Aurelia Butler

Gold, a precious metal, is arguably the most widely used metal across jewelry and coinage due to its physical properties that are unique to the world of metals. Not only is it a good conductor of heat and electricity, it is unaffected by air and most reagents. It is also used in a wide range of industrial, scientific, and medical applications. For example, it has been used as the template for molecular self-assembly, the supporting material for two-dimensional materials growth, and especially for the synthesis of carbon nanoribbons. More than half a century ago, researchers unveiled the fancy textures on gold surfaces at the nanoscale. Efforts for a better understanding of the surface structures on the atomic scale have been continually paid for from then on.
Au(111) surface, the most stable gold surface, has a periodic herringbone texture on it that can be observed by sophisticated microscopes. A long-term puzzle is why this strange herringbone forms on this gold surface. Extensive studies have been performed for decades but a thorough description of structure details is still missing and thus the underlying mechanism has never been properly understood. The difficulties in this issue lie in the fact that even though the size of the texture is at the nanoscale, its periodic unit still contains more than 100,000 atoms. To quantitatively study this system, one needs a very efficient and also very accurate computational method. In traditional approaches, however, these two requirements cannot be satisfied simultaneously.
Recently, Distinguished Professor Feng Ding (Department of Materials Science and Engineering) and his colleagues from the Center for Multidimensional Carbon Materials (CMCM), within the Institute for Basic Science (IBS) at UNIST, utilized the state-of-the-art neural network method to train a gold force field from an accurate but slow computational method.
Due to the powerful learning ability of neural networks, this new force field acquires almost the same accuracy, and more importantly, it is many orders of magnitude faster than the original method. Using this force field, the authors successfully simulated the experimentally observed herringbone texture on Au(111) surface and revealed that there is non-negligible deformation underneath the surface. This deformation is critical for the formation of the herringbone texture because it allows an effective relaxation of the rearranged surface atoms. If the deformation is suppressed (take a thin model for instance), the texture will form stripes.
Meanwhile, the authors also verified that the herringbone texture is sensitive to applied strains. On a strain-free surface, the herringbone texture is mirror-symmetric. However, if a slight strain is introduced, the texture becomes tilted. Above a critical strain, it thoroughly transforms into a stripe texture.
“This important work extends the application of the machine learning method in material science and opens a new avenue to study complex surface systems,” noted the research team.
Led by Distinguished Professor Feng Ding, this study was first authored by Dr. Pai Li. The findings of this research have been published in the October 2022 issue of Science Advances.
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Materials provided by Ulsan National Institute of Science and Technology(UNIST). Original written by JooHyeon Heo. Note: Content may be edited for style and length. More

From superfast magnetic levitation trains and computer chips to magnetic resonance imaging (MRI) machines and particle accelerators, superconductors are electrifying various aspects of our life. Superconductivity is an interesting property that allows materials to transfer moving charges without any resistance, below a certain critical point. This implies that superconducting materials can transfer electrical energy in a highly efficient manner without loss in the form of heat, unlike many conventional conductors.
Almost two decades ago scientists discovered superconductivity in a new material — magnesium diboride, or MgB2. There has been a resurgence in the of popularity MgB2 due to its low cost, superior superconducting abilities, high critical current density (which means that compared to other materials, MgB2 remains a semiconductor even when larger amounts of electric current is passed through it), and trapped magnetic fields arising from strong pinning of the vortices — which are cylindrical current loops or tubes of magnetic flux that penetrate a superconductor. The intermetallic MgB2 also allows adjustability of its properties. For instance, the critical current density values (Jc) of MgB2 can be improved by decreasing the grain size and increasing the number of grain boundaries. Such adjustability is not observed in conventional layered superconductors.
To widen the applications of MgB2, however, there is a need to simplify the method of its preparation. Recently, a team of researchers embarked on a journey to do so. They fabricated a novel bulk MgB2 via a process called spark plasma sintering (SPS). In their recent article, published first on 27 July 2022 in Nanomaterials, Prof. Muralidhar Miryala from Shibaura Institute of Technology (SIT), Japan, who led the group, explains “Spark plasma sintering (SPS) is a very interesting technique — it is a rapid consolidation method, where powder is turned into a dense solid. The heat source in this procedure is not external but is an electric current that flows across the die, causing the powder to sinter into a bulk material. The sintering kinetics can be understood and controlled better with SPS. Unlike other similar techniques, it allows grain growth control. What’s more, it also has a shorter processing time!”
Prof. Miryala and Prof. Jacques G. Noudem (from the University of Normandie, France) had used this unconventional method to prepare bulk samples of MgB2. The resultant material had excellent superconducting properties and a density that reached 95% of what was theoretically predicted for the material. The study team included Prof. Pierre Bernstein and Yiteng Xing, who is a double degree Ph.D. student at SIT and the University of Normandie.
To synthesize the bulk MgB2, the team loaded two powders — magnesium and amorphous boron — into a tungsten carbide (WC) mold and sintered them using SPS at different temperatures ranging from 500-750°C, and pressure ranging from 260-300 megapascal (MPa), then cooled the formed material. The total processing time was about 100 minutes. The team then analyzed the density and the structural properties of the prepared material, using various imaging and testing methods.
Their experiments revealed that the material had a very high density of 2.46 g/cm3 and a high packing factor of 95% (indicating that the atoms in the bulk material were situated very close to each other). It also showed the presence of nano-grains and a large number of grain boundaries. Moreover, it did not exhibit Mg-depleted phases like MgB4. Electromagnetic characterization of the material showed that it exhibited an extremely high Jc of up to 6.75 105 ampere/cm2 at about -253°C. This means that even at that high a current density, the bulk MgB2 made by the team would act as a superconductor. “Its Jc was quite remarkable for pure, undoped MgB2,” commented Prof. Miryala.
Curious as to how the material exhibited such excellent properties, the team dug deeper. They concluded that the prepared MgB2’s superconducting properties were due to its high density, excellent grain connectivity (due to no Mg-depleted phases), and the strong pinning of vortices availed by the presence of nano-grains and grain boundaries.
This study provided a new way to improve the properties of superconducting materials like MgB2. Given this material’s high Jc, it can be used in liquid hydrogen-cooled technology. It is also emerging as a promising candidate for liquid hydrogen-based transportation, storage, and fuel systems. “Global warming is one of the major threats humanity is facing today and shifting to a renewable energy economy is one of the most effective solutions to this problem. Given the material’s potential use in liquid hydrogen systems and its excellent structural and superconducting properties, our work is a positive step towards the realization of greener technology,” concludes Prof. Miryala.
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Materials provided by Shibaura Institute of Technology. Note: Content may be edited for style and length. More

Discarded electronic devices, such as cell phones, are a fast-growing source of waste. One way to mitigate the problem could be to use components that are made with renewable resources and that are easy to dispose of responsibly. Now, researchers reporting in ACS Applied Materials & Interfaces have created a prototype circuit board that is made of a sheet paper with fully integrated electrical components, and that can be burned or left to degrade.
Most small electronic devices contain circuit boards that are made from glass fibers, resins and metal wiring. These boards are not easy to recycle and are relatively bulky, making them undesirable for use in point-of-care medical devices, environmental monitors or personal wearable devices. One alternative is to use paper-based circuit boards, which should be easier to dispose of, less expensive and more flexible. However, current options require specialized paper, or they simply have traditional metal circuitry components mounted onto a sheet of paper. Instead, Choi and colleagues wanted to develop circuitry that would be simple to manufacture and that had all the electronic components fully integrated into the sheet.
The team designed a paper-based amplifier-type circuit that incorporated resistors, capacitors and a transistor. They first used wax to print channels onto a sheet of paper in a simple pattern. After melting the wax so that it soaked into the paper, the team printed semi-conductive and conductive inks, which soaked into the areas not blocked by wax. Then, the researchers screen-printed additional conductive metal components and casted a gel-based electrolyte onto the sheet.
Tests confirmed that the resistor, capacitor and transistor designs performed properly. The final circuit was very flexible and thin, just like paper, even after adding the components. To demonstrate the degradability of the circuit, the team showed that the entire unit quickly burned to ash after being lit on fire. The researchers say this represents a step toward producing completely disposable electronic devices.
The authors acknowledge funding from the National Science Foundation.
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Carnegie Mellon University researchers have pioneered the CMU Array — a new type of microelectrode array for brain computer interface platforms. It holds the potential to transform how doctors are able to treat neurological disorders.
3D printed at the nanoscale, the ultra-high-density microelectrode array (MEA) is fully customizable. This means that one day, patients suffering from epilepsy or limb function loss due to stroke could have personalized medical treatment optimized for their individual needs.
The collaboration combines the expertise of Rahul Panat, associate professor of mechanical engineering, and Eric Yttri, assistant professor of biological sciences. The team applied the newest microfabrication technique, Aerosol Jet 3D printing, to produce arrays that solved the major design barriers of other brain computer interface (BCI) arrays. The findings were published in Science Advances.
“Aerosol Jet 3D printing offered three major advantages,” Panat explained. “Users are able to customize their MEAs to fit particular needs; the MEAs can work in three dimensions in the brain; and the density of the MEA is increased and therefore more robust.”
MEA-based BCIs connect neurons in the brain with external electronics to monitor or stimulate brain activity. They are often used in applications like neuroprosthetic devices, artificial limbs, and visual implants to transport information from the brain to extremities that have lost functionality. BCIs also have potential applications in treating neurological diseases such as epilepsy, depression, and obsessive-compulsive disorder. However, existing devices have limitations.
There are two types of popular BCI devices. The oldest MEA is the Utah array, developed at the University of Utah and patented in 1993. This silicone-based array uses a field of tiny pins, or shanks, that can be inserted directly into the brain to detect electrical discharge from neurons at the tip of each pin. More

A new screening test app could help advance the early detection of Parkinson’s disease and severe COVID-19, improving the management of these illnesses.
Developed by a research team of engineers and neurologists led by RMIT University in Melbourne, the test can produce accurate results using just people’s voice recordings.
Millions of people worldwide have Parkinson’s, which is a degenerative brain condition that can be challenging to diagnose as symptoms vary among people. Common symptoms include slow movement, tremor, rigidity and imbalance.
Currently, Parkinson’s is diagnosed through an evaluation by a neurologist that can take up to 90 minutes.
Powered by artificial intelligence, the smartphone app records a person’s voice and takes just 10 seconds to reveal whether they may to have Parkinson’s disease and should be referred to a neurologist.
Lead researcher Professor Dinesh Kumar, from RMIT’s School of Engineering, said the easy-to-use screening test made it ideal to use in a national screening program. More

Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on “edge devices” that work independently from central computing resources.
Training a machine-learning model on an intelligent edge device allows it to adapt to new data and make better predictions. For instance, training a model on a smart keyboard could enable the keyboard to continually learn from the user’s writing. However, the training process requires so much memory that it is typically done using powerful computers at a data center, before the model is deployed on a device. This is more costly and raises privacy issues since user data must be sent to a central server.
To address this problem, researchers at MIT and the MIT-IBM Watson AI Lab developed a new technique that enables on-device training using less than a quarter of a megabyte of memory. Other training solutions designed for connected devices can use more than 500 megabytes of memory, greatly exceeding the 256-kilobyte capacity of most microcontrollers (there are 1,024 kilobytes in one megabyte).
The intelligent algorithms and framework the researchers developed reduce the amount of computation required to train a model, which makes the process faster and more memory efficient. Their technique can be used to train a machine-learning model on a microcontroller in a matter of minutes.
This technique also preserves privacy by keeping data on the device, which could be especially beneficial when data are sensitive, such as in medical applications. It also could enable customization of a model based on the needs of users. Moreover, the framework preserves or improves the accuracy of the model when compared to other training approaches.
“Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a broader reach, especially for low-power edge devices,” says Song Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, and senior author of the paper describing this innovation. More

A team led by the University of California San Diego has developed a new system of algorithms that enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles.
In tests, the system guided a robot to maneuver autonomously and swiftly across sandy surfaces, gravel, grass, and bumpy dirt hills covered with branches and fallen leaves without bumping into poles, trees, shrubs, boulders, benches or people. The robot also navigated a busy office space without bumping into boxes, desks or chairs.
The work brings researchers a step closer to building robots that can perform search and rescue missions or collect information in places that are too dangerous or difficult for humans.
The team will present its work at the 2022 International Conference on Intelligent Robots and Systems (IROS), which will take place from Oct. 23 to 27 in Kyoto, Japan.
The system provides a legged robot more versatility because of the way it combines the robot’s sense of sight with another sensing modality called proprioception, which involves the robot’s sense of movement, direction, speed, location and touch — in this case, the feel of the ground beneath its feet.
Currently, most approaches to train legged robots to walk and navigate rely either on proprioception or vision, but not both at the same time, said study senior author Xiaolong Wang, a professor of electrical and computer engineering at the UC San Diego Jacobs School of Engineering.
“In one case, it’s like training a blind robot to walk by just touching and feeling the ground. And in the other, the robot plans its leg movements based on sight alone. It is not learning two things at the same time,” said Wang. “In our work, we combine proprioception with computer vision to enable a legged robot to move around efficiently and smoothly — while avoiding obstacles — in a variety of challenging environments, not just well-defined ones.”
The system that Wang and his team developed uses a special set of algorithms to fuse data from real-time images taken by a depth camera on the robot’s head with data from sensors on the robot’s legs. This was not a simple task. “The problem is that during real-world operation, there is sometimes a slight delay in receiving images from the camera,” explained Wang, “so the data from the two different sensing modalities do not always arrive at the same time.”
The team’s solution was to simulate this mismatch by randomizing the two sets of inputs — a technique the researchers call multi-modal delay randomization. The fused and randomized inputs were then used to train a reinforcement learning policy in an end-to-end manner. This approach helped the robot to make decisions quickly during navigation and anticipate changes in its environment ahead of time, so it could move and dodge obstacles faster on different types of terrains without the help of a human operator.
Moving forward, Wang and his team are working on making legged robots more versatile so that they can conquer even more challenging terrains. “Right now, we can train a robot to do simple motions like walking, running and avoiding obstacles. Our next goals are to enable a robot to walk up and down stairs, walk on stones, change directions and jump over obstacles.”
Video: https://youtu.be/GKbTklHrq60
The team has released their code online at: https://github.com/Mehooz/vision4leg.
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Materials provided by University of California – San Diego. Original written by Liezel Labios. Note: Content may be edited for style and length. More

Although just cute little creatures at first glance, the microscopic geckos and octopuses fabricated by 3D laser printing in the molecular engineering labs at Heidelberg University could open up new opportunities in fields such as microrobotics or biomedicine. The printed microstructures are made from novel materials — known as smart polymers — whose size and mechanical properties can be tuned on demand and with high precision. These “life-like” 3D microstructures were developed in the framework of the “3D Matter Made to Order” (3DMM2O) Cluster of Excellence, a collaboration between Ruperto Carola and the Karlsruhe Institute of Technology (KIT).
“Manufacturing programmable materials whose mechanical properties can be adapted on demand is highly desired for many applications,” states Junior Professor Dr Eva Blasco, group leader at the Institute of Organic Chemistry and the Institute for Molecular Systems Engineering and Advanced Materials of Heidelberg University. This concept is known as 4D printing, and the additional fourth dimension refers to the ability of three-dimensionally printed objects to alter their properties over time. One prominent example of materials for 4D printing are shape memory polymers — smart materials that can return to their original shape from a deformed state in response to an external stimulus such as temperature.
The team led by Prof. Blasco recently introduced one of the first examples of 3D printed shape memory polymers at the microscale. In cooperation with the working group of biophysicist Prof. Dr Joachim Spatz, a scientist at Ruperto Carola and Director at the Max Planck Institute for Medical Research, the researchers developed a new shape memory material that can be 3D printed with high resolution both at the macro and at the microscale. The structures produced include box-shaped microarchitectures whose lids close in response to heat and can then be reopened. “These tiny structures show unusual shape memory properties at low activation temperatures, which is extremely interesting for bioapplications,” explains Christoph Spiegel, a doctoral researcher in the working group of Eva Blasco.
Using adaptive materials, the researchers succeeded in a follow-up study in producing much more complex 3D microstructures like geckos, octopuses, and even sunflowers with “life-like” properties. These materials are based on dynamic chemical bonds. The Heidelberg researchers report that alkoxyamines are particularly suitable for this purpose. After the printing process, these dynamic bonds allow for the complex, micrometric structures to grow eight-fold in just a few hours and to harden, while maintaining their shape. “Conventional inks do not offer such features,” emphasises Prof. Blasco. “Adaptive materials containing dynamic bonds have a bright future in the field of 3D printing,” adds the chemist.
Materials scientists at the Karlsruhe Institute of Technology (KIT) also participated in the research on adaptable materials with “life-like” properties. The German Research Foundation and the Carl Zeiss Foundation funded the work, which was carried out within the framework of the 3DMM2O Cluster of Excellence. The results were published in two papers in the journal Advanced Functional Materials.
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Materials provided by Heidelberg University. Note: Content may be edited for style and length. More