In-situ Exfoliation of Graphene Nanoplatelets Using Supercritical Fluid in Foaming Processing

The manufacturing of high-performance graphene/polymer nanocomposites relies on both the production of high-quality and low-cost graphene sheets and the fine dispersion of graphene in the nanocomposites. The commercial graphene materials in large quantity are typically GnPs with several to tens or even hundreds of graphene layers whose properties are inferior to monolayer graphene. In the previous work, it was demonstrated that SCF-assisted foaming of GnP/polymer nanocomposites can effectively exfoliate GnPs in situ.

Early stage detection of ovarian cancer

Infections acquired through long-term catheter use are a major problem for nearly 20% of all patients. This project seeks to apply Econous Systems’ anti-fouling MEG-OH coating to biomedical plastic catheters to see if they prevent the 3 most common microbes: E. coli, C. Albicans and S. Aureus, f rom growing. This will initially involve in vitro testing using a flow-through model to simulate blood flow, monitoring first static and then dynamic microbe growth using fluorescence microscopy. In vivo testing will follow using rats as a preclinical m

Bench Marking of FEA Software Packages in 2D and 3D

Finite Element Analysis (FEA) breaks object components into numerical finite elements and calculate their performance individually. As long as the finite element is small enough, the performance of the entire part can be simply obtained by adding up all the performances of finite elements. However, the giant number of finite elements in 3D models makes processing time too long, which reduces efficiency and adds costs. Therefore, in this project I will simplify 3D model into beam element (2D beams representing parts in 3D space to create structure).

Parallel Radiofrequency Transmission Technology for Magnetic Resonance Imaging at 3 Tesla - Year two

Funds are requested for one fellow to work in the laboratory of Dr. Simon Graham at Sunnybrook Research Institute, Toronto, in partnership with Siemens Healthcare Limited. The fellow will work on development of prototype instrumentation that will enable a technique called "parallel radiofrequency transmission (PTX)” to be implemented flexibly for research purposes on a Siemens 3 T MRI system at the Institute.

The effect of phyto-cannabinoids on osteoclast differentiation and function

The recent legalization of marijuana in Canada opens the opportunity to study in detail the effects of cannabinoids on human physiology. This academic-industry initiative will combine resources to isolate compounds from cannabinoids to study their effects on bone cells. Specifically it will examine the effects of cannabinoid compounds on how the destructive osteoclasts form and act upon the bone in vitro. This cell culture work will pave the way for future clinical studies. Bone-wasting disorders like osteoporosis are a major economic burden and inflict a large population of the elderly.

Generalization in Deep Learning

In recent years, deep learning has led to unprecedented advances in a wide range of applications including natural language processing, reinforcement learning, and speech recognition. Despite the abundance of empirical evidence highlighting the success of neural networks, the theoretical properties of deep learning remain poorly understood and have been a subject of active investigation. One foundational aspect of deep learning that has garnered great intrigue in recent years is the generalization behavior of neural networks, that is, the ability of a neural network to perform on unseen data.

Testing nutrient profiling tools and portion size based initiatives (education, regulation and reformulation) for public health policy in Canada - Year two

Poor diet is one of the factors associated with obesity and overweight, which may increase the risk for chronic diseases such as diabetes, heart disease and cancer. Two ways to improve the diets at the population level are to 1) establish public health initiatives (e.g.

Improved Numerical Combustion Models for Predicting and Reducing Pollutant Emissions in Gas Turbine Engines

Gas turbine engines are the primary propulsion device for today’s aircraft. These engines operate on liquid hydrocarbon-based fuels and as such can yield a range of undesirable pollutants including gaseous emissions such as nitrogen oxides (NOx), carbon monoxide (CO), green-house gases (GHG, largely CO2, really a combustion product) and unburned hydrocarbons (UHC), as well as nanometer-sized carbonaceous particulate matter or soot.

Improved Numerical Combustion Models for Predicting and Reducing Pollutant Emissions in Gas Turbine Engines

Gas turbine engines are the primary propulsion device for today’s aircraft. These engines operate on liquid hydrocarbon-based fuels and as such can yield a range of undesirable pollutants including gaseous emissions such as nitrogen oxides (NOx), carbon monoxide (CO), green-house gases (GHG, largely CO2, really a combustion product) and unburned hydrocarbons (UHC), as well as nanometer-sized carbonaceous particulate matter or soot.

Electrochemical Fischer-Tropsch Synthesis of Renewable Liquid Fuels from CO2

Despite a rapid decline of electricity costs, there is still demand for energy-dense liquid fuels, such as in heavy freight and air transportation. Liquid fuels can be synthesized from a mixture of carbon monoxide and hydrogen called synthesis gas (syngas). However, this process requires high temperatures and pressures, and is itself responsible for significant greenhouse gas emissions. We propose the use of electrocatalysis to produce these liquid fuels.

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