Evaluation of a green graphite based LTO battery

With millions of lithium batteries in the marked and billions yet to be, the question is can we produce a battery that can last 100 years, cannot catch fire, work in freezing conditions, charge 10 times faster and yet made from ecofriendly materials like graphite, rubber, wallpaper paste and paper? The answer is yes. Our goal is to attempt to build this novel eco-friendly battery and benchmark it with its equivalent traditionally produced counterpart. We anticipate the ecofriendly battery to perform on par but at a much lower cost of production.

Application of Machine Learning Models to Predict Potato Crop Health

This project aims to apply machine learning in the context of potato farming. By analyzing many previous spectrometer scans of potato plant leaves, models will be developed that can estimate the amount of nutrients in a potato plant based on a single scan. The models produced by this project will help offer a more scalable and economical solution for precisely monitoring potato crops. This will provide a way for farmers to assess plant health much more quickly in the field, which in turn allows them to use resources such as fertilizer and water in a more targeted and efficient way.

The potential of frass as a microbiome-based soil amendment

Industrial-scale production of protein from black soldier fly larvae (BSFL) has emerged as an efficient and sustainable alternative to wild harvesting or large animal farming. The principal waste product, called frass, has plant growth-enhancing properties exceeding those explained from primary nutrient (e.g., N,P,K) profiles, but how does frass confer these growth enhancing properties upon plants?

Data-driven quantitative analysis on strength training programs and the discovery of new training methods for high performance athletes with artificial intelligence.

The development of a standard database for the fitness industry that will store all the properties found in any given physical exercise, which will allow software applications to dissect training programs and reveal more information for specialists like strength coaches, therapists, and researchers to start making more informed decisions in their work and find new discoveries.

An accelerated COVID-19 diagnosis tool using interpretable deep learning

This research aims to develop an efficient machine learning model to detect COVID-19 patients by using their cough signals. The model is trained using thousands of audio recordings of cough signals from different subjects. The audio signals can be converted into spectrogram images that can be visually inspected to determine relevant regions of interest. There are two challenges in building the prediction models, including 1) complexity of finding the best architecture of the machine learning model and 2) understanding the reasons behind specific predictions.

Exploring adverse school experiences among underemployed young adults

This project explores the experiences of adverse school experiences among young adults who are underemployed and residing in a rural region of Southwest Saskatchewan. Adverse school experiences consist of distressing or disturbing experiences occurring within the context of schools or learning activities and can include academic failure, discrimination, or negative interactions with peers or teachers as a result of learning challenges.

Exploring Historic Black Nova Scotian Experiences with Mathematics Assessment

The overall purpose of this research is to explore the ways diagnostic assessments enable or disable growth in mathematics understanding for children most impacted by colonialism, in particular the Historical Black community in Nova Scotia. This research stems from ongoing collaborative relationships with the Delmore Buddy Daye Learning Institute (DBDLI) that has focused on strategies for decolonizing pedagogy and content in mathematics classrooms.

Developing a knowledge translation activity to share the neuroscience of living with housing instability

Homelessness is a growing challenge in Canada, with devastating economic and social costs. Neuroscience offers important insights into the causes and consequences of homelessness. Individuals experiencing housing instability are more likely to have a history of brain injury and mental illness than the general population. These brain-based conditions can be associated with impaired cognition, which can include difficulties in tasks such as following instructions, completing paperwork, or managing a schedule.

Critical minerals in the Antigonish Highlands, Nova Scotia

Natural Resources Canada (NRCan) recently developed a list of critical minerals that includes 31 minerals (chemical elements) deemed critical for Canada’s transition to greener energy. Critical elements are commodities that are geopolitically controlled, in low supply, or difficult to separate from other elements. Secure supplies of critical elements are essential for renewable energy and clean technology applications (e.g., batteries, permanent magnets, solar panels, and wind turbines).

Antifouling Performance of Graphene-based Coatings in Varying Flow Conditions

Marine biofouling, which is the growth of organisms on ocean infrastructure, is a widespread problem with
substantial economic and environmental costs. This project undertaken by the intern will develop dynamic tests
of a novel graphene-based antifouling coating designed by Graphite Innovation & Technologies (GIT). The
dynamic tests involve generating water flow over the coating surface, better mimicking real-life conditions for the
antifouling coating. The 3rd generation GIT coating is designed to be non-toxic, durable, and slippery, making it
difficult for biofouling organisms to attach.