Greening Our World Through Automation

Clean tech is in high demand, but optimizing key parts used to take years or decades. Automation and AI are shrinking that window.

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Curtis Berlinguette is a chemist working on the world’s first self-driving robot to help make new materials for clean technologies. He co-leads Project Ada, a group with the goal to accelerate development so that experiments that used to take decades can be done in weeks. His main target is to optimize thin films.

“Thin film materials are important to the clean tech industry because you require thin films in solar cells, you require them in batteries, you need them in more efficient windows,” explains Berlinguette, professor of chemistry at the University of British Columbia’s Stewart Blusson Quantum Matter Institute.

But optimizing new thin films and getting them onto the market can take over 20 years, says Berlinguette. Without them, clean energy isn’t keeping pace with demand. Automating their development helps scientists make smarter and faster decisions, and hopefully this can accelerate these long timelines.

To accomplish these goals, Berlinguette uses a combination of artificial intelligence and automation to make routine decisions and work around the clock to crank out results faster.

“Experiments that would have taken us a year do to just a short time ago, we can now do in a period of hours or maybe a couple of days at most,” adds Berlinguette. “So we’re really accelerating the development process with these materials.”

Better thin films would mean more stable solar cells that are easier to manufacture, driving down their cost and helping tip the economics towards sustainable energy. Everything from energy capture to storage relies on them.

Berlinguette also envisions electrochromic windows that can transition seamlessly between clear and tinted states, helping save energy on heating and cooling. His new fabrication methods make it possible to create uniform films without requiring high vacuums or sophisticated equipment.

“In five years’ time, these robotic tools and automated platforms are going to allow us to take a step back and ask bigger picture questions on what that next material is going to be,” adds chemist Frank Parlane, graduate student and Project Ada researcher.

Clean technology is urgently needed now. In this race against the clock, automation opens possibilities for faster adoption of more efficient and sustainable options, clearing a roadblock to a greener future.

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Curtis Berlinguette is a CIFAR Fellow and Professor of Chemistry and Chemical & Biological Engineering at the University of British Columbia. Berlinguette leads a multi-disciplinary program developing ways to use flexible automation and machine learning to accelerate the discovery and deployment of clean energy technologies. His program also studies the fundamental science of electron transfer during catalysis all the way through to the design and scale-up of CO2 utilization technologies. His program also likes to work on high risk, high impact clean energy projects like cold fusion.


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