Aurelia Butler

Scientists at Osaka University, Panasonic Corporation, and Waseda University used scanning electron microscopy (SEM) and X-ray absorption spectroscopy to determine which additives induce crystallization in supercooled aqueous solutions. This work may lead to the development of new energy storage materials based on latent heat.
If you put a bottle of water into the freezer, you will expect to pull out a solid cylinder of ice after a few hours. However, if the water has very few impurities and left undisturbed, it may not be frozen, and instead remain as a supercooled liquid. Be careful, because this state is very unstable, and the water will crystallize quickly if shaken or impurities are added — as many YouTube videos will attest. Supercooling is a phenomenon in which an aqueous solution maintains its liquid state without solidifying, even though its temperature is below the freezing point. Although many studies have been done on additives that trigger the freezing of supercooling liquids, the details of the mechanism are unknown. One potential application might be latent heat storage materials, which rely on freezing and melting to capture and later release heat, like a reusable freezer pack.
Now, a team of researchers led by Osaka University has shown that silver nanoparticles are very effective at inducing crystallization in clathrate hydrates. Clathrate hydrates physically look like ice and are composed of hydrogen-bonded water cages with guest molecules inside. “Using SEM with the freeze-fracture replica method, we captured the moment when a nascent cluster enveloped a silver nanoparticle in the aqueous solution of latent heat storage materials,” corresponding author Professor Takeshi Sugahara explains. This occurs because the nanoparticles serve as a “seed,” or nucleation site, for tiny clusters to form. Once this gets started, the remaining solute and water molecules can quickly form additional clusters and then cluster densification leads to the crystallization. The researchers found that while silver nanoparticles tended to accelerate the formation of these clusters, other metal nanoparticles, such as palladium, gold, and iridium do not promote crystallization. “The supercooling suppression effect obtained in the present study will contribute to achieve the practical use of clathrate hydrates as latent heat storage materials,” Professor Sugahara says. Material design guidelines for enhanced supercooling control, as described in this study, may lead to the application of latent heat storage materials in solar energy and heat recovery technologies with improved efficiency.
Story Source:
Materials provided by Osaka University. Note: Content may be edited for style and length. More

Predicting what someone is about to do next based on their body language comes naturally to humans but not so for computers. When we meet another person, they might greet us with a hello, handshake, or even a fist bump. We may not know which gesture will be used, but we can read the situation and respond appropriately.
In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.
“Our algorithm is a step toward machines being able to make better predictions about human behavior, and thus better coordinate their actions with ours,” said Carl Vondrick, assistant professor of computer science at Columbia, who directed the study, which was presented at the International Conference on Computer Vision and Pattern Recognition on June 24, 2021. “Our results open a number of possibilities for human-robot collaboration, autonomous vehicles, and assistive technology.”
It’s the most accurate method to date for predicting video action events up to several minutes in the future, the researchers say. After analyzing thousands of hours of movies, sports games, and shows like “The Office,” the system learns to predict hundreds of activities, from handshaking to fist bumping. When it can’t predict the specific action, it finds the higher-level concept that links them, in this case, the word “greeting.”
Past attempts in predictive machine learning, including those by the team, have focused on predicting just one action at a time. The algorithms decide whether to classify the action as a hug, high five, handshake, or even a non-action like “ignore.” But when the uncertainty is high, most machine learning models are unable to find commonalities between the possible options.
Columbia Engineering PhD students Didac Suris and Ruoshi Liu decided to look at the longer-range prediction problem from a different angle. “Not everything in the future is predictable,” said Suris, co-lead author of the paper. “When a person cannot foresee exactly what will happen, they play it safe and predict at a higher level of abstraction. Our algorithm is the first to learn this capability to reason abstractly about future events.”
Suris and Liu had to revisit questions in mathematics that date back to the ancient Greeks. In high school, students learn the familiar and intuitive rules of geometry — that straight lines go straight, that parallel lines never cross. Most machine learning systems also obey these rules. But other geometries, however, have bizarre, counter-intuitive properties; straight lines bend and triangles bulge. Suris and Liu used these unusual geometries to build AI models that organize high-level concepts and predict human behavior in the future.
“Prediction is the basis of human intelligence,” said Aude Oliva, senior research scientist at the Massachusetts Institute of Technology and co-director of the MIT-IBM Watson AI Lab, an expert in AI and human cognition who was not involved in the study. “Machines make mistakes that humans never would because they lack our ability to reason abstractly. This work is a pivotal step towards bridging this technological gap.”
The mathematical framework developed by the researchers enables machines to organize events by how predictable they are in the future. For example, we know that swimming and running are both forms of exercising. The new technique learns how to categorize these activities on its own. The system is aware of uncertainty, providing more specific actions when there is certainty, and more generic predictions when there is not.
The technique could move computers closer to being able to size up a situation and make a nuanced decision, instead of a pre-programmed action, the researchers say. It’s a critical step in building trust between humans and computers, said Liu, co-lead author of the paper. “Trust comes from the feeling that the robot really understands people,” he explained. “If machines can understand and anticipate our behaviors, computers will be able to seamlessly assist people in daily activity.”
While the new algorithm makes more accurate predictions on benchmark tasks than previous methods, the next steps are to verify that it works outside the lab, says Vondrick. If the system can work in diverse settings, there are many possibilities to deploy machines and robots that might improve our safety, health, and security, the researchers say. The group plans to continue improving the algorithm’s performance with larger datasets and computers, and other forms of geometry.
“Human behavior is often surprising,” Vondrick commented. “Our algorithms enable machines to better anticipate what they are going to do next.” More

Crude oil production and natural gas withdrawals in the United States have lessened the country’s dependence on foreign oil and provided financial relief to U.S. consumers, but have also raised longstanding concerns about environmental damage, such as groundwater contamination.
A researcher in Syracuse University’s College of Arts and Sciences, and a team of scientists from Penn State, have developed a new machine learning technique to holistically assess water quality data in order to detect groundwater samples likely impacted by recent methane leakage during oil and gas production. Using that model, the team concluded that unconventional drilling methods like hydraulic fracturing — or hydrofracking — do not necessarily incur more environmental problems than conventional oil and gas drilling.
The two common ways to extract oil and gas in the U.S. are through conventional and unconventional methods. Conventional oil and gas are pumped from easily accessed sources using natural pressure. Conversely, unconventional oil and gas are acquired from hard-to-reach sources through a combination of horizontal drilling and hydraulic fracturing. Hydrofracking extracts natural gas, petroleum and brine from bedrock formations by injecting a mixture of sand, chemicals and water. By drilling into the earth and directing the high-pressure mixture into rock, the gas inside releases and flows out to the head of a well.
Tao Wen, assistant professor of earth and environmental sciences (EES) at Syracuse, recently led a study comparing data from different states to see which method might result in greater contamination of groundwater. They specifically tested levels of methane, which is the primary component of natural gas.
The team selected four U.S. states located in important shale zones to target for their study: Pennsylvania, Colorado, Texas and New York. One of those states — New York — banned the practice of hydrofracking in 2015 following a review by the NYS Department of Health which found significant uncertainties about health, including increased water and air pollution.
Wen and his colleagues compiled a large groundwater chemistry dataset from multiple sources including federal agency reports, journal articles, and oil and gas companies. The majority of tested water samples in their study were collected from domestic water wells. Although methane itself is not toxic, Wen says that methane contamination detected in shallow groundwater could be a risk to the relevant homeowner as it could be an explosion hazard, could increase the level of other toxic chemical species like manganese and arsenic, and would contribute to global warming as methane is a greenhouse gas.
Their model used sophisticated algorithms to analyze almost all of the retained geochemistry data in order to predict if a given groundwater sample was negatively impacted by recent oil and gas drilling.
The data comparison showed that methane contamination cases in New York — a state without unconventional drilling but with a high volume of conventional drilling — were similar to that of Pennsylvania — a state with a high volume of unconventional drilling. Wen says this suggests that unconventional drilling methods like fracking do not necessarily lead to more environmental problems than conventional drilling, although this result might be alternatively explained by the different sizes of groundwater chemistry datasets compiled for these two states.
The model also detected a higher rate of methane contamination cases in Pennsylvania than in Colorado and Texas. Wen says this difference could be attributed to different practices when drillers build/drill the oil and gas wells in different states. According to previous research, most of the methane released into the environment from gas wells in the U.S. occurs because the cement that seals the well is not completed along the full lengths of the production casing. However, no data exists to conclude if drillers in those three states use different technology. Wen says this requires further study and review of the drilling data if they become available.
According to Wen, their machine learning model proved to be effective in detecting groundwater contamination, and by applying it to other states/counties with ongoing or planned oil and gas production it will be an important resource for determining the safest methods of gas and oil drilling.
Wen and his colleagues from Penn State, including Mengqi Liu, a graduate student from the College of Information Sciences and Technology, Josh Woda, a graduate student from Department of Geosciences, Guanjie Zheng, former Ph.D. student from the College of Information Sciences and Technology, and Susan L. Brantley, distinguished professor in the Department of Geosciences and director of Earth and Environmental Systems Institute, recently had their findings published in the journal Water Research.
The team’s work was funded by National Science Foundation IIS-16-39150, US Geological Survey (104b award G16AP00079), and College of Earth and Mineral Sciences Dean’s Fund for Postdoc-Facilitated Innovation at Penn State.
Story Source:
Materials provided by Syracuse University. Original written by Dan Bernardi. Note: Content may be edited for style and length. More

A team of Virginia Tech researchers from the Department of Mechanical Engineering and the Macromolecules Innovation Institute has created a new type of soft electronics, paving the way for devices that are self-healing, reconfigurable, and recyclable. These skin-like circuits are soft and stretchy, sustain numerous damage events under load without losing electrical conductivity, and can be recycled to generate new circuits at the end of a product’s life.
Led by Assistant Professor Michael Bartlett, the team recently published its findings in Communications Materials, an open access journal from Nature Research.
Current consumer devices, such as phones and laptops, contain rigid materials that use soldered wires running throughout. The soft circuit developed by Bartlett’s team replaces these inflexible materials with soft electronic composites and tiny, electricity-conducting liquid metal droplets. These soft electronics are part of a rapidly emerging field of technology that gives gadgets a level of durability that would have been impossible just a few years ago.
The liquid metal droplets are initially dispersed in an elastomer, a type of rubbery polymer, as electrically insulated, discrete drops.
“To make circuits, we introduced a scalable approach through embossing, which allows us to rapidly create tunable circuits by selectively connecting droplets,” postdoctoral researcher and first author Ravi Tutika said. “We can then locally break the droplets apart to remake circuits and can even completely dissolve the circuits to break all the connections to recycle the materials, and then start back at the beginning.”
The circuits are soft and flexible, like skin, continuing to work even under extreme damage. If a hole is punched in these circuits, the metal droplets can still transfer power. Instead of cutting the connection completely as in the case of a traditional wire, the droplets make new connections around the hole to continue passing electricity.
The circuits will also stretch without losing their electrical connection, as the team pulled the device to over 10 times its original length without failure during the research.
At the end of a product’s life, the metal droplets and the rubbery materials can be reprocessed and returned to a liquid solution, effectively making them recyclable. From that point, they can be remade to start a new life, an approach that offers a pathway to sustainable electronics.
While a stretchy smartphone has not yet been made, rapid development in the field also holds promise for wearable electronics and soft robotics. These emerging technologies require soft, robust circuitry to make the leap into consumer applications.
“We’re excited about our progress and envision these materials as key components for emerging soft technologies,” Bartlett said. “This work gets closer to creating soft circuitry that could survive in a variety of real-world applications.”
Story Source:
Materials provided by Virginia Tech. Original written by Alex Parrish. Note: Content may be edited for style and length. More

The promise of 5G Internet of Things (IoT) networks requires more scalable and robust communication systems — ones that deliver drastically higher data rates and lower power consumption per device.
Backscatter radios — passive sensors that reflect rather than radiate energy — are known for their low-cost, low-complexity, and battery-free operation, making them a potential key enabler of this future although they typically feature low data rates and their performance strongly depends on the surrounding environment.
Researchers at the Georgia Institute of Technology, Nokia Bell Labs, and Heriot-Watt University have found a low-cost way for backscatter radios to support high-throughput communication and 5G-speed Gb/sec data transfer using only a single transistor when previously it required expensive and multiple stacked transistors.
Employing a unique modulation approach in the 5G 24/28 Gigahertz (GHz) bandwidth, the researchers have shown that these passive devices can transfer data safely and robustly from virtually any environment. The findings were reported earlier this month in the journal Nature Electronics.
Traditionally, mmWave communications, called the extremely high frequency band, is considered “the last mile” for broadband, with directive point-to-point and point-to-multipoint wireless links. This spectrum band offers many advantages, including wide available GHz bandwidth, which enables very large communication rates, and the ability to implement electrically large antenna arrays, enabling on-demand beamforming capabilities. However, such mmWave systems depend on high-cost components and systems.
The Struggle for Simplicity Versus Cost
“Typically, it was simplicity against cost. You could either do very simple things with one transistor or you need multiple transistors for more complex features, which made these systems very expensive,” said Emmanouil (Manos) Tentzeris, Ken Byers Professor in Flexible Electronics in Georgia Tech’s School of Electrical and Computer Engineering (ECE). “Now we’ve enhanced the complexity, making it very powerful but very low cost, so we’re getting the best of both worlds.” More

A new electrode that could free up 20% more light from organic light-emitting diodes has been developed at the University of Michigan. It could help extend the battery life of smartphones and laptops, or make next-gen televisions and displays much more energy efficient.
The approach prevents light from being trapped in the light-emitting part of an OLED, enabling OLEDs to maintain brightness while using less power. In addition, the electrode is easy to fit into existing processes for making OLED displays and light fixtures.
“With our approach, you can do it all in the same vacuum chamber,” said L. Jay Guo, U-M professor of electrical and computer engineering and corresponding author of the study.
Unless engineers take action, about 80% of the light produced by an OLED gets trapped inside the device. It does this due to an effect known as waveguiding. Essentially, the light rays that don’t come out of the device at an angle close to perpendicular get reflected back and guided sideways through the device. They end up lost inside the OLED.
A good portion of the lost light is simply trapped between the two electrodes on either side of the light-emitter. One of the biggest offenders is the transparent electrode that stands between the light-emitting material and the glass, typically made of indium tin oxide (ITO). In a lab device, you can see trapped light shooting out the sides rather than traveling through to the viewer.
“Untreated, it is the strongest waveguiding layer in the OLED,” Guo said. “We want to address the root cause of the problem.”
By swapping out the ITO for a layer of silver just five nanometers thick, deposited on a seed layer of copper, Guo’s team maintained the electrode function while eliminating the waveguiding problem in the OLED layers altogether. More

A new University of Liverpool study could help scientists mitigate the future spread of zoonotic and livestock diseases caused by existing viruses.
Researchers have used a form or artificial intelligence (AI) called machine-learning to predict more than 20,000 unknown associations between known viruses and susceptible mammalian species. The findings, which are published in Nature Communications, could be used to help target disease surveillance programmes.
Thousands of viruses are known to affect mammals, with recent estimates indicating that less than 1% of mammalian viral diversity has been discovered to date. Some of these viruses such as human and feline immunodeficiency viruses have a very narrow host range, whereas others such as rabies and West Nile viruses have very wide host ranges.
“Host range is an important predictor of whether a virus is zoonotic and therefore poses a risk to humans. Most recently, SARS-CoV-2 has been found to have a relatively broad host range which may have facilitated its spill-over to humans. However, our knowledge of the host range of most viruses remains limited,” explains lead researcher Dr Maya Wardeh from the University’s Institute of Infection, Veterinary and Ecological Sciences.
To address this knowledge gap, the researchers developed a novel machine learning framework to predict unknown associations between known viruses and susceptible mammalian species by consolidating three distinct perspectives — that of each virus, each mammal, and the network connecting them, respectively.
Their results suggests that there are more than five times as many associations between known zoonotic viruses and wild and semi-domesticated mammals than previously thought. In particular, bats and rodents, which have been associated with recent outbreaks of emerging viruses such as coronaviruses and hantaviruses, were linked with increased risk of zoonotic viruses.
The model also predicts a five-fold increase in associations between wild and semi-domesticated mammals and viruses of economically important domestic species such as livestock and pets.
Dr Wardeh said: “As viruses continue to move across the globe, our model provides a powerful way to assess potential hosts they have yet to encounter. Having this foresight could help to identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations.”
Dr Wardeh is currently expanding the approach to predict the ability of ticks and insects to transmit viruses to birds and mammals, which will enable prioritisation of laboratory-based vector-competence studies worldwide to help mitigate future outbreaks of vector-borne diseases.
Story Source:
Materials provided by University of Liverpool. Note: Content may be edited for style and length. More