Eindhoven researchers have developed a soft robotic “hand” made from liquid crystals and graphene that could be used to design future surgical robots. The new work has just been published in the journal ACS Applied Materials & Interfaces.

In our future hospitals, soft robots might be used as surgical robots. But before that can happen, researchers need to figure out how to precisely control and move these deformable robots. Added to that, many current soft robots contain metals, which means that their use in water-rich environments—like the human body—is rather limited.

TU/e researchers led by Ph.D. candidate Laura van Hazendonk, Zafeiris Khalil (as part of his master’s research), Michael Debije, and Heiner Friedrich have designed a soft robotic hand or gripper made from graphene and liquid crystals (both organic materials). This opens the possibilities for such a device to be potentially and safely used in surgeries in the future.

Robots have an enormous influence on our world. For instance, in industry, robots build automobiles and televisions. In hospitals, robots—such as the da Vinci robotic surgical system—assist surgeons and allows for minimally invasive operations. And some of us even have robots to do our vacuum cleaning at home.

“Society has become dependent on robots, and we’re coming up with new ways to use them,” says Van Hazendonk, Ph.D. researcher in the Department of Chemical Engineering and Chemistry. “But in devising new ways to use them, we need to think about using different types of materials to make them.”

Thinking soft

The different materials that Van Hazendonk is referring to are fluids, gels, and elastic materials—which are all easily deformable. “Typically, robots are made from metals, which are rigid and hard. But in certain applications, robots made from hard and rigid materials limit the performance of the robot,” says Van Hazendonk. “The solution is to think soft.”

In soft robotics, the goal is to make robots from materials like fluids or gels that can deform in certain situations and then can act like robots made from traditional rigid and hard materials.

One area where soft robots look set to have a major impact is in surgical procedures. Van Hazendonk adds, “For a surgeon, many operations can be complex and delicate, and therefore require precise dexterity on the part of the surgeon. Sometimes this just isn’t possible, and they turn to robots.

“But rigid robots may not be able to access some areas with ease either. That’s where soft robots can come to the fore, and our goal was to offer the potential new helping hand for use in clamping and suturing used devices in surgeries, for example.”

Turning to Nobel materials

For their research, Van Hazendonk and her colleagues opted to use a different type of deformable material—liquid crystals—along with graphene to make a soft gripper device or “hand” with four controllable and deformable “fingers.”

Intriguingly, both liquid crystals and graphene are directly or indirectly connected to Nobel Prizes in Physics over the last 30 years or so. Back in 1991, Pierre-Gilles de Gennes won the prize for his work on order in complex matter, such as liquid crystals. And in 2010, Andre Geim and Konstantin Novoselov won the prize for their work on graphene—the super-strong material that is also transparent and an effective conductor of electricity and heat.

“A liquid crystal behaves as a liquid or a solid depending on how it is excited or perturbed. When it flows, it acts like a liquid. But in special situations, the molecules in the liquid can arrange themselves to create a regular pattern or structure, such as a crystal you would see in a solid material under a powerful microscope,” explains Van Hazendonk. “The ability for liquid crystal materials to act like this is perfect when it comes to making soft robots.”

Actuator challenge

With the materials selected, the researchers set out to design and make an actuator. “Actuators control and regulate motion in robotic systems. Usually, the actuator responds or moves when supplied with electricity, air, or a fluid,” says Van Hazendonk. “In our work, we turned to something else to drive liquid-crystal network (LCN) actuators.”

The researchers designed a gripper device with four ‘fingers’ controlled using LCN actuators that are deformed thanks to the effect of heat on graphene-based heating elements or tracks in the fingers of gripper or ‘hand.”

Bending of the fingers

“When electrical current passes through the black graphene tracks, the tracks heat up and then the heat from the tracks changes the molecular structure of the liquid crystal fingers and some of the molecules go from being ordered to disordered. This leads to bending of the fingers,” says Van Hazendonk. “Once the electrical current is switched off, the heat is lost, and the gripper returns to its initial state.”

One of the biggest challenges for the researchers related to the graphene heating elements as highlighted by Heiner Friedrich, assistant professor at the Department of Chemical Engineering and Chemistry.

“We needed to make sure that they would heat to the right temperature to change the liquid crystal layer, and we needed to make sure that this could be done at safe voltages. Initially, the graphene elements didn’t reach the right temperatures at safe voltages, or they would overheat and burn the device,” says Friedrich. “This and many other important problems were solved by Zafeiris Khalil during his MSc thesis.”

The researchers didn’t let this problem deter them, and in the end, they designed an actuator that can operate without any issues at voltages less than 15 volts. And in terms of performance, the grippers can lift small objects with a mass between 70 and 100 milligrams. “This might not sound like a lot, but in medical applications such as surgery, this can be useful for the exact and miniscule movement of tiny tools, implants, or biological tissue,” says Van Hazendonk.

For Van Hazendonk—who combines her Ph.D. research with being a member of the provincial parliament of Noord-Brabant (Provinciale Staten)—this research has been eye-opening for her.

She says, “I love how this work combines a useful and tangible application. The gripper device is based on fundamental technologies, but the actuator itself could form the basis for a suite of robots for use in biomedical or surgical applications in the future.”

And, in the future, Van Hazendonk and her colleagues have some interesting plans. She concludes, “We want to make a fully printed robot by figuring out a way to 3D-print the liquid-crystal layer. For our gripper, we made the layer by casting materials in a mold. Other researchers in the group of Michael Debije have shown that liquid crystals can be printed. For this gripper, we have printed the graphene layer, so it would be cool to have a fully printed device.”

When scientists pushed the world’s fastest supercomputer to its limits, they found those limits stretched beyond even their biggest expectations.

The Frontier supercomputer at the Department of Energy’s Oak Ridge National Laboratory set a new ceiling in performance when it debuted in 2022 as the first exascale system in history—capable of more than 1 quintillion calculations per second. Now researchers are learning just what heights of scientific discovery Frontier’s computational power can help them achieve.

In the latest milestone, a team of engineers and scientists used Frontier to simulate a system of nearly half a trillion atoms—the largest system ever modeled and more than 400 times the size of the closest competition—in a potential gateway to new insights across the scientific spectrum.

“It’s like test-driving a car with a speedometer that registers 120 miles per hour, but you press the gas pedal and find out it goes past 200,” said Nick Hagerty, a high-performance computing engineer for ORNL’s Oak Ridge Leadership Computing Facility, which houses Frontier.

“Nobody runs simulations at a scale this size because nobody’s ever tried. We didn’t know we could go this big.”

The results hold promise for scientific studies at a scale and level of detail not yet seen.

“Nobody on Earth has done anything remotely close to this before,” said Dilip Asthagiri, an OLCF senior computational biomedical scientist who helped design the test. “This discovery brings us closer to simulating a stripped-down version of a biological cell, the so-called minimal cell that has the essential components to enable basic life processes.”

Hagerty and his team sought to max out Frontier to set criteria for the supercomputer’s successor machine, still in development. Their mission: Push Frontier as far as it could go and see where it stopped.

The team used Frontier with the Large-scale Atomic and Molecular Massively Parallel Simulator software module, or LAMMPS, to simulate a system of room-temperature water molecules at the atomic level as they gradually increased the number of atoms.

“Water is a great test case for a machine like Frontier because any researcher studying a biological system at the atomic level will likely need to simulate water,” Hagerty said. “We wanted to see how big of a system Frontier could really handle and what limitations are encountered at this scale.

“As one of the first benchmarking efforts to use more than a billion atoms with long-range interactions, we would periodically find bugs in the LAMMPS source code. We worked with the LAMMPS developers, who were highly engaged and responsive, to resolve those bugs, and this was critical to our scaling success.”

Frontier tackles complex problems via parallelism, which means the supercomputer breaks up the computational workload across its 9,408 nodes, each a self-contained computer capable of around 200 trillion calculations per second. With each increase in problem size, the simulation demanded more memory and processing power. Frontier never blinked.

“We’re not talking about a large simulation just in terms of physical size,” Asthagiri said. “After all, a billion water molecules would fit in a cube with edges smaller than a micrometer (a millionth of a meter). We’re talking large in terms of the complexity and detail.

“These millions and eventually billions and hundreds of billions of atoms interact with every other atom, no matter how far away. These long-range interactions increase significantly with every molecule that’s added. This is the first simulation of this kind at this size.”

The simulation ultimately grew to more than 155 billion water molecules—a total of 466 billion atoms—across more than 9,200 of Frontier’s nodes. The supercomputer kept crunching the numbers, even with 95% of its memory full. The team stopped there.

“We could have gone even higher,” Hagerty said. “The next level would have been 600 billion atoms, and that would have consumed 99% of Frontier’s memory. We stopped because we were already far beyond a size anyone’s ever reached to conduct any meaningful science. But now we know it can be done.”

That capacity for detail could offer the chance to conduct vastly more complex studies than some scientists had hoped for on Frontier.

“This changes the game,” Asthagiri said. “Now we have a way to model these complex systems and their long-range interactions at extremely large sizes and have a hope of seeing emergent phenomena.

“For example, with this computing power, we could simulate sub-cellular components and eventually the minimal cell in atomic detail. From such explorations, we could learn about the spatial and temporal behavior of these cell structures that are basic to human, animal and plant life as we know it. This kind of opportunity is what an exascale machine like Frontier is for.”