University of Waterloo Electrochemical Society Student Chapter
Past Events
2020 Events
WatECS Kick-off Event
January 30, 2020
We had a great turnout to our launching event, where we had over 90 attendees from UWaterloo, University of Guelph, as well as McMaster University. In this event, we introduced our newly founded student chapter at the University of Waterloo, including our goal, benefits to electrochemical researchers at the university and a glimpse into the types of future events that will cater to both academic and industrial interests. Dinner and coffee breaks with a short game of trivia followed the introductory presentation to break some ice. The highlight event of the night, a panel discussion with our four faculty advisors, followed the break where we asked our advisors about their perspectives on the field and where it is headed in the future, along with general career questions. The night ended with closing remarks from our president, Kiana, where we also announced the raffle winner. The event was a great success and we enjoyed meeting everyone there! Thank you to all of you who joined us for the night!
Take a closer look at our event on our gallery page!
Hydrogenics Site Tour
March 11, 2020
Our second event was a site tour of Hydrogenics Corporation in Mississauga, Ontario. As a group of 18 graduate students, we were welcomed by engineers working at Hydrogenics, who proceeded with a presentation outlining the company's work and products. Following the presentation, we were given a tour of the company and the on-site hydrogen fuelling station. It was a great experience and a unique opportunity to observe an application of our graduate research in real life work. We thank Hydrogenics Corporation for providing us with this great opportunity.
Electrochemical Cell Design for Energy and Environmental Applications Webinar
June 30, 2020
In a collaboration with NSERC Create Me2, we co-hosted an online webinar that was delivered by Dr. Edward (Ted) Roberts, Professor and Associate Head (Research), Department of Chemical and Petroleum Engineering, Schulich School of engineering at The University of Calgary. Dr. Roberts outlined a general approach for electrochemical cell design (including the bipolar stack, and the integration of porous electrodes), and introduced case studies for both water treatment and energy storage systems, which reflected Dr. Roberts’ experience in electrochemical technology development, including adsorption / electrochemical regeneration, electrocoagulation, and redox flow batteries. He also highlighted cell design and scale-up challenges arising from flow/mass transport and electrical potential distribution. as well as some innovative ideas for cell design for water treatment that were discovered and tested by his research groups. The presentation was concluded by introducing the use of flow through electrodes to develop membrane free redox flow batteries and enhance performance in zinc electrode system, followed by a Q&A session with the audience. It was a very insightful webinar and we would like to NSERC Create Me2 and Dr. Roberts for this opportunity, as well as everyone who attended the event!
Applications of Machine Learning in Battery Research Webinar
July 22,2020
In what became our biggest event yet with over 360 registrations from over 25 countries, we hosted a webinar presented by Dr. Kristen Severson who is currently a post doctoral researcher at IBM with interests broadly in applied machine learning with a current focus on healthcare applications. Prior to joining IBM, Kristen earned her PhD at MIT where she worked on machine learning applied to problems in lithium-ion batteries, production oil wells, and bioinformatics, and also holds a BS from Carnegie Mellon University. In her presentation, she talked about generating comprehensive datasets consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2300 cycles, and by using discharge voltage curves from early cycles yet to exhibit capacity degradation, applying machine-learning tools to both predict and classify cells by cycle life. The resulting model is then used in a Bayesian optimization framework for design of experiments to identify high-cycle-life charging protocols among 224 candidates in only 16 days - as compared with over 500 days using exhaustive search without early prediction. Her work highlighted the promise of combining deliberate data generation with data-driven modeling and machine learning to predict the behavior of complex dynamical systems and accelerate scientific discovery.
This event has been recorded and uploaded to our gallery page as well as on YouTube which you can access by clicking the button below.
Pore-scale modeling of battery and fuel cell performance
July 29,2020
Dr. Jeff Gostick, Associate Professor, Department of Chemical Engineering, at the University of Waterloo, presented his group's work on pore network modelling of the complex multiphysics involved in electrode reactions. The presentation started with an explanation of the extraction of networks from tomograms (both traditional solid-void binary images as well as multiphase images of Li-ion cathodes consisting of void, active material, and binder), the construction of accurate representations of the pore network structure, followed by a description of the mathematics of the physics being considered, and a discussion of some developments in implementing true multiphysics within the PNM framework.
He then introduced, via case studies, several of the recent accomplishments that his group have made using PNMs. Firstly, fuel cell catalyst layers results were presented. This was conducted using networks obtained from pFIB-SEM serial sections, which produces images with 2nm voxel resolution. The incorporation of partitioning at the gas-ionomer interface and the role of ionomer thin films in ion conduction are both accessible within this model. Dr. Gostick also discussed the application of PNMs to Li-ion cathodes. This work was done in collaboration with Electrochemical Innovation Lab at University College of London (UCL). Network models were extracted from 3-phase tomograms including void, active material, and the carbon binder phase, and validated against direct numerical simulation. Finally, preliminary results were shown highlighting the ability of PNMs to simulate transient discharge behaviour in Li-ion.
The recording of this event can be found by clicking the button below.