Aurelia Butler

Researchers have developed an automatic drawing machine that uses pens and pencils to draw metamaterials onto paper. They demonstrated the new approach by using it to make three metamaterials that can be used to manipulate the microwave region of the electromagnetic spectrum.
Metamaterials are artificially engineered composite materials that derive their properties from patterned microstructures, rather than the chemical composition of the materials themselves. The exact shape, geometry, size, orientation and arrangement of the structures can be used to manipulate electromagnetic waves in ways that aren’t possible with conventional materials.
“Metamaterials, especially those used as absorbers, generally need to be thin, lightweight, wide and strong, but it isn’t easy to create thin and lightweight devices using traditional substrates,” said research team leader Junming Zhao from Nanjing University in China. “Using paper as the substrate can help meet these requirements while also lending itself to metasurfaces that conform to a surface or that are mechanically reconfigurable.”
In the journal Optical Materials Express, the researchers describe their new technique, which uses aballpoint pen with conductive ink to draw conductors and mechanical pencils to draw resistors and resistive films. They incorporated this process into a computer-controlled drawing machine to make it more automatic and accurate.
“Although paper-based metamaterials have been made previously using inkjet printing technology, our drawing technique is lower cost, simpler and more flexible,” said Zhao. “Our method could be useful for making reconfigurable antennas and metalenses as well as metamaterial devices that absorb incident electromagnetic energy from cell phones or other sources.”
Automated drawing
The new drawing machine uses pens with ink containing conductive material or normal mechanical pencils with varying graphite content. It has three stepper motors, two of which control the movement of the pen or pencil in the horizontal plane, while the other lifts or drops the writing instrument in the vertical plane. The parameters of the drawing machine, such as the movement speed, are controlled by a computer. More

Using existing experimental and computational resources, a multi-institutional team has developed an effective method for measuring high-dimensional qudits encoded in quantum frequency combs, which are a type of photon source, on a single optical chip.
Although the word “qudit” might look like a typo, this lesser-known cousin of the qubit, or quantum bit, can carry more information and is more resistant to noise — both of which are key qualities needed to improve the performance of quantum networks, quantum key distribution systems and, eventually, the quantum internet.
Classical computer bits categorize data as ones or zeroes, whereas qubits can hold values of one, zero orboth — simultaneously — owing to superposition, which is a phenomenon that allows multiple quantum states to exist at the same time. The “d” in qudit stands for the number of different levels or values that can be encoded on a photon. Traditional qubits have two levels, but adding more levels transforms them into qudits.
Recently, researchers from the U.S. Department of Energy’s Oak Ridge National Laboratory, Purdue University and the Swiss Federal Institute of Technology Lausanne, or EPFL, fully characterized an entangled pair of eight-level qudits, which formed a 64-dimensional quantum space — quadrupling the previous record for discrete frequency modes. These results were published in Nature Communications.
“We’ve always known that it’s possible to encode 10- or 20-level qudits or even higher using the colors of photons, or optical frequencies, but the problem is that measuring these particles is very difficult,” said Hsuan-Hao Lu, a postdoctoral research associate at ORNL. “That’s the value of this paper — we found an efficient and novel technique that is relatively easy to do on the experimental side.”
Qudits are even more difficult to measure when they are entangled, meaning they share nonclassical correlations regardless of the physical distance between them. Despite these challenges, frequency-bin pairs — two qudits in the form of photons that are entangled in their frequencies — are well suited to carrying quantum information because they can follow a prescribed path through optical fiber without being significantly modified by their environment. More

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.”
Story Source:
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.
Story Source:
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