Innovative Projects Realized

Cell Designs for High-Energy Electrically Rechargeable Zinc-Air Batteries

The objective of this project is to design and fabricate electrically rechargeable zinc-air batteries. These batteries are highly promising due to their ability to store up to double the amount of energy as current commercialized lithium-ion batteries, based on both energy-per-mass and energy-per-volume measurements. This advantage, as well as their low cost and inherent safety, could enable rechargeable zinc-air batteries to replace or supplement lithium-ion batteries to boost the driving range of electric vehicles and enable widespread integration of clean renewable power sources by storing and releasing energy on demand.

The main task of the project is to research and develop commercially feasible battery designs. The proposed project builds off the laboratory-scale advancements in zinc-air battery electrode materials made during the applicant’s PhD thesis at the University of Waterloo. The specific research conducted will include designing a zinc electrode material which can store several hundred battery charges, a gel-like electrolyte which resists evaporation and leakage, and a multiple-cell battery design for serving high-power applications. The intern and partner organization will aim to patent the designs, allowing the partner organization to enter license agreements or partnerships with battery manufacturers to produce industrial-size rechargeable zinc-air batteries.

Defining epigenetic drivers of primary and metastatic medulloblastoma – Year two

Medulloblastoma (MB) is the most common childhood brain cancer. Current treatment for these tumors is invasive involving irradiation of the entire brain and spine. Although some types of MB respond well, others have an abysmal prognosis, and the lack of less invasive therapies means that children undergoing treatment suffer from severe developmental defects and reduced quality of life. Since metastasis (cancer cells which leave initial tumor site and travel to other locations in the brain and spine) is the single biggest risk factor for poor prognosis, the Taylor Lab at SickKids is interested in generating metastatic MB cell models and determine how their characteristics differ from non-metastatic MB cells. Types of MB which metastasize frequently are observed to have aberrations in the processes that control gene expression (epigenetic proteins) in the cell. Changes in gene expression can favorably alter the environment in cells to promote uncontrolled growth and ability to metastasize. By collaborating with the Structural Genomics Consortium (SGC), we are screening metastatic MB cells with their library of chemical compounds that target epigenetic proteins.

Defining epigenetic drivers of primary and metastatic medulloblastoma

Medulloblastoma (MB) is the most common childhood brain cancer. Current treatment for these tumors is invasive involving irradiation of the entire brain and spine. Although some types of MB respond well, others have an abysmal prognosis, and the lack of less invasive therapies means that children undergoing treatment suffer from severe developmental defects and reduced quality of life. Since metastasis (cancer cells which leave initial tumor site and travel to other locations in the brain and spine) is the single biggest risk factor for poor prognosis, the Taylor Lab at SickKids is interested in generating metastatic MB cell models and determine how their characteristics differ from non-metastatic MB cells. Types of MB which metastasize frequently are observed to have aberrations in the processes that control gene expression (epigenetic proteins) in the cell. Changes in gene expression can favorably alter the environment in cells to promote uncontrolled growth and ability to metastasize. By collaborating with the Structural Genomics Consortium (SGC), we are screening metastatic MB cells with their library of chemical compounds that target epigenetic proteins.

City of Victoria Regional Cost Sustainability Analysis

The City of Victoria would like to review all costs associated with supporting the downtown core, and to determine which services are regional in nature. The City would also like to determine what the impact of supporting the core is to Victoria residents compared to residents in the rest of the CRD. The proposed research project will examine these costs and services, and identify possible mechanisms for sharing costs and fostering collaboration among jurisdictions. The focus of the study will be on the City of Victoria and its relationship with the surrounding municipalities with City and regional data being used in the analysis. The final report will include an overview of the research literature, and conclude with concrete recommendations (cost shares, total spending levels, distribution of services etc.) for the Greater Victoria area. The goal will be to provide balanced and reliable information for use in future…

Enhancing exercise adherence in people with persistent musculoskeletal pain using a behaviour change approach

Persistent pain is pain that is prolonged beyond normal healing time, usually defined as three months. Approximately one in five Canadians suffer from persistent pain conditions such as low back pain, osteoarthritis and fibromyalgia. These conditions account for $43-60 billion in healthcare costs. Prescribed exercise has been shown to decrease pain while increasing function and quality of life, however over half of patients with persistent pain do not adhere to the exercises they have been prescribed. There is a lack of research targeting exercise behaviours in a persistent pain population, and a shortage of multidisciplinary care available in Canada to treat these conditions. The proposed project builds upon proof-of-concept research established in the applicant’s PhD and will take a two-phase approach to test, refine and implement a novel empirically informed, theoretically driven intervention. The intervention employs group-based exercise and behavioural techniques to manage persistent pain and enhance adherence to exercise. This program will facilitate the Reh-Fit Centre’s mission to provide accessible programming underpinned by empirical evidence to support individuals with chronic and persistent conditions. Further, this research aims to alleviate the strain on the healthcare system by providing innovative, multidisciplinary care.

Assessing Perceptions Pertaining to Carbon Pricing in Kingston, Ontario

In their Major Research Papers, Jennifer Bunning and Nora Lobb each will explore the impact of different carbon pricing policies on citizens and businesses in Kingston, Ontario. Both a survey and a focus group will be used to engage with stakeholders from across Kingston. The findings from this research will be presented to provide context around the challenges that face the implementation of a Clean Fuels Standard and Carbon Pricing models in small communities across Canada. During their internship, they will be applying their understanding of how consumers and businesses will have to adapt to new policies towards creating a workplace sustainability program. This program will help both the individual employees and the business as a whole navigate these policies. The research will uncover pain points both businesses and individuals face when it comes to becoming more sustainable and provide a tool to help them reduce their carbon footprint.

FABJRP, Towards a Fully Automated Bilingual Job Recommendation Platform

Recruitment of future employees is an essential activity in any organization, yet it is tedious and error-prone. Substantial effort is spent in rote tasks like finding candidates matching a particular job offer, contacting them, scheduling an interview and performing the actual interview, while the more interesting tasks like making a final decision on who to hire are more exciting, yet risky. This project aims to explore the extent to which a recruitment platform could fully automated the hiring cycle, by leveraging AI technologies. In particular, we will automatically identify a shortlist of job candidates for a given offer, and make a final recommendation based on the interview responses. The interviews will be performed automatically by an AI chatbot who can engage job candidates and react to changes in emotions (e.g., stress or frustration). This project will help our industrial partner explore the limits of today’s AI and software technologies for AI-based recruitment.

Ciena OPn Innovation WP 1.1.6 – High Speed Low Power Transceiver

The intent of this project is to address the high-speed electronic portion of a silicon photonic transceiver solution that will explore new and innovative metro reach terabit optical modems. In total there are five projects that combine to create the solution. These five project areas are silicon photonic design, high-speed electronic design, modelling, packaging and test.
The throughput of Ciena’s next generation optical modems is approaching a Terabit per second, transporting data within the chip, across different dies within the same package, and between different modules on the card is becoming one of the limiting bottlenecks to our systems. To overcome this limit a 100Gb/s capable SERDES is required. Indeed, the next frontier that needs to be surpassed is a design of SERDES link in the most economical way in terms of power and real-estate. Our world-class analog/mixed-signal design team at Ciena has expertise covering high-speed data converters, multiplexors, de-multiplexors PLLs and CDRs. In this project the collaboration will be between Ciena and Prof. Mohamad Sawan, from Polytechnique Montréal. This research project will focus on designing, implementing and testing high-speed data transmitters.

Accelerating discovery through high-throughput experimentation and machine learning – Year two

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, and transform data from literature into actionable intelligence, and generate a robust workflow for the automated synthesis of catalysts and resins at NOVA Chemicals. Through ML models, we will leverage newly-generated data to guide experiments and simulations, enabling rapid molecule development, and culminate in the inverse design of molecules and materials targeting function rather than a particular molecular structure. By combining the expertise, software, and hardware tools of the Hein Lab with the instrumentation and extensive database at NOVA Chemicals, we will create a closed-loop, self-driving laboratory that will (i) be capable of implementing a diverse range of chemical workflows and (ii) create datasets that will be leveraged by AI, allowing users to navigate complex structure-function relationships and experimental landscapes.

Accelerating discovery through high-throughput experimentation and machine learning

Canonical methods of molecular discovery and reaction optimization rely on “trial-and-error” approaches and slow experimentation with low discovery rates. By harnessing high-throughput experimentation (HTE) with machine learning (ML) methods, artificial intelligence (AI) and robotics, we have the potential to dramatically accelerate the discovery and preparation of next generation molecules and materials. We will extract, unify, and transform data from literature into actionable intelligence, and generate a robust workflow for the automated synthesis of catalysts and resins at NOVA Chemicals. Through ML models, we will leverage newly-generated data to guide experiments and simulations, enabling rapid molecule development, and culminate in the inverse design of molecules and materials targeting function rather than a particular molecular structure. By combining the expertise, software, and hardware tools of the Hein Lab with the instrumentation and extensive database at NOVA Chemicals, we will create a closed-loop, self-driving laboratory that will (i) be capable of implementing a diverse range of chemical workflows and (ii) create datasets that will be leveraged by AI, allowing users to navigate complex structure-function relationships and experimental landscapes.

Use of a deep passive source extremely low frequency (ELF) conductivity mapping system to improve the definition of ore bodies at depth – Applications to Bathurst, NB

The Bathurst Mining Camp, located in northern New Brunswick, is one of Canada’s oldest mining districts. Most of the 46 known deposits were discovered in the 1950s using a combination of geological and geophysical methods. However, renewed exploration efforts over the past 15 years have not been as successful as one would expect for the level of expenditure the camp has gone through.
Aurora Geosciences Limited (AGL) is a leading-edge service provider in the application of geology and geophysics for mineral exploration. Over the past 5 years they have been using a passive source electromagnetic system (called ELF) that has the advantage of not requiring any active sources of energy (e.g generators) nor cables to be laid out in the ground. Another advantage of this system is that it can generate deep images of the subsurface up to 1-1.2 km depth. The goal of this project is to use Aurora’s ELF system over a series of known deposits in Bathurst and then use these data to produce 3D models. These models will help further exploration efforts in the area.

Disparition et formes de vie: étude sur le geste de disparaître et ses possibles dans la littérature et les arts actuels

Ce projet de recherche s’inscrit dans le cadre de ma cotutelle de thèse. Intitulée «Disparaître autrement. La disparition comme geste, et son rapport aux formes de vie dans la littérature et les arts actuels», ma thèse s’intéresse à un corpus tant littéraire qu’artistique, et s’appuie sur un édifice théorique principalement philosophique. Je cherche à analyser la représentation du «disparaître» au regard des formes de vie et des reconfigurations possibles du rapport entre l’individu et le social, dans des œuvres littéraires et artistiques (arts visuels, médiatiques et de performance) produites en Europe occidentale et en Amérique du Nord depuis 1990. Le projet de recherche que je présente aujourd’hui vise principalement à développer cette structure théorique liant littérature, philosophie, politique et représentations du réel, qui me permettra de problématiser les œuvres de mon corpus principal de thèse.