Screens on cell phones, laptops and tablets are challenged on a daily basis: that 9 a.m. coffee spill, the splattering of oil in the kitchen or maybe even a drop in the mud.
While waterproof glass exists, some accidental thrills and spills beyond water make a mess that can cause fogging and damage.
To meet these challenges, researchers from the University of Pittsburgh Swanson School of Engineering and the California-based software company SigOpt took inspiration from the wings of the glasswing butterfly and used machine learning to test out different design options. The result is a nanostructured glass that’s not only durable, but very clear, anti-fogging and liquid resistant. The research results were published in the journal Materials Horizons this summer.
“The glass is superomniphobic, meaning it repels a wide variety of liquids,” said Sajad Haghanifar, lead author of the research and doctoral candidate in industrial engineering at Pitt. “The glass is also anti-fogging, as water condensation tends to easily roll off the surface, and the view through the glass remains unobstructed. Finally, the nanostructured glass is durable from abrasion due to its self-healing properties — abrading the surface with a rough sponge damages the coating, but heating it restores it to its original function.”
The glass has random nanostructures smaller than the wavelengths of visible light. This allows the glass to have a very high transparency — 99.5% — when the random nanostructures are on both sides of the glass.
This high transparency can reduce the brightness and power demands on displays that could, for example, extend battery life. The glass is antireflective across higher angles, improving viewing from different directions. The glass also has low haze — less than 0.1% — which results in very clear images and text.
Natural surfaces like lotus leaves, moth eyes and butterfly wings display omniphobic properties which render them self-cleaning, bacteria resistant and water repellant — adaptations for survival that evolved over millions of years. Researchers have long sought inspiration from nature to replicate these properties in a synthetic material, and even to improve upon them.
While the team could not rely on evolution to achieve these results, they instead used machine learning for the final product.
“When you create something like this, you don’t start with a lot of data, and each trial takes a great deal of time. We used machine learning to suggest variables to change, and it took us fewer tries to create this material as a result,” said Paul Leu, associate professor of industrial engineering at the Swanson School, whose lab conducted the research. Leu also holds secondary appointments in the departments of mechanical engineering and materials science, and chemical and petroleum engineering.
“Machine learning and AI strategies are only relevant when they solve real problems; we are excited to be able to collaborate with the University of Pittsburgh to bring the power of Bayesian active learning to a new application,” said Bolong Cheng, PhD, research engineer at SigOpt.
“The collaboration epitomizes SigOpt’s vision, 'Empower the World's Experts;' the University of Pittsburgh wants to develop new materials to have a positive impact on the world, and we are thrilled to be able to help,” said Michael McCourt, a fellow engineering researcher at SigOpt.
The project was supported in part by a National Science Foundation CAREER Award. The Pitt team is now turning its attention to developing a commercially viable process to manufacture the new glass.