Grants and Contributions

The complex physics of impact and freezing process of water droplets when impinging on icephobic surfaces will be modelled and implemented using the Lattice Boltzmann Method. Accurate assessment of the effectiveness of the emerging ice protection technology, based on icephobic coatings, will thus be achieved through computational physics-based modelling and simulation.

Advanced solid-state NMR methods will be developed to study the molecular structure and hydrogen bonding in active pharmaceutical ingredients (APIs) in their pure and in formulated form to help unambiguous identify and quantify different polymorphs. A suitable API will be selected to be developed as a certified reference material for the quantification of different polymorphic and amorphous phases.

Accumulation of toxic forms of alpha-synuclein plays a central role in the development of Parkinson's disease. We aim to harness the ability of cells' own nanovesicles to cross the blood-brain barrier, silence alpha-synuclein and protect nerve cells, paving the way for novel therapeutics for silencing key toxic genes in other neurodegenerative diseases

Dravet Syndrome (DS) is a treatment-resistant Early-onset Infantile Epileptic Encephalopathy, severely impairing development. Identification and characterization of underlying SCN1A mutations will dictate drug screening strategies, utilizing a CHEO-curated library of blood-brain-penetrant FDA-approved drugs. This bench-to-bedside collaboration (NRC/CHEO) will establish a translational pipeline towards personalized precision medicine for children with DS.

Goal: address the challenge facing most 3D printing techniques - print multiple disparate materials into one object simultaneously e.g. metal and plastic
Partner: Professor Amaya, Carlton University
Novelty: 3D printing functional multi-material objects by controlling phase-separation
Expected outcomes: Addressable antennas with new spatial freedom in design for high-frequency 5G applications

This research team will study the air travel experience of those living with obesity with the aim of designing systems, technologies and processes to address the issues identified. The research will take place in the new Centre for Air Travel Research with collaborators from Carleton University, University of Alberta and Obesity Canada.

We will use metamaterial integrated photonic design to address the critical technical issues with current on-chip optical phased arrays, which are the core part of LIDAR systems. A new type of optical phased array based on densely packed high-efficiency grating couplers and surface-emitting nanoantennas will be investigated, including the key performance parameters such as the directionality of the emitted beam, the steering range and field divergence.

We wonder how symbolic and connectionist AI could converge in the next-generation deep learning algorithms. We approach this question by investigating and developing deep generative neural networks with capsules (multivariate stochastic neurons), which will represent elements of knowledge in capsules and dynamically piece together to produce novelties that make sense.

The proposed project aims at developing lifetime predictive models.

The effects of aluminum feedstock powder characteristics on cold spray (CS) coating adhesion on steel surfaces will be identified for the first time. It will lead to improvement in the fundamental understanding of the buildup mechanisms at the origin of CS deposition in order to optimise the CS coating adhesion properties.