Big Data and Synthetic Chemistry Could Fight Climate Change
May 15, 2020 | College of Arts and Sciences
Laura Murdock ’16 likes the thrill of creative chemistry ― making new materials that could, in turn, make a difference in the world.
After finishing her undergraduate degree in chemistry at South Carolina, she came back to earn a PhD and do research in synthetic chemistry. “I felt like it was something impactful,” she says. “You can make something that would look very small on paper, but actually makes a big difference.”
One of her latest creations is like that. It’s a thin, yellow-brown film. A small piece that she stretches between her gloved hands in the lab looks like old plastic wrap. Actually, it’s the world’s best known material for filtering carbon dioxide and methane from each other. And the method she used to make it could help materials scientists fight climate change or reduce pollution.
The new membrane resulted from teamwork by Murdock; her advisor, Brian Benicewicz, the University of South Carolina SmartState Chair in Polymer Nanocomposite Research; and scientists at Columbia University in New York. They wanted to develop a better way to design and make membranes that separate gases. Those materials could be used to filter greenhouse gases and pollutants, but they are generally designed and tested with a trial-and-error approach.
Their new method, published today in Science Advances, uses big data to improve the process.
It removes the guesswork and the old trial-and-error work, which is very ineffective. You don't have to make hundreds of different materials and test them.
― Brian Benicewicz, SmartState Professor
Benicewicz explained that gas-filtering membranes suffer from a tradeoff between selectivity and permeability ― a material that lets one gas through is unlikely to stop a molecule of another gas. “We're talking about some really small molecules,” Benicewicz said. “The size difference is almost imperceptible. If you want a lot of permeability, you're not going to get a lot of selectivity."
The team at Columbia University created a machine learning algorithm that analyzed the chemical structure and effectiveness of existing membranes used for separating carbon dioxide from methane. Once the algorithm could accurately predict the effectiveness of a given membrane, they turned the question around: What chemical structure would make the ideal gas separation membrane?
Sanat K. Kumar, the Bykhovsky Professor of Chemical Engineering at Columbia, compared it to Netflix’s method for recommending movies. Like Netflix identifies features a viewer enjoys in movies and then recommends movies that share that feature, his team’s algorithm found which chemical structures contributed to effective membranes.