New Superomniphobic Glass Soars High on Butterfly Wings Using Machine Learning
Chemical & Petroleum, Industrial, MEMS
PITTSBURGH (July 11,
2019) — Glass for technologies like displays, tablets, laptops, smartphones,
and solar cells need to pass
light through, but could benefit from a surface that repels water, dirt, oil,
and other liquids. Researchers from the University of Pittsburgh’s Swanson
School of Engineering have created a nanostructure glass that takes inspiration
from the wings of the glasswing butterfly to create a new type of glass that is
not only very clear across a wide variety of wavelengths and angles, but is
also antifogging. The team recently published a paper detailing their findings:
“Creating Glasswing-Butterfly Inspired Durable Antifogging Omniphobic
Supertransmissive, Superclear Nanostructured Glass Through Bayesian Learning
and Optimization” in Materials
Horizons (doi:10.1039/C9MH00589G).
They recently presented this work at the ICML conference in the “Climate
Change: How Can AI Help?” workshop. The nanostructured glass has random nanostructures, like the
glasswing butterfly wing, that are smaller than the wavelengths of visible
light. This allows the glass to have a very high transparency of 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 angles. The glass also has low haze, less than 0.1%, which results
in very clear images and text. “The glass is superomniphobic,
meaning it repels a wide variety of liquids such as orange juice, coffee, water, blood, and
milk,” explains Sajad Haghanifar, lead author of the paper 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.” Natural surfaces like lotus leaves, moth eyes and butterfly wings
display omniphobic properties that make them self-cleaning, bacterial-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
utilized machine learning. “Something significant about the nanostructured glass research,
in particular, is that we partnered with SigOpt to use machine learning to
reach our final product,” says Paul Leu, PhD, associate professor of industrial
engineering, whose lab conducted the research. Dr. Leu holds secondary
appointments in mechanical engineering and materials science and chemical
engineering. “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.” “Bayesian optimization and active search are the ideal tools to
explore the balance between transparency and omniphobicity efficiently, that is,
without needing thousands of fabrications, requiring hundreds of days.” said
Michael McCourt, PhD, research engineer at SigOpt. Bolong Cheng, PhD, fellow
research engineer at SigOpt, added, “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.” “Creating Glasswing-Butterfly Inspired Durable Antifogging
Omniphobic Supertransmissive, Superclear Nanostrcutured Glass Through Bayesian
Learning and Optimization” was coauthored by Sajad Haghanifar, and Paul Leu,
from Pitt’s Swanson School of Engineering; Michael McCourt and Bolong Cheng
from SigOpt; and Paul Ohodnicki and Jeffrey Wuenschell from the U.S. Department
of Energy’s National Energy Laboratory. The project was supported in part by a National
Science Foundation CAREER Award.
Maggie Pavlick