Aurelia Butler

Inspired by living things from trees to shellfish, researchers at The University of Texas at Austin set out to create a plastic much like many life forms that are hard and rigid in some places and soft and stretchy in others. Their success — a first, using only light and a catalyst to change properties such as hardness and elasticity in molecules of the same type — has brought about a new material that is 10 times as tough as natural rubber and could lead to more flexible electronics and robotics.
The findings are published today in the journal Science.
“This is the first material of its type,” said Zachariah Page, assistant professor of chemistry and corresponding author on the paper. “The ability to control crystallization, and therefore the physical properties of the material, with the application of light is potentially transformative for wearable electronics or actuators in soft robotics.”
Scientists have long sought to mimic the properties of living structures, like skin and muscle, with synthetic materials. In living organisms, structures often combine attributes such as strength and flexibility with ease. When using a mix of different synthetic materials to mimic these attributes, materials often fail, coming apart and ripping at the junctures between different materials.
Oftentimes, when bringing materials together, particularly if they have very different mechanical properties, they want to come apart,” Page said. Page and his team were able to control and change the structure of a plastic-like material, using light to alter how firm or stretchy the material would be.
Chemists started with a monomer, a small molecule that binds with others like it to form the building blocks for larger structures called polymers that were similar to the polymer found in the most commonly used plastic. After testing a dozen catalysts, they found one that, when added to their monomer and shown visible light, resulted in a semicrystalline polymer similar to those found in existing synthetic rubber. A harder and more rigid material was formed in the areas the light touched, while the unlit areas retained their soft, stretchy properties. More

Chemical engineers have developed a machine-learning model that can accurately predict the heat capacity of the versatile metal-organic framework materials. The work shows that the overall energy costs of carbon-capture processes could be much lower than expected.
Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which make them extremely versatile for a number of applications: separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions, and even gold from water are just a few examples.
MOFs are the focus of Professor Berend Smit’s research at EPFL School of Basic Sciences, where his group employs machine learning to make breakthroughs in the discovery, design, and even categorization of the ever-increasing MOFs that currently flood chemical databases.
In a new study, Smit and his colleagues have developed a machine-learning model that predicts the heat capacity of MOFs. “This is about very classical thermodynamics,” says Smit. “How much energy is needed to heat up a material by one degree? Until now, all engineering calculations have assumed that all MOFs have the same heat capacity, for the simple reason that there is hardly any data available.” Seyed Mohamad Moosavi, a postdoc at Smit’s group, adds: “If there is no data, how can one make a machine-learning model? That looks impossible!”
The answer is the most innovative aspect of the work: a machine-learning model that predicts how the local chemical environment changes the vibrations of each atom in a MOF molecule. “These vibrations can be related to the heat capacity,” says Smit. “Before, a very expensive quantum calculation would give us a single heat capacity for a single material, but now we get up to 200 data points on these vibrations. So, by doing 200 expensive calculations, we had 40,000 data points to train the model on how these vibrations depend on their chemical environment.”
The researchers then tested their model against experimental data as a real-life check. “The results were surprisingly poor,” says Smit, “until we realized that those experiments had been done with MOFs that had solvent in their pores. So, we re-synthesized some MOFs and carefully removed the synthesis solvent -measured their heat capacity — and the results were in very good agreement with our model’s predictions!”
“Our research showcases how Artificial Intelligence (AI) can accelerate solving multi-scale problems,” says Moosavi. AI empowers us to think about our problems in a new way and even sometimes tackle them.”
To demonstrate the real-world impact of the work, engineers at Heriot-Watt University simulated the MOFs performance in a carbon capture plant. “We used quantum molecular simulations, machine learning, and chemical engineering in process simulations,” says Smit. “The results showed that with correct heat capacity values of MOFs the overall energy cost of the carbon capture process can be much lower than we originally assumed. Our work is a true multi-scale effort, with a huge impact on the techno-economic viability of currently considered solutions to tackle climate change.”
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Materials provided by Ecole Polytechnique Fédérale de Lausanne. Original written by Nik Papageorgiou. Note: Content may be edited for style and length. More

According to the WHO, antibiotic resistance is one of the biggest threats to public health. DTU researchers have created a new tool in the fight against resistant bacteria that, based on 214,000 microbiome samples, can create an overview of the problem across countries, people and environments.
In the future, even a small infection can become life-threatening for people if disease-causing bacteria become resistant to traditional treatment with antibiotics.
Based on 214,000 microbiome samples, DTU researchers have created a freely accessible platform that shows where in the world different types of resistant bacteria are found and in what quantities.
To gain an understanding of how antibiotic resistance is spreading across the world, it is important to know where, which and how many resistance genes are found in all the environments that surround us. The genes that provide resistance can spread between animals, humans, and the environment.
Data can be used for tailoring guidelines
Today, there is a large amount of data available in various online repositories and a number of limited data sets on the occurrence of resistant bacteria in, for example, sewage, soil, animals or humans. But the data is not actively being used, because it until now has been difficult to get access to, handle, and, especially, utilize these large datasets due to the computing power needed. More

Early in the COVID-19 pandemic, there was a concern that cyberbullying incidents (online threats, mistreatment or harassment) would increase because children were spending more time online. Also, in the midst of a brewing firestorm, the politicization of the link between the COVID-19 virus and its presumed origination led to a pointed rise in sinophobia (anti-Chinese sentiment) and a documented increase in harassment, bullying, and even personal and property victimization against Asian Americans.
Until now, no research has explored the extent to which cyberbullying experiences increased generally among youth in the United States during the pandemic, and especially whether Asian American youth were disproportionately targeted.
Researchers from Florida Atlantic University and the University of Wisconsin-Eau Claire conducted a first-of-its-kind nationally-representative study of 13- to 17-year-old middle and high school students in public and private schools in the United States. They investigated if these children experienced more cyberbullying during the pandemic compared to prior years. They were especially interested in whether Asian American youth were targeted more.
For the study, researchers tracked experience over time with general cyberbullying, as well as cyberbullying based on race or color. In the 2021 survey, respondents were asked whether they had been cyberbullied more or less since the start of the COVID-19 pandemic.
Results, published in the Journal of School Health, showed that prevalence of cyberbullying victimization in general increased since the beginning of the COVID-19 pandemic. Specifically, about 17 percent of all youth said they were cyberbullied in 2016 and 2019, but that proportion rose to 23 percent in 2021.
Notably, Asian American youth experienced significantly more cyberbullying than their counterparts since the COVID-19 pandemic began. In 2019, Asian American youth in the U.S. were the least likely to have experienced cyberbullying (fewer than 10 percent reported being targeted overall and only about 7 percent were targeted because of their race similar to white/Caucasian youth).
In 2021, however, 19 percent of Asian American youth said they had been cyberbullied, and approximately 1 in 4 (23.5 percent) indicated they were victimized online because of their race/color. Additionally, Asian American youth were the only racial group where the majority (59 percent) reported more cyberbullying since the start of the COVID-19 pandemic.
“Race-based bullying has been linked to traumatic stress, poorer mental health outcomes, and even neurobiological harm,” said Sameer Hinduja, Ph.D., co-author, professor, FAU School of Criminology and Criminal Justice within the College of Social Work and Criminal Justice, co-director of the Cyberbullying Research Center, and a faculty associate at the Berkman Klein Center at Harvard University. “COVID-19 racism against Asian Americans is associated with lower psychological well-being as well as problematic internalizing and externalizing behaviors. In fact, some experts say that this population may be more susceptible to internalizing harm stemming from online victimization because of cultural stigmas among Asian Americans about help-seeking and mental health needs.”
As more adolescents continue to spend more time online, cyberbullying victimization may increase across all racial groups. In the current politicized environment, Asian Americans may continue to be targeted because of their race.
“COVID-19 will likely not go away anytime soon,” said Hinduja. “We hope findings from our study will further spotlight the reality of cyberbullying experiences among Asian American youth in a way that compels additional actions in school policies, pedagogy, state and federal laws, messaging campaigns, and other program implementations so that these youth are more meaningfully supported.”
Study co-author is Justin W. Patchin, Ph.D., Department of Political Science, University of Wisconsin-Eau Claire and co-director of the Cyberbullying Research Center.
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Materials provided by Florida Atlantic University. Original written by Gisele Galoustian. Note: Content may be edited for style and length. More

People often wonder why one nutritional study tells them that eating too many eggs, for instance, will lead to heart disease and another tells them the opposite. The answer to this and other conflicting food studies may lie in the use of statistics, according to a report published today in the American Journal of Clinical Nutrition.
The research, led by scientists at the University of Leeds and The Alan Turing Institute — The National Institute for data science and artificial intelligence — reveals that the standard and most common statistical approach to studying the relationship between food and health can give misleading and meaningless results.
Lead author Georgia Tomova, a PhD researcher in the University of Leeds’ Institute for Data Analytics and The Alan Turing Institute, said: “These findings are relevant to everything we think we know about the effect of food on health.
“It is well known that different nutritional studies tend to find different results. One week a food is apparently harmful and the next week it is apparently good for you.”
The researchers found that the widespread practice of statistically controlling, or allowing for, someone’s total energy intake can lead to dramatic changes in the interpretation of the results.
Controlling for other foods eaten can then further skew the results, so that a harmful food appears beneficial or vice versa. More

An odor machine, so-called olfactometer, makes it possible to smell in VR environments. First up is a “wine tasting game” where the user smells wine in a virtual wine cellar and gets points if the guess on aromas in each wine is correct. The new technology that can be printed on 3D printers has been developed in collaboration between Stockholm University and Malmö University. The research, funded by the Marianne and Marcus Wallenberg Foundation, was recently published in the International Journal of Human — Computer Studies.
“We hope that the new technical possibilities will lead to scents having a more important role in game development, says Jonas Olofsson, professor of psychology and leader of the research project at Stockholm University.
In the past, computer games have focused mostly on what we can see — moving images on screens. Other senses have not been present. But an interdisciplinary research group at Stockholm University and Malmö University has now constructed a scent machine that can be controlled by a gaming computer. In the game, the participant moves in a virtual wine cellar, picking up virtual wine glasses containing different types of wine, guessing the aromas. The small scent machine is attached to the VR system’s controller, and when the player lifts the glass, it releases a scent.
“The possibility to move on from a passive to a more active sense of smell in the game world paves the way for the development of completely new smell-based game mechanics based on the players’ movements and judgments,” says Simon Niedenthal, interaction and game researcher at Malmö University.
The olfactometer consists of four different valves each connected to a channel. In the middle there is a fan sucking the air into a tube. With the help of the computer, the player can control the four channels so that they open to different degrees and provide different mixtures of scent. Scent blends that can mimic the complexity of a real wine glass. The game has different levels of difficulty with increasing levels of complexity.
“In the same way that a normal computer game becomes more difficult the better the player becomes; the scent game can also challenge players who already have a sensitive nose. This means that the scent machine can even be used to train wine tasters or perfumers,” says Jonas Olofsson. More

Have you ever been accused (or accused someone else) of wasting too much time by looking at a cellphone? Turns out, that time might not be wasted time at all.
According to research recently published by Kaveh Abhari of San Diego State University and Isaac Vaghefi of City University of New York, using existing smartphone applications to monitor cellphone screen time can enhance focused or mindful cellphone usage, which, in turn, leads to higher perceived productivity and user satisfaction. The research was recently published in AIS Transactions on Human-Computer Interaction (THCI).
The Positive Effect of Self-Monitoring
Abhari (associate professor of management information systems at SDSU’s Fowler College of Business) and Vaghefi (assistant professor of information systems at the Zicklin School of Business at Baruch College) said while there was substantial research establishing the negative effects of cellphone screen time (intolerance, withdrawal, and conflict with job-related tasks), their research was designed to determine if self-regulatory behaviors could lead to modified user behavior for more positive outcomes.
“We theorized that individuals who tracked their cellphone usage and set goals surrounding that usage tended to have enhanced productivity and contentment with their productivity as they met their stated objectives,” said Abhari. “Previous research has shown that goal setting tends to raise performance expectations and we wanted to see if this theory held true for smartphone screen time as well.”
Putting it to the Test
To make this determination, the researchers surveyed 469 participating university undergraduate students in California, New York, and Hawaii. The three-week survey required all participants to complete four questionnaires and about half of them were required to download a screen-monitoring application to their phones. This app allowed users to monitor and set limits or goals with their cellphone screen time.
When the results were analyzed, researchers measured the perceived productivity of screen time reported by those surveyed, as well as the amount of screen time and the fatigue associated with self-monitoring. They also reviewed participants’ contentment with their productivity achieved through cellphone screen time. “Self-monitoring appears necessary to encourage the optimized use of smartphones,” said Abhari. “The results suggest that optimizing but not minimizing screen time is more likely to increase user productivity.”
The Effect of Fatigue
However, the researchers also found that self-monitoring induces fatigue and weakens the effect on productivity, though it was not a significant factor affecting the relationship between self-monitoring and contentment with productivity achievement.
In conclusion, Abhari and Vaghefi determined that while uncontrolled cellphone use (or cellphone addiction) could negatively impact people’s lives, monitored screen time — particularly monitored screen time with specific goals in mind — can result in positive outcomes and higher overall user satisfaction. “This study could lead system developers to embed features into mobile devices that enable self-monitoring,” said Abhari. “These features could improve quality screen time and enhance the relationship between humans and digital technology.”
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Materials provided by San Diego State University. Original written by Suzanne Finch. Note: Content may be edited for style and length. More

The key to maximizing traditional or quantum computing speeds lies in our ability to understand how electrons behave in solids, and a collaboration between the University of Michigan and the University of Regensburg captured electron movement in attoseconds — the fastest speed yet.
Seeing electrons move in increments of one quintillionth of a second could help push processing speeds up to a billion times faster than what is currently possible. In addition, the research offers a “game-changing” tool for the study of many-body physics.
“Your current computer’s processor operates in gigahertz, that’s one billionth of a second per operation,” said Mackillo Kira, U-M professor of electrical engineering and computer science, who led the theoretical aspects of the study published in Nature. “In quantum computing, that’s extremely slow because electrons within a computer chip collide trillions of times a second and each collision terminates the quantum computing cycle.
“What we’ve needed, in order to push performance forward, are snapshots of that electron movement that are a billion times faster. And now we have it.”
Rupert Huber, professor of physics at the University of Regensburg and corresponding author of the study, said the result’s potential impact in the field of many-body physics could surpass its computing impact.
“Many-body interactions are the microscopic driving forces behind the most coveted properties of solids — ranging from optical and electronic feats to intriguing phase transitions — but they have been notoriously difficult to access,” said Huber, who led the experiment. “Our solid-state attoclock could become a real game changer, allowing us to design novel quantum materials with more precisely tailored properties and help develop new materials platforms for future quantum information technology.”
To see electron movement within two-dimensional quantum materials, researchers typically use short bursts of focused extreme ultraviolet (XUV) light. Those bursts can reveal the activity of electrons attached to an atom’s nucleus. But the large amounts of energy carried in those bursts prevent clear observation of the electrons that travel through semiconductors — as in current computers and in materials under exploration for quantum computers. More