Grants and Contributions

Terahertz (THz) radiation is a form of electromagnetic energy that occupies a broad frequency band between 0.1 and 10 THz, and is non-ionizing. These frequencies coincide with natural dynamics of biological systems, including low-frequency vibrational modes of large molecules (e.g., all proteins, DNA) and the stretching/twisting modes of hydrogen bond networks that are ubiquitous in biological systems. This interaction mechanism is the basis behind existing novel THz diagnostic imaging technologies for which the high sensitivity to molecular/chemical structure provides excellent contrast between diseased and healthy tissue. However, exposure to higher levels of THz radiation can also affect the structure and function of target biomolecules. This project investigates these effects of intense terahertz radiation in proteins, cells, and skin tissue.

The Ultrafast Nanotools lab, houses the source of terahertz radiation and sample chambers that will be used to perform biological exposure studies. This is central to the proposed research, as sufficient radiation sources of the type required for these investigations are not commercially available. The state-of-the-art sources of intense terahertz pulses constructed for research purposes are known to be capable of generating peak electric fields and intensity distributions sufficient to induce significant non-thermal biological effect in target samples, as reported in several academic publications.

The project will examine the impact of solid state and submerged fermentation, and enzymatic modification on the functional and nutritional properties of commercial pulse (e.g., yellow pea, faba bean and lentil) protein concentrates or isolates. Fermentation of pulse fractions will be done using traditional GRAS strains, Aspergillus niger, A. oryzae, and Lactobacillus plantarum, under different fermentation conditions (pH, temperature and time) to obtain different levels of hydrolysis (5, 10, 15 and 20%). Similarly, proteases (Trypsin and Savinase) will be added to modify protein isolates under different conditions (enzyme: substrate ratios, temperatures, pH and times) to obtain similar levels of hydrolysis. Changes to the protein’s surface properties will be measured, along with their functionality (e.g., solubility, emulsifying, water/oil holding and foaming). Changes to levels of anti-nutritional compounds and protein quality (PDCAAS and digestibility) will also be examined as the result of processing.

Modern technological materials are typically multi-component compounds with complex structures that determine their opto/electronic/chemical properties. The design of new functional materials has been greatly assisted by developments in efficient and accurate electronic-structure methodologies, most notably Density Functional Theory (DFT). But DFT is too expensive to perform the rapid screening of thousands of candidates required for the design of new high-performance materials. This project aims to harness the power of modern Artificial Intelligence to build on the success of DFT and ferret out optimal design parameters. This project is of prime socioeconomic importance, aiming to reduce the environmental footprint of the Alberta oil sands by designing new nanocatalysts for the chemical reactions involved in in-the-reservoir upgrading.

The goal is to investigate intelligent management of supply chain digital twinning with application to the Global Agri-Food Value Chain (GAVC) that improves food quality and safety. GAVCs often span across the globe and involves various organizations that vary in production approaches and standards with regulatory actors and government regimes that vary in competence and transparency. The global distribution of agri-food products is creating longer and more complex supply chains that obscure traditional methods of oversight, coordination and collaboration.In recent years, there is an increased interest in the digitalisation and digital transformation of production processes and supply chains resulting from the acceleration of technological development such as the fourth industrial revolution (Industry 4.0). In this project, the focus on artificial intelligence solutions to integrate value chains across firms and manage the infusion of data to address many challenges such as complexity, uncertainty, risk, visibility, collaboration, cost, and sustainability.

Designing a drug for a specific disease type is highly challenging due to tremendous molecular search space and limited understanding about the disease of interest. Successful discovery of a medicine for a target (gene/protein/pathway) usually last for many years and costs a private pharmaceutical organization billions of dollars, which in turn determines the unaffordable price of medicines in the Canadian and global markets. The emerging of massive biochemical and high-throughput genomic data acquisition techniques and the rise of advanced artificial intelligence paradigms provide an unprecedented opportunity for automatic design of new drugs with much faster pace and much lower cost, igniting hopes to discovery drugs for diseases where no medicines have been found yet. A develop a data- and AI-driven drug design platform/pipeline will be developed for precise and effective treatment of cancers and complex diseases. The project will take advantage of NRC’s unique multidisciplinary expertise in AI, data science, and human health therapeutics, as well as external collaborators’ knowledge and facilities, to tackle challenges in every step of drug design. Project technologies and outcomes will be disruptive and beneficial to all Canadians.
This project will focus on the development and testing of AI methods and molecule parameterization.

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.

It is well-known that asphalt pavements in cold regions such as Canada are exposed to number of environmental stresses such as thermal fatigue cycles and low-temperatures which result in different modes of distresses most commonly cracking. In addition, repeated application of traffic loads on flexible pavement induces structural cracking known as bottom-up fatigue distress. Considering that cracked pavements are more susceptible to water seepage and frost action, it is crucial to reverse the cracking process through use of self-healing bituminous materials in order to extend the service life of the infrastructure, reduce the amount of Greenhouse Gas emissions and decrease the energy usage. Improving the self-healing properties of asphalt binder and Hot Mix Asphalt (HMA) through innovative techniques and novel applications is still at emerging stage. This project will investigate the rheological properties and healing characterization of nano-clay modified asphalt binder and performance testing of HMA specimens containing nano-clay modified asphalt binder.

Silicon nitride is becoming established as an important nanophotonic platform because of its compatibility with CMOS fabrication and its substantially broader spectral transparency range compared to silicon. Silicon nitride allows CMOS integrated photonics to break into new application areas, in particular sensing in the visible and mid-IR spectral ranges, quantum photonics and nonlinear optics. However, silicon nitride poses some fundamental challenges. The high index contrast, which allows device miniaturization, causes significant optical scattering losses, mode size disparity between optical fiber and on-chip waveguides results in high coupling losses, and fabrication tolerances are tight, making manufacturing yield a major concern. Subwavelength engineering was successfully used to overcome similar challenges in silicon waveguides and the team believes that this strategy can also be advantageously implemented in the silicon nitride platform, which has not yet been done. In this proposed New Beginnings project, the benefits of subwavelength metamaterial engineering for the emerging nitride nanophotonic platform will be demonstrated. This can result in major advances in state-of-the-art silicon nitride integrated devices, including new photonic sensors, next generation quantum communications chips and on-chip optical amplifiers for telecom applications.

Financial support is provided based on the conference’s goal to provide a welcoming, engaging and affordable environment for attendees by helping to keep the registration costs at a minimum for the student delegates.