QD Solar, a Canadian startup co-founded by U of T researchers Ted Sargent and Sjoerd Hoogland, has received a big boost in funding and an important international nod to help bring its solar technology to market.
On Monday, the company announced it had closed its first significant round of venture capital financing led by DSM Venturing, based in the Netherlands, with participation from existing investors, KAUST Innovation Fund and MaRS Innovation.
Coupled with the $2.55 million the company received from Sustainable Development Technology Canada last March, QD Solar “has the resources to advance, develop, test and de-risk our solar technology, while concurrently developing the manufacturing processes needed to bring this technology to market,” said Dan Shea, CEO of QD Solar and a former senior executive with Celestica and BlackBerry, in a news release.
QD Solar’s quantum dot-based solar cells use nano-engineered, low-cost materials that can absorb otherwise wasted infrared light. Solar panels with this technology can boost their overall power generation by 20 per cent, the company says.
Benjamin Alarie, who is both the Osler Chair in Business Law at the University of Toronto and founder of Blue J Legal, says he wants to create the equivalent of a racing video game for the legal profession – an ultra-realistic simulation that can help lawyers and accountants get a good sense of what they might experience in a real courtroom. Tax Foresight, the company’s first product in that vein, went on sale last month.
The subscription software uses artificial intelligence to scan legal documents, case files and decisions to create a mock judgement. Users can thus get a more accurate assessment of cases and a prediction of how a particular dispute might go.
It’s an improvement over existing methods, Alarie says. The rapidly multiplying number of cases and documents is outstripping human researchers’ own capabilities, so tax professionals are having trouble making accurate judgement predictions.
“We use shortcuts, like looking at leading cases,” he says. “The software should actually take into account all the information from the case law. Rather than looking at the tallest trees in the forest, we want to take into account all of the trees.”
A new set of machine learning algorithms developed by U of T researchers that can generate 3D structures of tiny protein molecules may revolutionize the development of drug therapies for a range of diseases from Alzheimer’s to cancer.
“Designing successful drugs is like solving a puzzle,” says U of T PhD student Ali Punjani, who helped develop the algorithms. “Without knowing the three-dimensional shape of a protein, it would be like trying to solve that puzzle with a blindfold on.”
The ability to determine the 3D atomic structure of protein molecules is critical in understanding how they work and how they will respond to drug therapies, notes Punjani.
Drugs work by binding to a specific protein molecule and changing its 3D shape, altering the way it works once inside the body. The ideal drug is designed in a shape that will only bind to a specific protein or proteins involved in a disease while eliminating side effects that occur when drugs bind to other proteins in the body.