Researchers at North Carolina State University have developed a super-stretchable material that repels nearly all liquids and withstands extreme deformation.
The research team used laser ablation instead of chemical spray coating to build a liquid-repellent surface, eliminating the need for strong solvents.
The material maintains its superphobic properties even after being stretched to five times its original length and undergoing more than 5,000 stretch-release cycles.
This means it can withstand repeated pulling, bending, and twisting without losing its ability to repel liquids.
“Superhydrophobic materials can repel virtually any liquid, including very strong acids, bases, and solvents, just as they repel water,” said Arun Kumar Kota, associate professor of mechanical and aerospace engineering at North Carolina State University.
“They are useful in a wide range of applications, including soft robots that require materials that can withstand harsh environments, stretch, and change shape.”
Superphobic surfaces are typically created by spray coating materials with a solvent containing nanoparticles. The coating forms a rough texture that prevents liquid from adhering. However, these coatings tend to delaminate when stretched beyond 100% strain, limiting their use in flexible systems.
Breaking through the barrier of delamination
In previous work, Kota’s group addressed this problem by adding microprotrusions, small pillars 10 to 100 microns wide, before applying the spray coating.
The coating between the pillars peeled off under stress, but the tops of the pillars remained protected.
“It’s a rough analogy, but think of your outstretched arm as a material and your hair as a microscopic protrusion,” says Kota.
“My hair doesn’t feel stressed and unaffected by my arm being pulled. We found that the spray-coated material with microprojections was superphobic up to five times its initial length.”
In the new study, the team removed the spray coating completely.
“In this work, we use laser ablation instead of spray coating to create both microscopic protrusions and a rough surface that creates superphobicity,” Kota continues.
“But first we had to determine the optimal parameters of the laser: power, speed, and spatial frequency, or the number of laser pulses per unit length.”
Because laser ablation has millions of possible parameter combinations, researchers turned to machine learning.
They input the laser power, speed, spatial frequency, and desired slip angle into the model to predict the optimal settings and avoid long trial-and-error cycles.
machine learning guided laser
The optimized process was tested on a siloxane elastomer modified with a fluorocarbon silane to increase its hydrophobicity.
The resulting surfaces remained superhydrophobic through up to 400% strain and over 5,000 stretching cycles. The research team also analyzed how stretching affected contact angle, breakthrough pressure, and sliding angle.
“We have created a platform to create stretchable, superphobic materials without using chemical solvents or requiring hundreds of thousands of trial-and-error experiments,” Cota says.
“This method is a greener and more cost-effective way to produce materials for a variety of applications, from textile coatings to stretchable electronics that can be used in chemically harsh environments.”
The researchers say this approach could support the development of artificial skin, soft robotics, wearable electronics and textile protective coatings designed for harsh conditions.
The study was published in the journal Case.
