Artificial active

The artificial soft surface independently imitates s

Video: Watch this thin, flexible material learn to mimic ocean waves and flex palms in real time. Relying on electromagnetic actuation, mechanical modeling and machine learning to form new configurations, the surface can even learn to adapt to obstacles such as broken elements, unexpected stresses or changing environments.
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Credit: Véronique Koch, Duke University

DURHAM, NC — Engineers at Duke University have developed a scalable soft surface that can constantly reshape itself to mimic objects in nature. Relying on electromagnetic actuation, mechanical modeling and machine learning to form new configurations, the surface can even learn to adapt to obstacles such as broken elements, unexpected stresses or changing environments.

The research appears online September 21 in the journal Nature.

“We are driven by the idea of ​​controlling material properties or mechanical behaviors of an engineering object on the fly, which could be useful for applications such as soft robotics, augmented reality, biomimetic materials and subject-specific wearables,” said Xiaoyue Ni, assistant professor of mechanical engineering and materials science at Duke. “We focus on engineering the shape of the material that has not been predetermined, which is quite a difficult task to achieve, especially for soft materials.”

Previous work on matter morphing, according to Ni, has generally not been programmable; it was programmed instead. That is, soft surfaces equipped with engineered active elements can change shape between a few shapes, such as a piece of origami, in response to light or heat or other stimuli triggers. By contrast, Ni and his lab wanted to create something much more controllable that could morph and reconfigure itself as often as it wished into any physically possible form.

To create such a surface, the researchers started by laying out a grid of snake-like beams made of a thin layer of gold encapsulated by a thin layer of polymer. The individual bundles are only eight micrometers thick, about the thickness of a cotton fiber, and less than a millimeter wide. The lightness of the beams allows magnetic forces to deform them easily and quickly.

To generate local forces, the surface is placed in a low level static magnetic field. Voltage changes create a complex but easily predictable electrical current along the golden grid, causing the grid to move out of plane.

“This is the first soft artificial surface fast enough to accurately mimic a continuous shape-changing process in nature,” Ni said. “A key advancement is the structural design that allows for an unusual linear relationship between electrical inputs and the resulting shape, making it easy to understand how to apply voltages to achieve a wide variety of target shapes.”

The new “metasurface” shows a wide range of morphing and imitation skills. It creates bulges that rise and move around the surface like a cat trying to find its way under a blanket, oscillating wave patterns, and a convincing replica of a drop of liquid dripping and falling onto a solid surface. And it produces those shapes and behaviors at any speed or acceleration you want, which means it can reimagine that tricked-out cat or drop-out in slow motion or fast-forward.

With cameras monitoring the morphing surface, the contortionist surface can also learn to recreate shapes and patterns on its own. By slowly adjusting the applied voltages, a learning algorithm takes into account 3D imaging feedback and determines the effects of different inputs on the shape of the metasurface.

In the article, a human palm speckled with 16 black dots moves slowly under a camera, and the surface perfectly reflects the movements.

“The control doesn’t need to know anything about material physics, just take small steps and watch to see if it’s getting close to the target or not,” Ni said. “It currently takes about two minutes to get a new shape, but eventually we hope to improve the feedback system and learning algorithm to the point where it’s near real-time.”

Because the surface learns to move on its own through trial and error, it can also adapt to damage, unexpected physical stresses or environmental changes. In one experiment, he quickly learns to mimic a bulging mound despite having one of its beams cut. In another, he manages to mimic a similar shape despite having a weight attached to one of the grid nodes.

There are many immediate opportunities to expand the scale and configuration of the soft surface. For example, a surface table can scale the size to that of a touch screen. Or manufacturing techniques with higher precision can reduce the size to one millimeter, making it more suitable for biomedical applications.

In the future, Ni wants to create robotic metasurfaces with built-in shape-sensing functions to mimic the shape of complex and dynamic surfaces in nature in real time, such as water ripples, fish fins, or the human face. The lab may also consider integrating more components into the prototype, such as on-board power sources, sensors, computing resources, or wireless communication capabilities.

“Along with pursuing the creation of programmable and robotic materials, we envision that future materials may change to perform functions dynamically and interactively,” Ni said. “These materials can sense and perceive users’ requirements or information, and transform and adapt according to the real-time needs of their specific performance, just like the microbots in Big Hero 6. The soft surface can find applications as a teleoperated robot, dynamic 3D display, camouflage, exoskeleton or other intelligent and functional surfaces that can operate in harsh and unpredictable environments.

The research was supported by the National Science Foundation (CMMI 16-35443). This work was performed in part at the Duke University Shared Materials Instrumentation Facility (SMIF), a member of the North Carolina Research Triangle Nanotechnology Network (RTNN), which is supported by the National Science Foundation (ECCS-2025064).

QUOTE: “A dynamically reprogrammable metasurface with self-evolving shape morphing”, Yun Bai, Heling Wang, Yeguang Xue, Yuxin Pan, Jin-Tae Kim, Xinchen Ni, Tzu-Li Liu, Yiyuan Yang, Mengdi Han, Yonggang Huang , John A. Rogers and Xiaoyue Ni. NatureSeptember 21, 2022. DOI: 10.1038/s41586-022-05061-w

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