Safa Jinje

Microfluidic device reveals how tumour shapes can predict cancer aggressiveness

rahul.kalvapalle — Wed, 18 Dec 2024 15:46:03 +0000

Microfluidic device reveals how tumour shapes can predict cancer aggressiveness

rahul.kalvapalle Wed, 12/18/2024 - 10:46

Cutline

Associate Professor Edmond Young of U of T's Faculty of Applied Science & Engineering (left) and PhD alum Sina Kheiri co-developed the Recoverable-Spheroid-on-a-Chip with Unrestricted External Shape – or "ReSCUE" – platform (photos courtesy of Edmond Young and Sina Kheiri)

Safa Jinje

Topic

Breaking Research

department of mechanical and industrial engineering

Institute of Biomedical Engineering

Faculty of Applied Science & Engineering

Subheadline

The platform, developed by U of T researchers, allows for unprecedented control and manipulation of tumour shapes

Researchers in the University of Toronto’s Faculty of Applied Science & Engineering have designed a microfluidic platform that can be used to predict cancer cell behaviour and aggressiveness, opening up new avenues for personalized and targeted cancer treatment.

The Recoverable-Spheroid-on-a-Chip with Unrestricted External Shape (ReSCUE) platform, developed by a team led by Edmond Young, an associate professor in the department of mechanical and industrial engineering, gives researchers the ability to recover and release tumoroids – tumour cells derived from patients – to perform downstream analysis and characterization.

This allows for unprecedented control and manipulation of tumour shapes, a largely unexplored area in cancer research.

“While there are several platforms for in vitro modelling of spheroids – three-dimensional aggregates of cells that can mimic tissues and mini tumours – a challenge in the cancer research field has been the inability to control the shape, recovery and location of these cancer organoids,” says Sina Kheiri, a PhD alum and co-lead author of the study, which was published in Advanced Materials.

“So, researchers end up with these tumours-on-a-chip that can’t be easily characterized because they are stuck on the device and can only be observed through optical microscopy.”

The platform also enables researchers to grow cancer organoids in different shapes. This is important, Kheiri says, because much of the current research on cancer cell in vitro modelling is focused on spherical tumours, but tumours in a body can take many different shapes.

“In many invasive cancers, the tumour shape is not spherical. For example, in a recent study of 85 patients with breast cancer, only 20 per cent of tumours were spherical,” he says. “If modelling studies are limited to spherical tumour shapes, then we are not looking at the full parametric space and scale of tumours that are seen in real life. We are only looking at a small portion of the whole answer to understand cancer cell behaviour.”

Kheiri’s PhD research was co-supervised by Young and Eugenia Kumacheva, a professor in the Faculty of Arts & Science’s department of chemistry who is cross-appointed to the Institute of Biomedical Engineering. Kumacheva’s lab developed a biomimetic hydrogel that is used as a scaffold in the multi-layer ReSCUE platform, allowing the patient-derived cancer cells to grow and organize the way they would inside human tissue.

The platform was developed in collaboration with David Cescon, a clinician scientist and breast medical oncologist at Princess Margaret Cancer Centre and associate professor in the Institute of Medical Science at the Temerty Faculty of Medicine. Cescon’s team provided access to the cancer cells that were used to form breast cancer organoids.

This image shows culture, release and transfer of tumoroids from the ReSCUE platform, as well as the released breast cancer disk-, rod-, and U-shaped tumoroids cultured in biomimetic hydrogel over zero, seven, 14 and 21 days (image courtesy of Young Lab)

The idea that tumour shapes determine cancer cell behaviour was a serendipitous discovery for Kheiri: while optimizing and developing the microfluidic platform, he discovered that some of the patient-derived tumoroids were forming positive curvatures because of the shape of the microwell. 

“I was playing with the aspect ratio of the microwells and observed that when the wells had a more rod-like or elongated shape, rather than a circular or disc shape, the tissues formed cellular strands at the regions with positive curvature,” he says. “I didn’t see that in tumoroids from the same cancer-cell sample that formed a spherical shape.

"So, we started to make different shapes and analyze the effects of shape or curvature on cancer behaviour."

The team looked at disk-, rod- and U-shaped tumoroids; they found higher cell activity and higher proliferation at the positive curvatures – where the tumour shape is convex and outward curving.

This could mean that the growth of cells in these areas is more invasive compared to areas of the tumour that have a flat curvature.

“Understanding the relationship between tumour shape and cell behaviour is important for predicting tumour aggressiveness and planning appropriate treatment strategies, such as targeted radiation therapy or drug delivery,” says Kheiri. “We want to open this door and give researchers a platform that they can use to study how different tumour shapes respond in anti-cancer drug treatment, in radiotherapy and chemotherapy.”

Now a postdoctoral researcher at the Massachusetts Institute of Technology (MIT), Kheiri continues to provide support to the Young lab on development of the ReSCUE platform. The researchers recently submitted a U.S. patent and are looking to build on their results.

“We hope that these uniquely shaped mini tumours can help biologists and cancer researchers better understand the biology of cancer cells and how they respond to drugs,” says Young.

“We’re going to add even more complex features, such as surrounding vasculature. The more control we have over the features we can include in our models, the more realistic they become, and the more accurate our drug testing will be.”

PhD student aims to improve the robotic tools used by neurosurgeons

Christopher.Sorensen — Fri, 18 Oct 2024 17:14:27 +0000

PhD student aims to improve the robotic tools used by neurosurgeons

Christopher.Sorensen Fri, 10/18/2024 - 13:14

Cutline

Kejah Bascon, a PhD student in the Faculty of Applied Science & Engineering, is one of the 2024 recipients of the Indigenous and Black Engineering and Technology (IBET) Momentum Fellowships (photo by Safa Jinje)

Safa Jinje

Topic

Our Community

department of mechanical and industrial engineering

Faculty of Applied Science & Engineering

Subheadline

“I’ve aspired to be a neurosurgeon since childhood, and since then, my ambitions have expanded”

Kejah Bascon’s path to pursuing a PhD in engineering at the University of Toronto – where she aims to design better neurosurgical tools – had many twists and turns.

Born and raised in Ottawa, she spent much of her primary and secondary education in homeschooling before enrolling in a public high school for her final year to help simplify her transition to university.

“I had wanted to study biomedical engineering for my undergraduate degree, but I worked a lot during high school to support myself – and so I wasn’t able to manage all the prerequisites with my circumstances,” Bascon says.

“I was quite hard on myself, but I knew I would find a way to make it work.”

Bascon ultimately completed her undergraduate degree in cognitive psychology at Carleton University, where she also minored in neuroscience. She then spent three months in medical school abroad before returning to Ottawa to complete her master’s degree in neuroscience.

“I’ve aspired to be a neurosurgeon since childhood, and since then, my ambitions have expanded to include applying engineering to neurosurgical practice to innovate surgical tools and technologies,” she says.

“I realized that my path wasn’t going to be as linear as I thought when I first fell in love with the brain and the responsibility of holding a person’s whole consciousness in one’s hands.”

Bascon is one of the 2024 recipients of the Faculty of Applied Science & Engineering’s Indigenous and Black Engineering and Technology (IBET) Momentum Fellowships, which provide financial support, mentorship, training and networking opportunities to reduce systemic barriers for entry into academia for members of underrepresented groups.

While Bascon is still planning the direction of her PhD research, she aims to make robotic surgical tools more user-friendly for neurosurgeons by employing engineering principles and a human factors approach.

Improving robotic-assisted neurosurgical tools such as the endoscope can help reduce the cognitive load on neurosurgeons and prevent musculoskeletal injuries following repeated long surgical procedures.

Bascon will be working under the supervision of Myrtede Alfred, an assistant professor in the department of mechanical and industrial engineering, to achieve her research goals.

“I never had a Black TA or a Black professor before coming to U of T,” says Bascon, “and now I get to benefit from mentorship and leadership from a Black woman.

“It has only been a few weeks, but I already feel so much support for my academic journey from Professor Alfred.”

As an IBET fellow, Bascon says she is especially looking forward to taking advantage of all the opportunities to gain professional development, expand her academic and professional network and give back her time as a mentor.

“I’m beyond thankful to receive this support for my PhD journey,” she says. “I hope I can offer an example for others like me, who are from low-income, underrepresented communities, who may have trouble seeing themselves on a university campus.

“I was once in that position too – of not having the right representation. If I can set an example for others, I want to show that this is what an engineer can look like, and this is what an engineer’s background can look like.”

Battery-powered EV chargers – co-developed at U of T – installed on St. George campus

rahul.kalvapalle — Fri, 20 Sep 2024 14:34:25 +0000

Battery-powered EV chargers – co-developed at U of T – installed on St. George campus

rahul.kalvapalle Fri, 09/20/2024 - 10:34

Cutline

The Level 3+ battery-powered EV charging stations are available in U of T's Landmark Garage, located beneath King's College Circle on the St. George campus (photo by Safa Jinje)

Safa Jinje

Topic

Breaking Research

department of mechanical and industrial engineering

Facilities and Services

Faculty of Applied Science & Engineering

Landmark

Sustainability

Subheadline

The rapid-charging stations were developed by Canadian EV tech company Jule in collaboration with experts at the Faculty of Applied Science & Engineering

The historic core of the University of Toronto’s St. George campus is now home to a pair of next-generation electric vehicle (EV) charging stations that utilize technology co-developed at the university.

The two new stations use direct current (DC)-sourced EV chargers boasting integrated battery energy storage systems – novel technology that minimizes strain on the electrical grid.

Located in the Landmark Garage beneath King’s College Circle, the chargers are rated Level 3+, meaning they can charge EVs in under 30 minutes, and bring the total number of EV charging stations in the garage to 50.

The technology underpinning the new charges was developed by Jule, an energy storage and EV solutions company co-founded by Faculty of Applied Science & Engineering alumnus Carmine Pizzurro, in collaboration with U of T faculty members.

Jule embarked on its first research collaboration with U of T shortly after its founding in 2009, teaming up with the Centre for Applied Power Electronics led by Professor Reza Iravani at the Edward S. Rogers Sr. department of electrical and computer engineering.

It also worked closely with the city's electric utility.

“One of our first collaborations was with Toronto Hydro, which led to us being the first company in the world to put lithium-ion batteries on the distribution grid to provide backup power during outages and reduce stress on the grid during peak periods,” says Pizzurro, who earned his bachelor’s and master’s degrees in mechanical engineering at U of T.

Cristina Amon and Carlos Da Silva (fourth and fifth from left, respectively, in the front row) pose with students and staff in the Thermal Management Systems lab (photo by Aaron Demeter)

Pizzurro went on to install Jule’s first battery-powered fast chargers in northern Canada as part of a collaboration with Natural Resources Canada.

But the company needed to address a longstanding challenge with lithium-ion batteries: they’re temperature-sensitive and must be able to operate reliably in both hot and cold environments if they are to help power a net-zero future.

To tackle this issue, Jule in 2018 expanded its partnership with the Faculty of Applied Science & Engineering to include University Professor Cristina Amon and senior research associate Carlos Da Silva of the Advanced Thermofluids Optimization, Modelling and Simulation (ATOMS) laboratory in the department of mechanical and industrial engineering.

The ATOMS experts have been developing computational models and experimental characterization to optimize Jule’s battery thermal management systems – work that is being carried out in a state-of-the-art battery testing facility that received funding from the Canada Foundation for Innovation and Ontario Research Fund.

“Thermal management is an issue that impacts both aspects of Jule’s EV fast charging technology: the power electronics to enable the charging, as well as their unique integrated battery storage system,” says Da Silva, who is also executive director of the U of T Electrification Hub. “Thermal management is critical for mitigating battery degradation. It requires regulating the temperature in such a way that you keep the battery within an optimal range that will extend its life span.”

Jule’s fast chargers use energy stored in batteries, rather than drawing it directly from the electrical grid in the manner of traditional fast chargers. That means they don’t cause grid overloading during peak usage times and can be charged during off-peak hours when electricity is less costly; they also don’t require significant investments in electricity upgrades.

“The battery storage system is charged using current from the electrical grid, which is alternating current (AC); and then this larger battery, which uses direct current (DC), charges the smaller battery in the electric vehicle,” says Amon. “It is more efficient to fast-charge from a stationary battery to an EV – DC to DC – than it is to fast-charge an EV from the electrical grid, which requires converting AC to DC power.”

Jule’s Level 3+ charging station can provide up to 200 kilowatts of power output, yet only needs 45 kilowatts of input power. Amon says this rapid charging speed can help alleviate range anxiety among EV users: “Some drivers fear that EV batteries may not have enough energy to reach a desired destination. But if charging time is much closer to the time required to fill up a tank of a gas-powered car, that can reduce this worry.”

L-R: Professor Reza Iravani, Jule co-founder Carmine Pizzurro, U of T Electrification Hub Executive Director Carlos Da Silva, University Professor Cristina Amon and U of T Engineering Dean Christopher Yip (photo by Safa Jinje)

The Level 3+ stations are joining 48 Level 2 chargers that are already available for public use at the Landmark Garage.

This increases the campus’s charging capacity to over 25,000 charges per year, which can eliminate over 700 tons of greenhouse gas emissions, according to U of T’s Sustainability Office.

“Hosting these first-of-their-kind EV chargers right here on campus extends beyond providing a new and exciting sustainability service to our community,” says Ron Saporta, U of T’s chief operating officer, property services and sustainability. “It represents just one example of how we are supporting the intersection of research, learning and commercialization of sustainable innovations developed by members of our very own U of T community.”

The charging stations will also serve as a living lab to test future thermal innovations jointly developed by U of T Engineering researchers and Jule.

“Having these chargers on campus enables us to have a greater capacity to test the system in ways we are limited by doing in a lab setting,” says Da Silva.

Healthy aging in place: New pilot program to provide high-tech cognitive, physical enrichment for seniors

rahul.kalvapalle — Thu, 22 Aug 2024 13:32:02 +0000

Healthy aging in place: New pilot program to provide high-tech cognitive, physical enrichment for seniors

rahul.kalvapalle Thu, 08/22/2024 - 09:32

Cutline

2RaceWithMe, a tool developed by Professor Mark Chignell from the department of mechanical and industrial engineering in U of T's Faculty of Applied Science & Engineering, combines physical and cognitive engagement by requiring users to pedal to be able to watch scenic videos (photo by Justin Greaves/Centivizer)

Safa Jinje

Topic

Our Community

department of mechanical and industrial engineering

Institutional Strategic Initiatives

Connaught Fund

Faculty of Applied Science & Engineering

Subheadline

The initiative, supported by a Connaught Community Partnership Research Program award, explores the use of interactive tools to promote active lifestyles among older adults

Older adults living in a Toronto co-operative apartment building will soon have access to high-tech activity spaces designed to promote cognitive, physical and social enrichment, thanks to a new pilot project led by the University of Toronto’s Mark Chignell.

The activity spaces aim to promote healthy aging for older adults who are aging in place – meaning they have the social and health supports to live safely and independently. They will be located in the communal area of a City Park co-op building that is considered a naturally occurring retirement community, where more than 30 per cent of occupants are over the age of 65.

The initiative was launched by Chignell, a professor in the department of mechanical and industrial engineering in U of T’s Faculty of Applied Science & Engineering, in collaboration with HelpAge Canada, a non-profit that supports community-based services for seniors, and AGE-WELL, a research network and U of T institutional strategic initiative.

It’s one of nine projects to be supported by 2024-25 Community Partnership Research Program awards, given by U of T’s Connaught Fund with the aim of accelerating research carried out in collaboration with community partners and driven by their needs and priorities.

“My work is motivated by the fact that physical and cognitive health can decline very quickly for older adults,” says Chignell.

“We know the physical body and brain work together, and that physical exercise is important for cognitive status and preventing dementia. So being able to promote more active lifestyles for people who are still living independently in the community can have an immense benefit for our society.”

The activity spaces will feature products from Centivizer, a U of T startup spun off from Chignell’s research, that specializes in creating interactive activities, games and cognitive assessment tools to support healthy aging.

These include 2RaceWithMe, a device that promotes both physical and cognitive engagement by having users pedal while watching scenic videos that only play when the pedals are in motion.

Centivizer has also developed a suite of whack-a-mole-style games for cognitive assessment that Chignell hopes can be used to promote cognitive safety in clinical practice.

A two-time recipient of Connaught Innovation Awards, Chignell says he feels honoured to now receive support from the Connaught Fund's Community Partnership Research Program.

“The Connaught is a great validation as I start this project in naturally occurring retirement communities,” says Chignell. “I am also continuing to work with retirement homes and long-term care centres, including a new collaboration with a long-term care home in Tokyo, Japan.

“I’m hoping that this project will demonstrate the value of using our products in the community to help older people retain their physical and cognitive abilities for longer.”

U of T team takes top spot in self-driving car challenge for 6th time in 7 years

Christopher.Sorensen — Wed, 07 Aug 2024 17:42:55 +0000

U of T team takes top spot in self-driving car challenge for 6th time in 7 years

Christopher.Sorensen Wed, 08/07/2024 - 13:42

Cutline

As part of the competition, the U of T team’s autonomous vehicle had to react to obstacles such as a fake deer moving across the road (photo courtesy of aUToronto)

Safa Jinje

Topic

Our Community

Alumni

Artificial Intelligence

Faculty of Applied Science & Engineering

Graduate Students

Research & Innovation

Self-Driving Cars

Undergraduate Students

UTIAS

Subheadline

"Each time we saw an obstacle – a stop sign, a red light, the railroad bar coming down – and the car reacted by stopping and then continuing, we let out a big cheer or a sigh of relief"

A team from the University of Toronto has placed first for sixth time in seven years in a North American self-driving car competition.

After finishing in second place last year, the aUToronto team returned to the top spot at the 2024 SAE AutoDrive Challenge II, which was held in June at the Mcity Test Facility in Ann Arbor, Mich.

The aUToronto team competed against nine other teams from across Canada and the United States.

“Through the AutoDrive Challenge, we are preparing the next generation of engineers to head into the industry, to keep pushing towards the challenging goal of reaching Level 4 autonomous driving,” says Tim Barfoot, a professor at the U of T Institute for Aerospace Studies (UTIAS) in the Faculty of Applied Science & Engineering and one of the team’s academic advisers.

“The team did another excellent job this year.”

The team approached the competition by going back to first principles to ensure they had a reliable and robust system, says Kelvin Cui, a U of T Engineering alumnus and the team’s principal.

He joined aUToronto last fall after five years with the University of Toronto Formula Racing team, where he founded the “driverless” division.

“We looked at what was going to get us the most points at competition and made sure that we were not overbuilding our system and adding too much complexity,” he says.

This meant pushing for additional testing time at UTIAS and achieving more than 900 kilometres of system testing prior to the competition.

The team placed first out of 10 teams from institutions across the United States and Canada (photo courtesy of aUToronto)

A partnership with the AutoDrive team from Queen’s University was instrumental to aUToronto’s preparation. The aUToronto team drove Artemis, their autonomous vehicle, to Kingston, Ont. to assess the system at Queen’s testing facility, which features intersections and electronic streetlights.

“We added radar to our vehicle as a new sensor, so we needed to be aware of all the sensor failure modes,” says third-year Engineering Science student Robert Ren.

“A lot of our testing time went into making sure that including radar didn’t break anything else in our system, and that it could handle any sensor failure cases.”

Including radar sensors in the vehicle’s perception system allowed it to measure the motion of objects directly, which is not possible with light detection and ranging (LiDAR) sensors. 

“Radar can help with adverse weather object detections,” adds Ren. “So, if the vehicle is operating under heavy rain or fog, the LiDAR is going to be limited, but the radio waves from radar can help the vehicle see what objects are in front and what objects are moving. This enables it to make good decisions when driving in uncertain scenarios.” 

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A post shared by aUToronto (@autoronto_uoft)

In an event where both LiDAR and radar sensors fail, the aUToronto system can still rely on visual cameras to perform object tracking. This made the team’s object tracker much more robust compared to last year when the team experienced sensor failure during a dynamic event.

Brian Cheong, a U of T Engineering master’s student who has been a member of aUToronto since 2021, acted as technical director of the autonomy team this year – part of a new leadership structure introduced by Cui.  

“In the past, it was a lot of work for our team’s principal to keep track of all the systems,” Cheong says. “So instead of having to work directly with all 15 sub teams, Kelvin created groups of sub teams that we called stacks, and each stack had a director.”

The restructuring and technical innovations paid off, with aUToronto completing its first clean sweep in the AutoDrive Challenge II, placing first in all static and dynamic events, including the concept design presentation and intersection challenge.

“The intersection challenge was a big highlight for us,” says Cheong. “Kelvin and Robert were in the car, and I was on the sidelines watching with the rest of the team. Each time we saw an obstacle – a stop sign, a red light, the railroad bar coming down – and the car reacted by stopping and then continuing, we let out a big cheer or a sigh of relief.

“And then we were all silent as the car approached the final obstacle, which was a deer. We watched as Artemis slowed down to a stop and the deer moved by. Then we screamed and cheered, and we could hear cheering from inside the car.”

“Our success is entirely a team effort,” adds Cui. “It was not smooth sailing before the competition. The only reason we won is because everybody put in so much effort to test our vehicle every day.

“That’s how we were able to get this reliable system across the line.” 

What makes a chess move brilliant? Researchers use AI to find out

Christopher.Sorensen — Wed, 07 Aug 2024 17:25:51 +0000

What makes a chess move brilliant? Researchers use AI to find out

Christopher.Sorensen Wed, 08/07/2024 - 13:25

Cutline

U of T Engineering researchers Kamron Zaidi, left, and Michael Guerzhoy, right, use game trees and deep neural networks to enable chess engines to recognize brilliant moves (photo by Safa Jinje)

Safa Jinje

Topic

Breaking Research

Alumni

Artificial Intelligence

Faculty of Applied Science & Engineering

Mechanical & Industrial Engineering

Research & Innovation

Subheadline

AI system developed by U of T researchers is being used to study human creativity and make a chess computer that is more entertaining to play against

Researchers at the University of Toronto have designed a new AI model that understands how humans perceive creativity in chess.

In a recent paper presented at an international conference, researchers in U of T’s Faculty of Applied Science & Engineering describe how they used techniques such as game trees and deep neural networks to enable chess engines to recognize brilliant moves.

The development could lead to chess engines that can find the most creative and clever path to victory in game, rather than just making moves to maximize win rates. That, in turn, could have implications for other AI systems tasked with creative endeavours.

“A chess move can be perceived as brilliant, or creative, when the strategic payoff isn’t clear at first, but in retrospect the player had to follow a precise path in gaming out all the possibilities to see so far into the future,” says paper co-author Michael Guerzhoy, an assistant professor, teaching stream, of mechanical and industrial engineering and engineering science who wrote about the research on his Substack.

“We wanted our system to understand human perception of what constitutes brilliance in chess and distinguish that from just winning.”

Most of the current research into chess AI is focused on enabling moves that create a higher chance of winning. But this doesn’t always make for an exciting game.

Skilled human chess players, on the other hand, can play in a more dramatic or imaginative way by making moves that may break traditional rules –for example, sacrificing a piece in a way that may initially look like a mistake, but ultimately, paves the way to a win.

A chess board depicts a move from the “Game of the Century” in 1956, when future American chess grandmaster Bobby Fischer (black) sacrificed his queen in a move that was celebrated as brilliant (photo by Safa Jinje)

The team worked with Leela Chess Zero, a top chess engine that learns through self-play and has played over 1.6 billion games against itself. They also employed Maia, a human-like neural network chess engine developed by U of T computer science researchers.

“We used the two neural network chess engines to create our game trees at different levels of depth in a game,” says paper co-author Kamron Zaidi, a recent U of T Engineering graduate.

“Using these game trees, we extracted many different features from it. We then fed the features into a neural network that we trained on the Lichess database of online chess games, which are labelled by human users of the database.”

A game tree in chess represents the current state of a chess board along with all the possible moves and counter moves that can occur. Each board position is represented as a node and the game tree can be expanded on until the game is either won, drawn or lost.

The researchers began with small game trees then slowly increased the size, adding more nodes to the tree. They found that when the neural network looks at all the game tree features and makes a prediction as to whether the move is brilliant or not, it reached an accuracy rate of 79 per cent using the test data set.

The research – based on Zaidi’s undergraduate engineering science thesis, which was supervised by Guerzhoy – was presented at the International Conference on Computational Creativity in Jönköping, Sweden.

“There were people from all over the world presenting research on more traditional aspects of creativity, but we were all focused on the same thing, which is, ‘How can we use AI to enhance our interactions and understandings of creativity?’” says Zaidi. 

The work has also received media coverage in outlets, including New Scientist, where English chess grandmaster Matthew Sadler says that a model that can understand brilliance could be used as a training tool for professionals and potentially lead to a more entertaining engine opponent for amateur players.

The team sees their system as having broad applicability when it comes to perception of creativity and brilliance.

“One of the biggest areas that is of interest to me is characterizing what we perceive as creativity,” says Guerzhoy.

“Not just in board games but in other creative endeavours, including music and art, where there is a formal framework and rules that need to be followed. Highly creative work involves planning in advance and gaming out the possibilities.

“But everyone I’ve talked to since the paper came out wants to know when they can play against our brilliant chess engine. So, I think making that possible is the obvious next step for us.”

U of T researchers design new method of recycling steel that could reduce industry's carbon footprint

rahul.kalvapalle — Fri, 26 Jul 2024 16:09:57 +0000

U of T researchers design new method of recycling steel that could reduce industry's carbon footprint

rahul.kalvapalle Fri, 07/26/2024 - 12:09

Cutline

PhD candidate Jaesuk Paeng (left) and Professor Gisele Azimi from the department of chemical engineering and applied chemistry in U of T's Faculty of Applied Science & Engineering display an electrochemical cell that's vital to their novel steel-recycling method (photo by Safa Jinje)

Safa Jinje

Topic

Breaking Research

Chemical Engineering

Faculty of Applied Science & Engineering

Materials Science

Research & Innovation

Sustainability

Subheadline

“Our study is the first reported instance of electrochemically removing copper from steel and reducing impurities to below alloy level”

Researchers at the University of Toronto’s Faculty of Applied Science & Engineering have designed a novel way to recycle steel that could help decarbonize several manufacturing industries and usher in a circular steel economy.

The new method introduces an innovative oxysulfide electrolyte for electrorefining, an alternative way of removing copper and carbon impurities from molten steel. The process also generates liquid iron and sulfur as by-products.

It’s outlined in a new paper published in Resources, Conservation and Recycling and co-authored by Jaesuk (Jay) Paeng, a PhD candidate in the department of chemical engineering and applied chemistry, William Judge, a PhD alum from the department of materials science and engineering, and Professor Gisele Azimi from the department of chemical engineering and applied chemistry.

“Our study is the first reported instance of electrochemically removing copper from steel and reducing impurities to below alloy level,” says Azimi, who holds the Canada Research Chair in Urban Mining Innovations.

Currently, only 25 per cent of steel produced comes from recycled material. But the global demand for greener steel is projected to grow over the next two decades as governments around the world endeavour to achieve net-zero emission goals.

Steel is created by reacting iron ore with coke – a prepared form of coal – as the source of carbon and blowing oxygen through the metal produced. Current processes generate nearly two tonnes of carbon dioxide per tonne of steel produced, making steel production one of the highest contributors to carbon emissions in the manufacturing sector.

Traditional steel recycling methods use an electric arc furnace to melt down scrap metal. Since it is difficult to physically separate copper material from scrap before melting, the element is also present in the recycled steel products.

“The main problem with secondary steel production is that the scrap being recycled may be contaminated with other elements, including copper,” says Azimi.

“The concentration of copper adds up as you add more scrap metals to be recycled, and when it goes above 0.1 weight percentage in the final steel product, it will be detrimental to the properties of steel.”

Copper cannot be removed from molten steel scrap using the traditional electric arc furnace steelmaking practice, so this limits the secondary steel market to producing lower-quality steel product, such as reinforcing bars used in the construction industry.

“Our method can expand the secondary steel market into different industries,” says Paeng. “It has the potential to be used to create higher-grade products such as galvanized cold rolled coil used in the automotive sector, or steel sheets for deep drawing used in the transport sector.”

To remove copper from iron to below 0.1 weight percentage, the team had to first design an electrochemical cell that could withstand temperatures up to 1,600 degrees Celsius.

Inside the cell, electricity flows between the negative electrode (cathode) and positive electrode (anode) through a novel oxysulfide electrolyte designed from slag — a waste derived from steelmaking that often ends up in cement or landfills.

“We put our contaminated iron that has the copper impurity as the anode of the electrochemical cell,” says Azimi. “We then apply an electromotive force, which is the voltage, with a power supply and we force the copper to react with the electrolyte.”

“The electrolyte targets the removal of copper from the iron when we apply electricity to the cell,” adds Paeng. “When we apply electricity on the one side of the cell, we force the copper to react with the electrolyte and come out from iron. At the other end of the cell, we simultaneously produce new iron.”

Azimi’s lab collaborated on the research with Tenova Goodfellow Inc., a global supplier of advanced technologies, products and services for metal and mining industries, where study co-author Judge works as a senior research and development engineer.

Looking forward, the team wants to enable the electro-refining process to remove other contaminants from steel, including tin.

“Iron and steel are the most widely used metals in the industry, and I think the production rate is as high as 1.9 billion tonnes per year,” says Azimi.

“Our method has great potential to offer the steelmaking industry a practical and easily implementable way to recycle steel to produce more of the demand for high-grade steel globally.” 

Study identifies sources of indoor air pollution in Toronto subway system

Christopher.Sorensen — Fri, 05 Jul 2024 19:02:08 +0000

Study identifies sources of indoor air pollution in Toronto subway system

Christopher.Sorensen Fri, 07/05/2024 - 15:02

Cutline

A study led by researchers at the Faculty of Applied Science & Engineering identified friction braking – used in the Toronto subway system's Line 2 – as having a significant influence on indoor air quality (photo by Roberto Machado Noa/UCG/Universal Images Group via Getty Images)

Safa Jinje

Topic

Breaking Research

Chemical Engineering

Faculty of Applied Science & Engineering

Research & Innovation

Subheadline

Wear of train wheels and rails - caused by braking - was found to be a key cause of particulate pollution along the Toronto Transit Commission (TTC) subway system

Research carried out by University of Toronto experts in partnership with Health Canada has identified braking of trains – and resulting wear of wheels and rails – as the major cause of particulate pollution in Toronto’s subway system.

For the study, which was published in Transportation Research Part D: Transport and Environment, researchers measured the chemical composition of particulate matter, which refers to fine particles of airborne solids or liquids that are smaller than 2.5 micrometres per cubic metre of air, and coupled this with modelling.

They found that most of the particulate pollution was coming from wheels and rails when brakes were applied – a discovery that marks an important step towards improving indoor air quality along the Toronto Transit Commission (TTC)'s subway system.

“Our early results pointed to the brake pads themselves as being the major cause of the emissions. However, we were surprised to find that the main source of this indoor air pollution is wear of wheels and rails during braking, rather than coming from the brake pads,” says Greg Evans, a professor in the department of chemical engineering and applied chemistry in the Faculty of Applied Science & Engineering, who led the study alongside PhD alum Keith Van Ryswyk, a senior air pollution exposure researcher at Health Canada.

“The amount of wear is influenced by the degree of braking applied, that is, how quickly the trains come into the station.

“We can’t replace the wheels and rails across the entire system, but if we can change the way that drivers apply the brakes, so they aren’t hit as hard or as often, that offers an interim way to reduce the emissions.”

A: View of a track bed with running and contact third rails. B: Close-up of a train bogie with brake and wheel contact. C: Full view of train bogie with wheels, brake pads and contact shoe. (image courtesy of Keith Van Ryswyk)

The study continues research published in 2021 which found that concentrations of particulate matter in 2018 had increased in the TTC’s Line 2 along Bloor-Danforth, while Line 1 along Yonge-University saw a drop in emissions.

Braking technology has a significant influence on emissions, Evans said, noting the TTC’s Line 2 uses older trains that are nearing the end of their 30-year design life cycle and reduce speed through regenerative and friction braking, whereas Line 1 has a fleet of newer trains which largely use regenerative braking to convert the train’s energy back into electricity.

“On Line 1, the braking is mostly regenerative, which involves no direct physical friction contact between the brake materials themselves,” says Evans. “They are also putting in automatic train control on Line 1, a system where braking is automated, which further reduces friction braking. These are all positive steps, but Line 2 has not benefitted from these changes yet.”

While the adverse health effects of outdoor particulate matter have been well established, the consequences of inhaling particles in subways are not as clear.

“The particulate matter in the subway is actually very different from what we find in ambient, outdoor pollution,” Evans says. “It’s very metal-rich and mostly made up of iron. So, there is good reason to think that it may be more hazardous.” 

Beyond reducing emissions, improving ventilation is the second way to improve air quality on subway trains and platforms.

Subway systems in cities such as Montreal and Barcelona use continuous mechanical ventilation for cooling, which also results in lower levels of particulate pollutant concentrations. But Toronto’s system uses limited forced ventilation, says Evans.

“It really relies on trains pushing the air like a piston as they go through the tunnels. And eventually the trains come to an open area, where the train goes outside and pushes the contaminated air out with it, which is what provides most of the ventilation,” he says.

Evans hopes these new findings will not only accelerate the technological changes needed to improve indoor air quality on Line 2, but also influence the plans for new subway lines in Toronto, such as the Ontario Line, and support the design of subways in other cities across the globe.

“We hope this work will help design better subway lines given that so much valuable work is going into creating better transit systems,” says Evans.

“Good transit is central to both decarbonization and the smooth operation of modern cities. It’s important for transit systems like subways to provide a healthy environment rather than expect passengers themselves to take precautionary steps in response to poor air quality.”

U of T's top undergraduate says he fell in love with the process of learning

rahul.kalvapalle — Tue, 25 Jun 2024 14:13:32 +0000

U of T's top undergraduate says he fell in love with the process of learning

rahul.kalvapalle Tue, 06/25/2024 - 10:13

Cutline

Jeff Chen achieved perfect grades in 17 of his 46 courses on his way to graduating with a bachelor of applied science in civil engineering (photo by Bronson Lo)

Safa Jinje

Topic

Our Community

Convocation 2024

Faculty of Applied Science & Engineering

Subheadline

Jeff Chen was awarded the 2024 John Black Aird Scholarship as the top student from an undergraduate degree program at U of T

Jeff Chen says he's always prioritized his academics, but what really elevated his performance to the next level was pouring his energies into the process of learning and self-improvement – rather than obsessing over the outcome.

“In my first year, I spent a lot of time anticipating the marks I would need to get a 4.0 GPA,” says Chen. “I stopped doing that in second year and cared more about the effort I put in to get the result.”

The approach bore fruit. Chen graduated this June with his bachelor of applied science in civil engineering, achieving 100 per cent in 17 of his 46 courses for an overall average of 96.5 per cent – the highest among his cohort of graduates across U of T’s three campuses.

His performance earned him the 2024 John Black Aird Scholarship, which is awarded annually to the top student from an undergraduate degree program at U of T. His stellar academic record also led to him receiving a Governor General’s Silver Medal.

“This honestly wasn’t something I was expecting but I am immensely grateful,” says Chen. “I am treating these awards as a culmination of four years of hard work.”

Chen adopted a methodical process to course work, starting his mornings by writing a list of what he wanted to accomplish that day. The items would be small, such as studying lecture notes for 15-minute increments, and he preferred to spread assignments and projects across several days. “I found that breaking things up helped me not get distracted and everything was continually fresh in my mind,” he says.

Chen also applied the same approach to his pastimes, which include playing chess, hiking and working out. “My hobbies allow for me to set and fulfill goals in the same way I approach my academics,” he says. “I’ve recently gotten into powerlifting, and I always aim to hit higher weights for the squat, bench and deadlift.”

One of the highlights of Chen’s undergraduate career at U of T was his fourth-year thesis, which explored the use of deep neural networks – a form of artificial intelligence – to predict the behaviour of structures during earthquakes.

The research focused on the seismic response of structural systems that exhibit non-linear stress-strain behaviour when subjected to strong forces. “Under an earthquake, dynamic forces on the structure are often strong enough that they enter this non-linear range, which has complex, difficult-to-predict behaviour,” Chen says.

The work, supervised by Professor Oh-Sung Kwon of the department of civil and mineral engineering, began when Chen received an undergraduate summer research award from the Natural Sciences and Engineering Research Council of Canada (NSERC).

“The thesis project was completely open-ended and self-directed, unlike my course work, where outcomes are determined by the professors,” Chen says. “I got to explore the intersection between artificial intelligence and structural engineering, and how we can utilize deep neural networks to design better and more resilient structures in the case of disasters.

“I really enjoyed the challenge of working on this research, so I spent a lot of time on it.”

This fall, Chen will take on his next academic challenge as a law student at the University of California, Berkeley. He says he’s looking forward to gaining foundational legal knowledge during his first year of law school before committing to a specific area of law. “I am keeping my options open, but I know my engineering degree will be useful, especially if I go into intellectual property law, since working on patents can be very technical.”

Reflecting on his time at U of T, Chen says he hopes his experiences will inspire other students to enjoy the journey of higher learning.

“U of T has opened many doors for me and there are many paths I can take going forward.”

U of T researchers enhance object-tracking abilities of self-driving cars

rahul.kalvapalle — Wed, 29 May 2024 14:59:42 +0000

U of T researchers enhance object-tracking abilities of self-driving cars

rahul.kalvapalle Wed, 05/29/2024 - 10:59

Cutline

Sandro Papais, a PhD student, is the co-author of a new paper that introduces a graph-based optimization method to improve object tracking for self-driving cars (photo courtesy of aUToronto)

Safa Jinje

Topic

Breaking Research

Artificial Intelligence

Faculty of Applied Science & Engineering

Subheadline

The new tools could help robotic systems of autonomous vehicles better track the position and motion of vehicles, pedestrians and cyclists

Researchers at the University of Toronto Institute for Aerospace Studies (UTIAS) have introduced a pair of high-tech tools that could improve the safety and reliability of autonomous vehicles by enhancing the reasoning ability of their robotic systems.

The innovations address multi-object tracking, a process used by robotic systems to track the position and motion of objects – including vehicles, pedestrians and cyclists – to plan the path of self-driving cars in densely populated areas.

Tracking information is collected from computer vision sensors (2D camera images and 3D LIDAR scans) and filtered at each time stamp, 10 times a second, to predict the future movement of moving objects.

“Once processed, it allows the robot to develop some reasoning about its environment. For example, there is a human crossing the street at the intersection, or a cyclist changing lanes up ahead,” says Sandro Papais, a PhD student in UTIAS in the Faculty of Applied Science & Engineering. "At each time stamp, the robot’s software tries to link the current detections with objects it saw in the past, but it can only go back so far in time.”

In a new paper presented at the 2024 International Conference on Robotics and Automation in Yokohama, Japan, Papais and co-authors Robert Ren, a third-year engineering science student, and Professor Steven Waslander, director of UTIAS’s Toronto Robotics and AI Laboratory, introduce Sliding Window Tracker (SWTrack) – a graph-based optimization method that uses additional temporal information to prevent missed objects.

The tool is designed to improve the performance of tracking methods, particularly when objects are occluded from the robot’s point of view.

A visualization of a nuScenes dataset used by the researchers. The image is a mosaic of the six different camera views around the car with the object bounding boxes rendered overtop of the images (image courtesy of the Toronto Robotics and AI Laboratory)

“SWTrack widens how far into the past a robot considers when planning,” says Papais. “So instead of being limited by what it just saw one frame ago and what is happening now, it can look over the past five seconds and then try to reason through all the different things it has seen.” 

The team tested, trained and validated their algorithm on field data obtained through nuScenes, a public, large-scale dataset for autonomous driving vehicles that have operated on roads in cities around the world. The data includes human annotations that the team used to benchmark the performance of SWTrack.

They found that each time they extended the temporal window, to a maximum of five seconds, the tracking performance got better. But past five seconds, the algorithm’s performance was slowed by computation time.

“Most tracking algorithms would have a tough time reasoning over some of these temporal gaps. But in our case, we were able to validate that we can track over these longer periods of time and maintain more consistent tracking for dynamic objects around us,” says Papais.

Papais says he’s looking forward to building on the idea of improving robot memory and extending it to other areas of robotics infrastructure. “This is just the beginning,” he says. “We’re working on the tracking problem, but also other robot problems, where we can incorporate more temporal information to enhance perception and robotic reasoning.”

Another paper, co-authored by master’s student Chang Won (John) Lee and Waslander, introduces UncertaintyTrack, a collection of extensions for 2D tracking-by-detection methods that leverages probabilistic object detection.

“Probabilistic object detection quantifies the uncertainty estimates of object detection,” explains Lee. “The key thing here is that for safety-critical tasks, you want to be able to know when the predicted detections are likely to cause errors in downstream tasks such as multi-object tracking. These errors can occur because of low-lighting conditions or heavy object occlusion.

“Uncertainty estimates give us an idea of when the model is in doubt, that is, when it is highly likely to give errors in predictions. But there’s this gap because probabilistic object detectors aren’t currently used in multi-tracking object tracking.” 

Lee worked on the paper as part of his undergraduate thesis in engineering science. Now a master’s student in Waslander’s lab, he is researching visual anomaly detection for the Canadarm3, Canada’s contribution to the U.S.-led Gateway lunar outpost. “In my current research, we are aiming to come up with a deep-learning-based method that detects objects floating in space that pose a potential risk to the robotic arm,” Lee says.

Waslander says the advancements outlined in the two papers build on work that his lab has been focusing on for a number of years.

“[The Toronto Robotics and AI Laboratory] has been working on assessing perception uncertainty and expanding temporal reasoning for robotics for multiple years now, as they are the key roadblocks to deploying robots in the open world more broadly,” Waslander says.

“We desperately need AI methods that can understand the persistence of objects over time, and ones that are aware of their own limitations and will stop and reason when something new or unexpected appears in their path. This is what our research aims to do.”