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Stuttgart – Scientists at the Max Planck Institute for Intelligent Systems (MPI-IS) have developed millimeter-sized magnetic arrays, which can pump viscous fluids as effectively as biological cilia that are common throughout nature. Their research was recently published in Science Advances with the title “Bioinspired cilia arrays with programmable nonreciprocal motion and metachronal coordination”. The authors Dr. Xiaoguang Dong, Dr. Wenqi Hu, Ziyu Ren, and Prof. Metin Sitti are members of the Physical Intelligence Department at the MPI-IS, where Sitti is the Director.

Cilia are hair-like structures that exist, for instance, in the respiratory tract of the human body to clear away mucus and dirt, and in the female reproductive system to transport oocytes. Reef corals also have cilia – tiny slender appendages that can stir water to bring nutrients and oxygen.

The soft robotic devices that the scientists have created greatly resemble real cilia. The only difference is the size: each replica array is just under one millimeter in size. This is one hundred times larger than natural cilia found in a lung, for instance.

The scientists built the artificial cilia arrays from an elastomer into which they embedded magnetic particles in a very specific pattern. In this way, each array becomes programmable: the researchers can remotely control not only the asymmetric motion of each cilium, but also the coordinated motion of all cilia relative to their neighbors.

Figure 1. Inspired by biological cilia (left), the artificial cilia arrays (right) pump fluid and transport particles efficiently thanks to their optimally coordinated movement. Copyright: Physical Intelligence Department (MPI-IS).

The researchers applied a rotating external magnetic field and showed that their artificial cilia arrays performed a wave-like motion similar to natural cilia (Figure 1). They used a red-color dye and particle imaging techniques to show how the arrays pump a viscous fluid (glycerol) and transport particles efficiently thanks to the arrays’ optimally coordinated movement (Figure 2).

Figure 2. The bioinspired cilia arrays pump a viscous fluid (glycerol) visualized by a red-color dye at the low Reynolds number regime. Scale bar, 1 mm. Copyright: Physical Intelligence Department (MPI-IS).

The researchers recorded the movement of the artificial cilia using a high-speed camera, as featured in this video. The clip shows how the slender structures beat in a coordinated manner, similar to real cilia: the artificial arrays neither move back and forth in synchrony nor randomly. In fact, they perform a coordinated traveling wave-like movement. Research has shown that this so-called metachronal wave has been perfected in the course of evolution, optimizing the fluid flows.

Investigating natural cilia and their collective behavior is a challenging task, as they are only a few micrometers small. Even with a microscope, cilia arrays are very difficult to see. Additionally, it is impossible to control their movement. Now, however, for the first time, the Stuttgart-based scientists were able to experimentally reveal the fundamental quantitative relationship between metachronal coordination and its induced fluid flow. “With our artificial system, we can mimic the cilia motion in a similar way to real cilia at a larger scale, while always maintaining the same physical laws. In this way, we can generate much cleaner data. We can quantify what movement patterns generate the maximal fluid flow,” Xiaoguang Dong says.

In their experiments, the team showed that only antiplectic metachronal waves can enhance the fluid flow. Antiplectic means that the wave propagation direction is opposite to that of the fluid flow. “Our observation proves the critical function of antiplectic metachronal waves and reveals the fundamental quantitative relationship between metachronal coordination and the induced fluid flow in both artificial and biological cilia,” Ziyu Ren says.

The team envisions that their research project could inspire many more: “Our artificial cilia arrays could be used to further investigate many interesting scientific questions, such as how the fluid flow patterns change when varying the cilia beating dynamics, boundary geometries, or the type of surrounding fluid,” Wenqi Hu says.

“My team, the Physical Intelligence Department, is inspired by various biological systems. We want to learn the underlying physical principles behind their locomotion, intelligence and functional behavior and then use this knowledge to create state-of-the-art miniature robots. By doing so, we not only better understand the biological systems: in the future, we also aim to use our robots as innovative wireless medical devices to advance the healthcare system, helping to improve the welfare of our society,” Sitti says. 

The full scientific paper can be found here:

https://advances.sciencemag.org/content/6/45/eabc9323.full

About the scientists:

Dr. Xiaoguang Dong is a Postdoctoral Researcher in the Physical Intelligence Department. He received a Ph.D. degree in Mechanical Engineering from Carnegie Mellon University, USA in 2019. His research focus is developing miniature soft robots and devices for answering scientific questions in biomechanics and resolving engineering challenges in biomedical applications.

Dr. Wenqi Hu comes from South West China. He received his Ph.D. from the University of Hawaii in 2014. He has been working on soft millirobots since he joined the Physical Intelligence Department team in Stuttgart back in 2014.

Ziyu Ren is a Ph.D. student in the Physical Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart. Ziyu received his BSc and MSc degrees in mechanical engineering from Beihang University, China in 2014 and 2017. In his Ph.D. he focuses on developing magnetic soft millirobots.

Prof. Dr. Metin Sitti has been the Director of the Physical Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany since 2014. As his current ancillary academic positions, he is a professor at ETH Zurich, Switzerland, professor at Koç University, Turkey, and an honorary professor at the University of Stuttgart, Germany. He was a professor in the Department of Mechanical Engineering and Robotics Institute at Carnegie Mellon University, Pittsburgh, USA (2002-2014), and a research scientist at the University of California at Berkeley, USA (1999-2002). He received the BSc (1992) and MSc (1994) degrees in electrical and electronics engineering and physics from Boğaziçi University, Turkey, and the Ph.D. degree (1999) in electrical engineering from the University of Tokyo, Japan.

Sitti and his team aim to understand the principles of design, locomotion, perception, learning, and control of small-scale mobile robots made of smart and soft materials. The intelligence of such robots mainly comes from their physical design, material, adaptation, and self-organization more than from their computational intelligence. Such physical intelligence methods are essential for small-scale milli- and micro-robots especially due to their inherently limited on-board computation, actuation, powering, perception, and control capabilities. Sitti envisions his novel small-scale robotic systems to be applied in healthcare, bioengineering, manufacturing, or environmental monitoring to name a few.

Berlin – At its Grand Finale on November 9, exactly 31 years after the fall of the Berlin Wall, Falling Walls presented Prof. Dr. Metin Sitti with the «Breakthrough of the Year» Award in the Engineering and Technology category. The Falling Walls World Science Summit is an annual international scientific event in Berlin that serves as a forum for discussion and debate about the scientific breakthroughs of the future.

Sitti is a pioneer in many research areas, such as wireless miniature medical robots, gecko-inspired microfiber adhesives, bio-inspired miniature robots, and physical intelligence. “I am honored to receive this award. It is a great acknowledgment of my team's breakthrough research efforts,” says Sitti who is the Director of the Physical Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, a Professor in the Department of Information Technology and Electrical Engineering at ETH Zurich, and a Professor at the School of Medicine and College of Engineering at Koç University.

“My research breaks down barriers for the use of wireless medical devices inside the human body. I was greatly inspired by Fantastic Voyage, a 1966 science-fiction film about a microscopic submarine that ventures into a stroke patient’s body to open the blood clot in his brain. The moment I saw it, I started dreaming of creating tiny medical devices that could navigate complex regions inside our body. In minimally invasive procedures, these machines could diagnose and treat diseases at early stages and with few side effects,” Sitti says.

In recent months, nearly 1000 nominees from 111 countries have competed for a Science Breakthrough of the Year Award in ten different categories. The awards honor some of the world’s most outstanding science projects and bright minds. At today’s Grand Finale, Sitti and the other recipients came together to discuss how science and innovation can contribute to the state of humanity.

Sitti is an acclaimed scientist. In March 2019, he received a prestigious Advanced Grant from the European Research Council (ERC), which is awarded only to established researchers with a proven track record of excellence. In terms of the originality and significance of research contributions, grant recipients are exceptional leaders in their given fields. What is more, Sitti and his team recently received the Best Paper Award at the prestigious Robotics Science and Systems Conference for their invention of a baby jellyfish-inspired soft millirobot with medical functions.

“I am passionate about pushing the scientific boundaries of what is currently possible with wireless tiny soft robots and their use in clinical applications, with the ultimate aim of saving more lives,” says Sitti.

Soft milli- and microrobots such as those Sitti and his team are developing hold huge potential and could one day have a radical impact on medicine. The scientists are working intensively on developing wireless tiny machines that will, in the near future, be able to access smaller, difficult-to-access high-risk regions inside the body. As semi-implantable medical devices, these technologies will remain in patients’ bodies for long periods of time, enabling minimally or non-invasive diagnostic and therapeutic interventions.

However, many challenges must still be overcome before these tiny robots can be applied. For instance, scientists must develop ways to move and control these tiny robots inside the body with high levels of precision, taking body fluid flows and organ movement into account. They must also ensure that patient safety is guaranteed, and that these robots can be in us for long periods of time, even in the face of potential immune or other reactions.

Sitti and his team have been working on shape-programmable tiny soft machines built with biocompatible magnetic soft materials for many years. Their findings have been published in the world’s most respected scientific journals. For instance, in a Nature publication, his team showed how to program the shape deformations of a soft millirobot via external magnetic fields. This enables the robot to crawl, swim, jump, and roll safely through the complex regions of the human body. The robot can deliver medication at a target location, while being tracked by medical imaging systems remotely.

Sitti received his BSc and MSc degrees in electrical and electronics engineering and physics from Bogazici University in Istanbul in 1992 and 1994, respectively, and his Ph.D. degree in electrical engineering from the University of Tokyo in 1999. He was a research scientist at UC Berkeley from 1999 to 2002 and a Professor at the Department of Mechanical Engineering and Robotics Institute at Carnegie Mellon University from 2002 to 2014. He became a Director at the Max Planck Institute for Intelligent Systems in Stuttgart in 2014.

About Falling Walls:

The fall of the Berlin Wall marked the dawn of a new era of freedom by breaking barriers both physical and imaginary in Germany and around the world. Three decades on, the night of 9 November 1989 has become a symbol of changing status quos and overcoming divisions of all kinds. Drawing inspiration from this historic moment, the Falling Walls Conference is driven by the desire to find out: which are the next walls to fall in science and society?

Since its inception, the conference has become a unique global platform connecting science, business and society through a shared commitment to create breakthrough solutions to challenges across borders and disciplines.

https://falling-walls.com/

In an effort to enable more accurate network analyses in applied network science, scientists at the Max Planck Institute for Intelligent Systems in Tübingen recently developed the Theoretical Criterion for Eigenvector Centrality (TCEC), a quantitative sampling method. The method aims to preserve representative network properties within finite data samples by measuring the influence of a single node in a network. Now, Nicolò Ruggeri and Caterina De Bacco are expanding the model to include a new sampling algorithm that improves estimates of PageRank centrality (PR), the algorithm used by Google Search to rank web pages in search engine results.

They present their research in the paper entitled “Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures.” The paper was recently published in Applied Network Science. Ruggeri, the paper’s lead author, is a PhD student in the Cyber Valley Physics for Inference and Optimization group at MPI-IS in Tübingen and at ETH Zurich. De Bacco, who co-authored the paper, is group leader. 

Ruggeri and De Bacco’s research can make a decisive contribution in a broad range of research fields, for instance in social science analyses that seek to infer the relationship between nodes in a network, such as social media channels.  

“Our work was inspired by a conversation we had with a sociologist whose research focuses on analyzing the dynamics of hate speech on Twitter,” said Ruggeri. In such analyses, a major challenge lies in analyzing data samples drawn from complex networks. In most cases, not all the relevant information is available in the sample, and this can lead to biases in the analysis of the network properties under investigation. For instance, social scientists attempting to make an accurate analysis of the most influential users in an entire network would need a data sample that reflected influence accurately. 

This is where the TCEC sampling model comes in. With it, Ruggeri and De Bacco have made it possible to get representative snapshots of user dynamics in major networks like Facebook and Twitter. It does this by selecting subsamples that well represent a measure of influence of single nodes in the network, which is also known as eigenvector centrality. “If a researcher selects network users randomly, the sampled network will likely be very different than the actual whole. Any analysis will then be a distortion of reality. With TCEC, a scientist sampling a network of millions of users like Twitter can select a data sample of 10,000 nodes that is more representative of the whole, in terms of users’ influence as measured by eigenvector centrality,” says De Bacco. 

By expanding the model to include PageRank centrality, Ruggeri and De Bacco hope to further improve data samples, thus making network analyses even more accurate. Until now, however, the theoretical results of their research have shown that sampling for PR would lower the performance of the original TCEC algorithm. In a next step, the researchers plan to continue fine-tuning their method and applying it in collaboration with sociologists to study how influence on online social networks is related to real-life events.

The original TCEC sampling method is available for use: Ruggeri and De Bacco have released the code, which is available at: https://github.com/cdebacco/tcec_sampling