Aurelia Butler

Researchers from Trinity College Dublin have shed new light on the formation of increasingly precious rare earth elements (REEs) by creating synthetic rocks and testing their responses to varying environmental conditions. REEs are used in electronic devices and green energy technologies, from smartphones to e-cars.
The findings, just published in the journal Global Challenges, have implications for recycling REEs from electronic waste, designing materials with advanced functional properties, and even for finding new REE deposits hidden around the globe.
Dr Juan Diego Rodriguez-Blanco, Associate Professor in Nanomineralogy at Trinity and an iCRAG (SFI Research Centre in Applied Geosciences) Funded Investigator, was the principal investigator of the work. He said:
“As both the global population and the fight against carbon emissions grow in the wake of global climate change, the demand for REEs simultaneously increases, which is why this research is so important. By growing our understanding of REE formation, we hope to pave the way to a more sustainable future.
“The genesis of rare earth deposits is one of the most complex problems in Earth sciences, but our approach is shedding new light on the mechanisms by which rocks containing rare earths form. This knowledge is critical for the energy transition, as rare earths are key manufacturing ingredients in the renewable energy economy.”
Many countries are currently searching for more REE deposits with minable concentrations, but the extraction processes are often challenging, and the separation methods are expensive and environmentally aggressive. More

Computers that can make use of the “spooky” properties of quantum mechanics to solve problems faster than current technology may sound alluring, but first they must overcome a massive disadvantage. Scientists from Japan may have found the answer through their demonstration of how a superconducting material, niobium nitride, can be added to a nitride-semiconductor substrate as a flat, crystalline layer. This process may lead to the easy manufacturing of quantum qubits connected with conventional computer devices.
The processes used to manufacture conventional silicon microprocessors have matured over decades and are constantly being refined and improved. In contrast, most quantum computing architectures must be designed mostly from scratch. However, finding a way to add quantum capabilities to existing fabrication lines, or even integrate quantum and conventional logic units in a single chip, might be able to vastly accelerate the adoption of these new systems.
Now, a team of researchers at the Institute of Industrial Science at The University of Tokyo have shown how thin films of niobium nitride (NbNx) can be grown directly on top of an aluminum nitride (AlN) layer. Niobium nitride can become superconducting at temperatures colder than about 16 degrees above absolute zero. As a result, it can be used to make a superconducting qubit when arranged in a structure called a Josephson junction. The scientists investigated the impact of temperature on the crystal structures and electrical properties of NbNx thin films grown on AlN template substrates. They showed that the spacing of atoms in the two materials was compatible enough to produce flat layers. “We found that because of the small lattice mismatch between aluminum nitride and niobium nitride, a highly crystalline layer could grow at the interface,” says first and corresponding author Atsushi Kobayashi.
The crystallinity of the NbNx was characterized with X-ray diffraction, and the surface topology was captured using atomic force microscopy. In addition, the chemical composition was checked using X-ray photoelectron spectroscopy. The team showed how the arrangement of atoms, nitrogen content, and electrical conductivity all depended on the growth conditions, especially the temperature. “The structural similarity between the two materials facilitates the integration of superconductors into semiconductor optoelectronic devices,” says Atsushi Kobayashi.
Moreover, the sharply defined interface between the AlN substrate, which has a wide bandgap, and NbNx, which is a superconductor, is essential for future quantum devices, such as Josephson junctions. Superconducting layers that are only a few nanometers thick and high crystallinity can be used as detectors of single photons or electrons.
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Materials provided by Institute of Industrial Science, The University of Tokyo. Note: Content may be edited for style and length. More

Stepping stones are placed to help travelers to cross streams. As long as there are stepping stones that connect the both sides of the water, one can easily get across with just a few steps. Using the same principal, a research team at POSTECH has developed technology that cuts the power consumption in semiconductor devices in half by placing stepping stones.
A research team led by Professor Junwoo Son and Dr. Minguk Cho (Department of Materials Science and Engineering) at POSTECH has succeeded in maximizing the switching efficiency of oxide semiconductor devices by inserting platinum nanoparticles. The findings from the study were recently published in the international journal Nature Communications.
The oxide material with the metal-insulator phase transition, in which the phase of a material rapidly changes from an insulator to a metal when the threshold voltage is reached, is spotlighted as a key material for fabricating low-power semiconductor devices.
The metal-insulator phase transition occurs when insulator domains, several nanometer (nm, billionth of a meter) units big, are transformed into metal domains. The key was to reduce the magnitude of the voltage applied to the device to increase the switching efficiency of a semiconductor device.
The research team succeeded in increasing the switching efficiency of the device by using platinum nanoparticles. When voltage was applied to a device, an electric current “skipped” through these particles and a rapid phase transition occurred.
The memory effect of the device also increased by more than a million times. In general, after the voltage is cut off, it immediately changes to the insulator phase where no current flows; this duration was extremely short at 1 millionth of a second. However, it was confirmed that the memory effect of remembering the previous firing of the devices can be increased to several seconds, and the device could be operated again with relatively low voltage owing to the residual metallic domains remaining near the platinum nanoparticles.
This technology is anticipated to be essential for the development of next-generation electronic devices, such as intelligent semiconductors or neuromorphic semiconductor devices that can process vast amounts of data with less power.
This study was conducted with the support from the Basic Science Research Program, Mid-career Researcher Program, and the Next-generation Intelligence Semiconductor Program of the National Research Foundation of Korea.
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Materials provided by Pohang University of Science & Technology (POSTECH). Note: Content may be edited for style and length. More

An international research team led by drone expert Mirko Kovac of Empa and Imperial College London has taken bees as a model to develop a swarm of cooperative, 3D-printing drones. Under human control, these flying robots work as a team to print 3D materials for building or repairing structures while flying, as the scientists report in the cover story of the latest issue of Nature.
3D printing is gaining momentum in the construction industry. Both on-site and in the factory, static and mobile robots print materials for use in construction projects, such as steel and concrete structures.
A new approach to 3D printing — led in its development by Imperial College London and Empa, the Swiss Federal Laboratories of Materials Science and Technology — uses flying robots, known as drones, that use collective building methods inspired by natural builders like bees and wasps.
The system, called Aerial Additive Manufacturing (Aerial-AM), involves a fleet of drones working together from a single blueprint.
It consists of BuilDrones, which deposit materials during flight, and quality-controlling ScanDrones, which continually measure the BuilDrones’ output and inform their next manufacturing steps.
The researchers say that in contrast to alternative methods, in-flight 3D printing unlocks doors that will lead to on-site manufacturing and building in difficult-to-access or dangerous locations such as post-disaster relief construction and tall buildings or infrastructure. More

Millions of quantum bits are required for quantum computers to prove useful in practical applications. The scalability is one of the greatest challenges in the development of future devices. One problem is that the qubits have to be very close to each other on the chip in order to couple them together. Researchers at Forschungszentrum Jülich and RWTH Aachen University have now come a significant step closer to solving the problem. They succeeded in transferring electrons, the carriers of quantum information, over several micrometres on a quantum chip. Their “quantum bus” could be the key component to master the leap to millions of qubits.
Quantum computers have the potential to vastly exceed the capabilities of conventional computers for certain tasks. But there is still a long way to go before they can help to solve real-world problems. Many applications require quantum processors with millions of quantum bits. Today’s prototypes merely come up with a few of these compute units.
“Currently, each individual qubit is connected via several signal lines to control units about the size of a cupboard. That still works for a few qubits. But it no longer makes sense if you want to put millions of qubits on the chip. Because that’ s necessary for quantum error correction,” says Dr. Lars Schreiber from the JARA Institute for Quantum Information at Forschungszentrum Jülich and RWTH Aachen University.
At some point, the number of signal lines becomes a bottleneck. The lines take up too much space compared to the size of the tiny qubits. And a quantum chip cannot have millions of inputs and outputs — a modern classical chip only contains about 2000 of these. Together with colleagues at Forschungszentrum Jülich and RWTH Aachen University, Schreiber has been conducting research for several years to find a solution to this problem.
Their overall goal is to integrate parts of the control electronics directly on the chip. The approach is based on so-called semiconductor spin qubits made of silicon and germanium. This type of qubit is comparatively tiny. The manufacturing processes largely match those of conventional silicon processors. This is considered to be advantageous when it comes to realising very many qubits. But first, some fundamental barriers have to be overcome.
“The natural entanglement that is caused by the proximity of the particles alone is limited to a very small range, about 100 nanometres. To couple the qubits, they currently have to be placed very close to each other. There is simply no space for additional control electronics that we would like to install there,” says Schreiber. More

An automated process that combines natural language processing and machine learning identified people who inject drugs (PWID) in electronic health records more quickly and accurately than current methods that rely on manual record reviews.
Currently, people who inject drugs are identified through International Classification of Diseases (ICD) codes that are specified in patients’ electronic health records by the healthcare providers or extracted from those notes by trained human coders who review them for billing purposes. But there is no specific ICD code for injection drug use, so providers and coders must rely on a combination of non-specific codes as proxies to identify PWIDs — a slow approach that can lead to inaccuracies.
The researchers manually reviewed 1,000 records from 2003-2014 of people admitted to Veterans Administration hospitals with Staphylococcus aureus bacteremia, a common infection that develops when the bacteria enters openings in the skin, such as those at injection sites. They then developed and trained algorithms using natural language processing and machine learning and compared them with 11 proxy combinations of ICD codes to identify PWIDs.
Limitations to the study include potentially poor documentation by providers. Also, the dataset used is from 2003 to 2014, but the injection drug use epidemic has since shifted from prescription opioids and heroin to synthetic opioids like fentanyl, which the algorithm may miss because the dataset where it learned the classification does not have many examples of that drug. Finally, the findings may not be applicable to other circumstances given that they are based entirely on data from the Veterans Administration.
Use of this artificial intelligence model significantly speeds up the process of identifying PWIDs, which could improve clinical decision making, health services research, and administrative surveillance.
“By using natural language processing and machine learning, we could identify people who inject drugs in thousands of notes in a matter of minutes compared to several weeks that it would take a manual reviewer to do this,” said lead author Dr. David Goodman-Meza, assistant professor of medicine in the division of infectious diseases at the David Geffen School of Medicine at UCLA. “This would allow health systems to identify PWIDs to better allocate resources like syringe services programs and substance use and mental health treatment for people who use drugs.”
The study’s other researchers are Dr. Amber Tang, Dr. Matthew Bidwell Goetz, Steven Shoptaw, and Alex Bui of UCLA; Dr. Michihiko Goto of University of Iowa and Iowa City VA Medical Center; Dr. Babak Aryanfar of VA Greater Los Angeles Healthcare System; Sergio Vazquez of Dartmouth College; and Dr. Adam Gordon of University of Utah and VA Salt Lake City Health Care System. Goodman-Meza and Goetz also have appointments with VA Greater Los Angeles Healthcare System.
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Materials provided by University of California – Los Angeles Health Sciences. Note: Content may be edited for style and length. More

Cornell University researchers have installed electronic “brains” on solar-powered robots that are 100 to 250 micrometers in size — smaller than an ant’s head — so that they can walk autonomously without being externally controlled.
While Cornell researchers and others have previously developed microscopic machines that can crawl, swim, walk and fold themselves up, there were always “strings” attached; to generate motion, wires were used to provide electrical current or laser beams had to be focused directly onto specific locations on the robots.
“Before, we literally had to manipulate these ‘strings’ in order to get any kind of response from the robot,” said Itai Cohen, professor of physics. “But now that we have these brains on board, it’s like taking the strings off the marionette. It’s like when Pinocchio gains consciousness.”
The innovation sets the stage for a new generation of microscopic devices that can track bacteria, sniff out chemicals, destroy pollutants, conduct microsurgery and scrub the plaque out of arteries.
The project brought together researchers from the labs of Cohen, Alyosha Molnar, associate professor of electrical and computer engineering; and Paul McEuen, professor of physical science, all co-senior authors on the paper. The lead author is postdoctoral researcher Michael Reynolds.
The team’s paper, “Microscopic Robots with Onboard Digital Control,” published Sept. 21 in Science Robotics.
The “brain” in the new robots is a complementary metal-oxide-semiconductor (CMOS) clock circuit that contains a thousand transistors, plus an array of diodes, resistors and capacitors. The integrated CMOS circuit generates a signal that produces a series of phase-shifted square wave frequencies that in turn set the gait of the robot. The robot legs are platinum-based actuators. Both the circuit and the legs are powered by photovoltaics.
“Eventually, the ability to communicate a command will allow us to give the robot instructions, and the internal brain will figure out how to carry them out,” Cohen said. “Then we’re having a conversation with the robot. The robot might tell us something about its environment, and then we might react by telling it, ‘OK, go over there and try to suss out what’s happening.'”
The new robots are approximately 10,000 times smaller than macroscale robots that feature onboard CMOS electronics, and they can walk at speeds faster than 10 micrometers per second.
The fabrication process that Reynolds designed, basically customizing foundry-built electronics, has resulted in a platform that can enable other researchers to outfit microscopic robots with their own apps — from chemical detectors to photovoltaic “eyes” that help robots navigate by sensing changes in light.
“What this lets you imagine is really complex, highly functional microscopic robots that have a high degree of programmability, integrated with not only actuators, but also sensors,” Reynolds said. “We’re excited about the applications in medicine — something that could move around in tissue and identify good cells and kill bad cells — and in environmental remediation, like if you had a robot that knew how to break down pollutants or sense a dangerous chemical and get rid of it.”
Video: https://youtu.be/bCjnekohBAY
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Materials provided by Cornell University. Original written by David Nutt, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length. More

A person’s distrust in humans predicts they will have more trust in artificial intelligence’s ability to moderate content online, according to a recently published study. The findings, the researchers say, have practical implications for both designers and users of AI tools in social media.
“We found a systematic pattern of individuals who have less trust in other humans showing greater trust in AI’s classification,” said S. Shyam Sundar, the James P. Jimirro Professor of Media Effects at Penn State. “Based on our analysis, this seems to be due to the users invoking the idea that machines are accurate, objective and free from ideological bias.”
The study, published in the journal of New Media & Society also found that “power users” who are experienced users of information technology, had the opposite tendency. They trusted the AI moderators less because they believe that machines lack the ability to detect nuances of human language.
The study found that individual differences such as distrust of others and power usage predict whether users will invoke positive or negative characteristics of machines when faced with an AI-based system for content moderation, which will ultimately influence their trust toward the system. The researchers suggest that personalizing interfaces based on individual differences can positively alter user experience. The type of content moderation in the study involves monitoring social media posts for problematic content like hate speech and suicidal ideation.
“One of the reasons why some may be hesitant to trust content moderation technology is that we are used to freely expressing our opinions online. We feel like content moderation may take that away from us,” said Maria D. Molina, an assistant professor of communication arts and sciences at Michigan State University, and the first author of this paper. “This study may offer a solution to that problem by suggesting that for people who hold negative stereotypes of AI for content moderation, it is important to reinforce human involvement when making a determination. On the other hand, for people with positive stereotypes of machines, we may reinforce the strength of the machine by highlighting elements like the accuracy of AI.”
The study also found users with conservative political ideology were more likely to trust AI-powered moderation. Molina and coauthor Sundar, who also co-directs Penn State’s Media Effects Research Laboratory, said this may stem from a distrust in mainstream media and social media companies.
The researchers recruited 676 participants from the United States. The participants were told they were helping test a content moderating system that was in development. They were given definitions of hate speech and suicidal ideation, followed by one of four different social media posts. The posts were either flagged for fitting those definitions or not flagged. The participants were also told if the decision to flag the post or not was made by AI, a human or a combination of both.
The demonstration was followed by a questionnaire that asked the participants about their individual differences. Differences included their tendency to distrust others, political ideology, experience with technology and trust in AI.
“We are bombarded with so much problematic content, from misinformation to hate speech,” Molina said. “But, at the end of the day, it’s about how we can help users calibrate their trust toward AI due to the actual attributes of the technology, rather than being swayed by those individual differences.”
Molina and Sundar say their results may help shape future acceptance of AI. By creating systems customized to the user, designers could alleviate skepticism and distrust, and build appropriate reliance in AI.
“A major practical implication of the study is to figure out communication and design strategies for helping users calibrate their trust in automated systems,” said Sundar, who is also director of Penn State’s Center for Socially Responsible Artificial Intelligence. “Certain groups of people who tend to have too much faith in AI technology should be alerted to its limitations and those who do not believe in its ability to moderate content should be fully informed about the extent of human involvement in the process.”
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Materials provided by Penn State. Original written by Jonathan McVerry. Note: Content may be edited for style and length. More