Aurelia Butler

Scientists seek stable, high-energy batteries designed for the electric grid.
Bringing new sources of renewable energy like wind and solar power onto the electric grid will require specially designed large batteries that can charge when the sun is shining and give energy at night. One type of battery is especially promising for this purpose: the flow battery. Flow batteries contain two tanks of electrically active chemicals that exchange charge and can have large volumes that hold a lot of energy.
For researchers working on flow batteries, their chief concern involves finding target molecules that offer the ability to both store a lot of energy and remain stable for long periods of time.
To find the right flow battery molecules, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of artificial intelligence (AI) to search through a vast chemical space of over a million molecules. Discovering the right molecules requires optimizing between several different characteristics. “In these batteries, we know that a majority of the molecules that we need will have to satisfy multiple properties,” said Argonne chemist Rajeev Assary. “By optimizing several properties simultaneously, we have a better shot of finding the best possible chemistry for our battery.”
In a new study that follows on from work done last year, Assary and his colleagues in Argonne’s Joint Center for Energy Storage Research modeled anolyte redoxmers, or electrically active molecules in a flow battery. For each redoxmer, the researchers identified three properties that they wanted to optimize. The first two, reduction potential and solvation free energy, relate to how much energy the molecule can store. The third, fluorescence, serves as a kind of self-reporting marker that indicates the overall health of the battery.
Because it is extraordinarily time consuming to calculate the properties of interest for all potential candidates, the researchers turned to a machine learning and AI technique called active learning, in which a model can actually train itself to identify increasingly plausible targets. “We’re essentially looking for needles in haystacks,” said Argonne postdoctoral researcher Hieu Doan. “When our model finds something that looks like a needle, it teaches itself how to find more.”
For the most efficient use of active learning, the researchers started with a fairly small “haystack” — a dataset of 1400 redoxmer candidates whose properties they already knew from quantum mechanical simulations. By using this dataset as practice, they were able to see that the algorithm correctly identified the molecules with the best properties. More

Emerging 5G wireless systems are designed to support high-bandwidth and low-latency networks connecting everything from autonomous robots to self-driving cars. But these large and complex communication networks could also pose new security concerns.
Encryption methods now used to secure communications from eavesdroppers can be challenging to scale towards such high-speed and ultra-low latency systems for 5G and beyond. This is because the very nature of encryption requires exchange of information between sender and receiver to encrypt and decrypt a message. This exchange makes the link vulnerable to attacks; it also requires computing that increases latency. Latency, the amount of time between sending instructions on a network and the arrival of the data, is a key measure for tasks like autonomous driving and industrial automation. For networks that support latency-critical systems such as self-driving cars, robots and other cyber-physical systems, minimizing time-to-action is critical.
Seeking to close this security gap, Princeton University researchers have developed a methodology that incorporates security in the physical nature of the signal. In a report published Nov. 22 in Nature Electronics, the researchers describe how they developed a new millimeter-wave wireless microchip that allows secure wireless transmissions to prevent interception without reducing latency, efficiency and speed of the 5G network. According to senior researcher Kaushik Sengupta, the technique should make it very challenging to eavesdrop on such high-frequency wireless transmissions, even with multiple colluding bad actors.
“We are in a new era of wireless — the networks of the future are going to be increasingly complex while serving a large set of different applications that demand very different features,” Sengupta said. “Think low-power smart sensors in your home or in an industry, high-bandwidth augmented reality or virtual reality, and self-driving cars. To serve this and serve this well, we need to think about security holistically and at every level.”
Instead of relying on encryption, the Princeton method shapes the transmission itself to foil would-be eavesdroppers. To explain this, it helps to picture wireless transmissions as they emerge from an array of antennas. With a single antenna, radio waves radiate from the antenna in a wave. When there are multiple antennas working as an array, these waves interfere with each other like waves of water in a pond. The interference increases the size of some wave crests and troughs and smooths out others.
An array of antennas is able to use this interference to direct a transmission along a defined path. But besides the main transmission, there are secondary paths. These secondary paths are weaker than the main transmission, but in a typical system they contain the exact same signal as the main path. By tapping these paths, potential eavesdroppers can compromise the transmission. More

A new “image analysis pipeline” is giving scientists rapid new insight into how disease or injury have changed the body, down to the individual cell.
It’s called TDAExplore, which takes the detailed imaging provided by microscopy, pairs it with a hot area of mathematics called topology, which provides insight on how things are arranged, and the analytical power of artificial intelligence to give, for example, a new perspective on changes in a cell resulting from ALS and where in the cell they happen, says Dr. Eric Vitriol, cell biologist and neuroscientist at the Medical College of Georgia.
It is an “accessible, powerful option” for using a personal computer to generate quantitative — measurable and consequently objective — information from microscopic images that likely could be applied as well to other standard imaging techniques like X-rays and PET scans, they report in the journal Patterns.
“We think this is exciting progress into using computers to give us new information about how image sets are different from each other,” Vitriol says. “What are the actual biological changes that are happening, including ones that I might not be able to see, because they are too minute, or because I have some kind of bias about where I should be looking.”
At least in the analyzing data department, computers have our brains beat, the neuroscientist says, not just in their objectivity but in the amount of data they can assess. Computer vision, which enables computers to pull information from digital images, is a type of machine learning that has been around for decades, so he and his colleague and fellow corresponding author Dr. Peter Bubenik, a mathematician at the University of Florida and an expert on topological data analysis, decided to partner the detail of microscopy with the science of topology and the analytical might of AI. Topology and Bubenik were key, Vitriol says.
Topology is “perfect” for image analysis because images consist of patterns, of objects arranged in space, he says, and topological data analysis (the TDA in TDAExplore) helps the computer also recognize the lay of the land, in this case where actin — a protein and essential building block of the fibers, or filaments, that help give cells shape and movement — has moved or changed density. It’s an efficient system, that instead of taking literally hundreds of images to train the computer how to recognize and classify them, it can learn on 20 to 25 images. More

The ancient arts of origami, the art of paper-folding, and kirigami, the art of paper-cutting, have gained popularity in recent years among researchers building mechanical metamaterials. Folding and cutting 2D thin-film materials transforms them into complex 3D structures and shapes with unique and programmable mechanical properties.
In Applied Physics Reviews, by AIP Publishing, researchers in the United States and China categorize origami- and kirigami-based mechanical metamaterials, artificially engineered materials with unusual mechanical properties, into six groups based on two different criteria.
“Origami and kirigami are, by nature, mechanical metamaterials, because their properties are mainly determined by how the crease patterns and/or cuts are made and just slightly depend on the material that folds the origami or kiragami,” said author Hanqing Jiang.
The researchers divided the mechanical metamaterials into three categories that include origami-based metamaterials (folding only), kirigami-based metamaterials (cutting only), and hybrid origami-kirigami metamaterials (both folding and cutting). The hybrid origami-kirigami metamaterials, in particular, offer great potential in shape morphing.
Each group was subdivided into a rigid or deformable category based on the elastic energy landscape. Metamaterials were classified as rigid if energy was stored in the creases or linkages only. Metamaterials were put in the deformable category if energy was stored in both creases or linkages and panels.
The researchers want to discover new origami and kirigami designs, especially curved origami designs, hybrid origami-kirigami designs, modular designs, and hierarchical designs.
They plan to focus on the selection of new materials for origami- and kirigami-based mechanical metamaterials. Traditionally paper is used to prototype metamaterials but there are limits based on the fragility and plasticity of paper. To design for real-world applications, it will be helpful to explore materials with different properties such as thin or thick, soft or hard, and elastic or plastic.
They want to use the energy landscape and energy distribution as two powerful tools to analyze mechanical performances of origami and kirigami and will seek to carefully design the actuation method of origami- and kirigami-based mechanical metamaterials.
“Origami- and kiragami-based mechanical metamaterials can be applied in many fields, including flexible electronics, medical devices, robotics, civil engineering and aerospace engineering,” said Jiang.
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Materials provided by American Institute of Physics. Note: Content may be edited for style and length. More

Mathematicians have analysed global COVID-19 data to identify two constants which can drastically change a country’s rate of infection.
An international team of researchers led by Professor Alexander Gorban from the University of Leicester used available data from 13 countries to determine the rate of stress response, or ‘mobilisation’ and the rate of spontaneous exhaustion, or ‘demobilisation’.
Their findings, published in Scientific Reports, show that social stress — which varied broadly across the countries studied — drives the multi-wave dynamics of COVID-19 outbreaks.
The study analysed data from China, the USA, UK, Germany, Colombia, Italy, Spain, Israel, Russia, France, Brazil, India, and Iran — and contributed to the research team’s proposed new system of models, which combine the dynamics of the established concept of social stress with classical epidemic models.
Alexander Gorban is a Professor of Applied Mathematics at the University of Leicester, and Director of the Centre for Artificial Intelligence, Data Analysis and Modelling. Professor Gorban said:
“We tried to use the pandemic for research and quantify the social and cultural differences between countries. We measured how variable countries are in two processes: mobilisation of people for rational protective behaviour and exhaustion of this mobilisation with destroying of rational behaviour. More

Science has the technology to measure the activity of every gene within a single individual cell, and just one experiment can generate thousands of cells worth of data. Researchers at Lund University in Sweden have now revolutionised the way this data is analysed — by using 3D video gaming technology. The study is published in the journal iScience.
Advanced techniques in DNA and RNA sequencing have opened up the possibility of studying individual cells in tissue in a more comprehensive way than was previously possible. The big challenge with these sequencing techniques is that they lead to large amounts of data.
“When you want to distinguish cancer cells from normal cells, for example, you need to examine thousands of cells to get a proper understanding, which translates into enormous amounts of numerical data,” says Shamit Soneji, researcher in computational biology at Lund University.
To make this data comprehensible, each cell is mathematically positioned in three-dimensional space to form a “roadmap” of the cells, and how they relate to each other. However, these maps can be difficult to navigate using a regular desktop computer.
“To be able to walk around your own data and manipulate it intuitively and efficiently gives it a whole new understanding. I would actually go so far as to say that one thinks differently in VR, thanks to the technique’s ability to involve your body in the analysis process,” explains Mattias Wallergård. researcher in interaction design and virtual reality at Lund University.
The Lund University team have developed the software CellexalVR; a virtual reality environment that enables researchers to use intuitive tools to explore all their data in one place. 3D maps of cells that have been calculated from gene activity and other information captured from individual cells can be displayed, and the researcher can clearly see which genes are active when certain cell types are formed.
Using a VR headset, the user has a complete universe of cell populations in front of them, and can more accurately determine how cells relate to one another. Using two hand controllers, they can select cells of interest for further analysis with simple hand gestures as if they were physical objects.
Since space is not an issue, it is possible to have several cellular maps in the same “room” and compare them side by side, something that is difficult on a traditional computer screen. Researchers can also meet in this VR world to analyze data together, despite being in different places geographically.
“Even if you are not familiar with computer programming, this type of analysis is open to everyone. A virtual world is a fast developing area of research that has enormous potential for scientists that need to access and process big-data in a more interactive and collaborative way,” concludes Shamit Soneji.
The software can be downloaded for free at https://www.cellexalvr.med.lu.se/
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Materials provided by Lund University. Note: Content may be edited for style and length. More

The perception that boys are more interested than girls in computer science and engineering starts as young as age six, according to a new study published in Proceedings of the National Academy of Sciences. That may be one reason why girls and women are underrepresented in these STEM career fields, reports study co-author Allison Master, assistant professor at the University of Houston College of Education.
“Gender-interest stereotypes that say ‘STEM is for boys’ begin in grade school, and by the time they reach high school, many girls have made their decision not to pursue degrees in computer science and engineering because they feel they don’t belong,” said Master.
Researchers at UH and the University of Washington surveyed nearly 2,500 students in firstthrough12th grade from diverse racial and socioeconomic backgrounds. The results of those studies were combined with laboratory experiments to provide important insights into how stereotypes impact children’s motivation.
More children believed girls had less interest than boys in key STEM fields. Specifically, 63% of the students believed girls were less interested in engineering than boys were, while 9% believed girls were more interested in the subject. Regarding computer science, 51% thought girls had less interest while 14% thought girls had more interest than boys.
These interest patterns play out in the job market. According to United States Census Bureau statistics, while women make up nearly half of the workforce, they account for only 25% of computer scientists and 15% of engineers.
Researchers say educators, parents and policymakers can help close these gender gaps by introducing girls to high quality computer science and engineering activities in elementary school before stereotype endorsements take root. They also suggest educators who wish to promote girls’ interest and engagement in STEM should consider using inclusive programs designed to encourage girls’ sense of belonging in STEM.
The laboratory experiments gave children a choice between computer science activities. Fewer girls (only 35%) chose a computer science activity they believed boys were more interested in, compared to the 65% of girls who chose an activity for which they believed boys and girls were equally interested.
“It’s time for all stakeholders to be united in sending the message that girls can enjoy STEM just as much as boys do, which will help draw them into STEM activities,” added Master, who directs UH’s Identity and Academic Motivation (I AM) Lab.
Co-authors on the study are Andrew N. Meltzoff of the University of Washington, Seattle’s Institute for Learning & Brain Sciences; and Sapna Cheryan, University of Washington, Seattle’s Department of Psychology.
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Materials provided by University of Houston. Original written by Sara Tubbs. Note: Content may be edited for style and length. More

Every day, cytologists around the world use optical microscopes to analyze and classify samples of bone marrow cells thousands of times. This method to diagnose blood diseases was established more than 150 years ago, but it suffers from being very complex. Looking for rare but diagnostically important cells is both a laborious and time-consuming task. Artificial intelligence has the potential to boost this method — however it needs a large amount of high-quality data to train an AI algorithm.
Largest open-source database for bone marrow cell images
The Helmholtz Munich researchers developed the largest open access database on microscopic images of bone marrow cells to date. The database consists of more than 170,000 single-cell images from over 900 patients with various blood diseases. It is the result of a collaboration from Helmholtz Munich with the LMU University Hospital Munich, the MLL Munich Leukemia Lab (one of the largest diagnostic providers in this field worldwide) and Fraunhofer Institute for Integrated Circuits.
Using the database to boost artificial intelligence
“On top of our database, we have developed a neural network that outperforms previous machine learning algorithms for cell classification in terms of accuracy, but also in terms of generalizability,” says Christian Matek, lead author of the new study. The deep neural network is a machine learning concept specifically designed to process images. “The analysis of bone marrow cells has not yet been performed with such advanced neural networks,” Christian Matek explains, “which is also due to the fact that high-quality, public datasets have not been available until now.”
The researchers aim to further expand their bone marrow cell database to capture a broader range of findings and to prospectively validate their model. “The database and the model are freely available for research and training purposes — to educate professionals or as a reference for further AI-based approaches e.g. in blood cancer diagnostics,” says study leader Carsten Marr.
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Materials provided by Helmholtz Zentrum München – German Research Center for Environmental Health. Note: Content may be edited for style and length. More