environments will also bring the capacity for planning new operations, training and system robustness evaluation in the presence of unknown and changing conditions.