Aurelia Butler

Perovskites, a family of materials with unique electric properties, show promise for use in a variety fields, including next-generation solar cells. A Penn State-led team of scientists created a new process to fabricate large perovskite devices that is more cost- and time-effective than previously possible and that they said may accelerate future materials discovery.
“This method we developed allows us to easily create very large bulk samples within several minutes, rather than days or weeks using traditional methods,” said Luyao Zheng, a postdoctoral researcher in the Department of Materials Science at Penn State and lead author on the study. “And our materials are high quality — their properties can compete with single-crystal perovskites.”
The researchers used a sintering method called the electrical and mechanical field-assisted sintering technique (EM-FAST) to create the devices. Sintering is a commonly used process to compress fine powders into a solid mass of material using heat and pressure.
A typical process for making perovskites involves wet chemistry — the materials are liquefied in a solvent solution and then solidified into thin films. These materials have excellent properties, but the approach is expensive and inefficient for creating large perovskites and the solvents used may be toxic, the scientists said.
“Our technique is the best of both worlds,” said Bed Poudel, a researcher professor at Penn State and a co-author. “We get single-crystal-like properties, and we don’t have to worry about size limitations or any contamination or yield of toxic materials.”
Because it uses dry materials, the EM-FAST technique opens the door to include new dopants, ingredients added to tailor device properties, that are not compatible with the wet chemistry used to make thin films, potentially accelerating the discovery of new materials, the scientists said.

“This opens up possibilities to design and develop new classes of materials, including better thermoelectric and solar materials, as well as X- and γ-ray detectors,” said Amin Nozariasbmarz, assistant research professor at Penn State and a co-author. “Some of the applications are things we already know, but because this is a new technique to make new halide perovskite materials with controlled properties, structures, and compositions, maybe there is room in the future for new breakthroughs to come from that.”
In addition, the new process allows for layered materials — one powder underneath another — to create designer compositions. In the future, manufactures could design specific devices and then directly print them from dry powders, the scientists said.
“We anticipate this FAST perovskite would open another dimension for high throughput material synthesis, future manufacturing directly printing devices from powder and accelerating the material discovery of new perovskite compositions,” said Kai Wang, an assistant research professor at Penn State and a co-author.
EM-FAST, also known as spark plasma sintering, involves applying electric current and pressure to powders to create new materials. The process has a 100% yield — all the raw ingredients go into the final device, as opposed to 20 to 30% in solution-based processing.
The technique produced perovskite materials at .2 inch per minute, allowing scientists to create quickly create large devices that maintained high performance in laboratory tests. The team reported their findings in the journal Nature Communications.

Penn State scientists have long used EM-FAST to create thermoelectric devices. This work represents the first attempt to create perovskite materials with the technique, the scientists said.
“Because of the background we have, we were talking and thought we could change some parameters and try this with perovskites,” Nozariasbmarz said. “And it just opened a door to a new world. This paper is a link to that door — to new materials and new properties.”
Other Penn State researchers on the project were Wenjie Li and Dong Yang, assistant research professors; Ke Wang, staff scientist in the Materials Research Institute; Jungjin Yoon, Tao Ye and Yu Zhang, postdoctoral researchers; Yuchen Hou, doctoral candidate; and Shashank Priya, former associate vice president for research and director of strategic initiatives and professor of materials science and engineering.
Also contributing was Mohan Sanghadasa, U.S. Army Combat Capabilities Development Command Aviation and Missile Center.
Researchers received support from the National Science Foundation Industry-University Research Partnerships’ Center for Energy Harvesting Materials and Systems, U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, Air Force Office of Scientific Research, and Office of Naval Research and Army Research. More

Octopus arms coordinate nearly infinite degrees of freedom to perform complex movements such as reaching, grasping, fetching, crawling, and swimming. How these animals achieve such a wide range of activities remains a source of mystery, amazement, and inspiration. Part of the challenge comes from the intricate organization and biomechanics of the internal muscles.
This problem was tackled in a multidisciplinary project led by Prashant Mehta and Mattia Gazzola, professors of mechanical science & engineering at the University of Illinois Urbana-Champaign. As reported in Proceedings of the Royal Society A, the two researchers and their groups have developed a physiologically accurate model of octopus arm muscles. “Our model, the first of its kind, not only provides insight into the biological problem, but a framework for design and control of soft robots going forward,” Mehta said.
The impressive capabilities of octopus arms have long served as an inspiration for the design and control of soft robots. Such soft robots have the potential to perform complex tasks in unstructured environments while operating safely around humans, with applications ranging from agriculture to surgery.
Graduate student Heng-Sheng Chang, the study’s lead author, explained that soft-bodied systems like octopuses’ arms present a major modeling and control challenge. “They are driven by three major internal muscle groups — longitudinal, transverse, and oblique — that cause the arm to deform in several modes — shearing, extending, bending, and twisting,” he said. “This endows the soft muscular arms with significant freedom, unlike their rigid counterparts.”
The team’s key insight was to express the arm musculature using a stored energy function, a concept borrowed from the theory of continuum mechanics. Postdoctoral scholar and corresponding author Udit Halder explained that “The arm rests at the minimum of an energy landscape. Muscle actuations modify the stored energy function, thus shifting the equilibrium position of the arm and guiding the motion.”
Interpreting the muscles using stored energy dramatically simplifies the arm’s control design. In particular, the study outlines an energy-shaping control methodology to compute the necessary muscle activations for solving manipulation tasks such as reaching and grasping. When this approach was numerically demonstrated in the software environment Elastica, This model led to remarkably life-like motion when an octopus arm was simulated in three dimensions. Moreover, according to Halder, “Our work offers mathematical guarantees of performance that are often lacking in alternative approaches, including machine learning.”
“Our work is part of a larger ecosystem of ongoing collaborations at the University of Illinois,” Mehta said. “Upstream, there are biologists who perform experiments on octopuses. Downstream, there are roboticists who are taking these mathematical ideas and applying them to real soft robots.”
Mehta’s and Gazzola’s groups collaborated with Rhanor Gillette, Illinois Professor Emeritus of molecular and integrative physiology, to incorporate observed octopus physiology into their mathematical model for this study. Future work will discuss the biological implications of energy-based control. In addition, the researchers are collaborating with Girish Krishnan, an Illinois professor of industrial & enterprise systems engineering, to incorporate their mathematical ideas into real soft robot design and control. This will not only create a systematic way of controlling soft robots, but will also provide a deeper understanding of their working mechanisms.
This work was part of the CyberOctopus project, a multidisciplinary university research initiative in the University of Illinois’ Coordinated Science Laboratory supported by the Office of Naval Research. More

A team from the University of Michigan has developed a new software tool to help researchers across the life sciences more efficiently analyze animal behaviors. 
The open-source software, LabGym, capitalizes on artificial intelligence to identify, categorize and count defined behaviors across various animal model systems.
Scientists need to measure animal behaviors for a variety of reasons, from understanding all the ways a particular drug may affect an organism to mapping how circuits in the brain communicate to produce a particular behavior.
Researchers in the lab of U-M faculty member Bing Ye, for example, analyze movements and behaviors in Drosophila melanogaster—or fruit flies—as a model to study the development and functions of the nervous system. Because fruit flies and humans share many genes, these studies of fruit flies often offer insights into human health and disease.
“Behavior is a function of the brain. So analyzing animal behavior provides essential information about how the brain works and how it changes in response to disease,” said Yujia Hu, a neuroscientist in Ye’s lab at the U-M Life Sciences Institute and lead author of a Feb. 24 Cell Reports Methods study describing the new software.  
But identifying and counting animal behaviors manually is time-consuming and highly subjective to the researcher who is analyzing the behavior. And while a few software programs exist to automatically quantify animal behaviors, they present challenges.

“Many of these behavior analysis programs are based on pre-set definitions of a behavior,” said Ye, who is also a professor of cell and developmental biology at the Medical School. “If a Drosophila larva rolls 360 degrees, for example, some programs will count a roll. But why isn’t 270 degrees also a roll? Many programs don’t necessarily have the flexibility to count that, without the user knowing how to recode the program.”
Thinking more like a scientist
 
To overcome these challenges, Hu and his colleagues decided to design a new program that more closely replicates the human cognition process—that “thinks” more like a scientist would—and is more user-friendly for biologists who may not have expertise in coding. Using LabGym, researchers can input examples of the behavior they want to analyze and teach the software what it should count. The program then uses deep learning to improve its ability to recognize and quantify the behavior.
One new development in LabGym that helps it apply this more flexible cognition is the use of both video data and a so-called “pattern image” to improve the program’s reliability. Scientists use videos of animals to analyze their behavior, but videos involve time series data that can be challenging for AI programs to analyze.

To help the program identify behaviors more easily, Hu created a still image that shows the pattern of the animal’s movement by merging outlines of the animal’s position at different timepoints. The team found that combining the video data with the pattern images increased the program’s accuracy in recognizing behavior types.
LabGym is also designed to overlook irrelevant background information and consider both the animal’s overall movement and the changes in position over space and time, much as a human researcher would. The program can also track multiple animals simultaneously.
Species flexibility improves utility
 
Another key feature of LabGym is its species flexibility, Ye said. While it was designed using Drosophila, it is not restricted to any one species.
“That’s actually rare,” he said. “It’s written for biologists, so they can adapt it to the species and the behavior they want to study without needing any programming skills or high-powered computing.”
After hearing a presentation about the program’s early development, U-M pharmacologist Carrie Ferrario offered to help Ye and his team test and refine the program in the rodent model system she works with.
Ferrario, an associate professor of pharmacology and adjunct associate professor of psychology, studies the neural mechanisms that contribute to addiction and obesity, using rats as a model system. To complete the necessary observation of drug-induced behaviors in the animals, she and her lab members have had to rely largely on hand-scoring, which is subjective and extremely time-consuming.
“I’ve been trying to solve this problem since graduate school, and the technology just wasn’t there, in terms of artificial intelligence, deep learning and computation,” Ferrario said. “This program solved an existing problem for me, but it also has really broad utility. I see the potential for it to be useful in almost limitless conditions to analyze animal behavior.”
The team next plans to further refine the program to improve its performance under even more complex conditions, such as observing animals in nature.
This research was supported by the National Institutes of Health.
In addition to Ye, Hu and Ferrario, study authors are: Alexander Maitland, Rita Ionides, Anjesh Ghimire, Brendon Watson, Kenichi Iwasaki, Hope White and Yitao Xi of the University of Michigan, and Jie Zhou of Northern Illinois University.
Study: LabGym: quantification of user-defined animal behaviors 1 using learning-based holistic assessment (DOI: 10.1016/j.crmeth.2023.100415) (available once embargo lifts) More

Imagine using an online banking app to deposit money into your account. Like all information sent over the internet, those communications could be corrupted by noise that inserts errors into the data.
To overcome this problem, senders encode data before they are transmitted, and then a receiver uses a decoding algorithm to correct errors and recover the original message. In some instances, data are received with reliability information that helps the decoder figure out which parts of a transmission are likely errors.
Researchers at MIT and elsewhere have developed a decoder chip that employs a new statistical model to use this reliability information in a way that is much simpler and faster than conventional techniques.
Their chip uses a universal decoding algorithm the team previously developed, which can unravel any error correcting code. Typically, decoding hardware can only process one particular type of code. This new, universal decoder chip has broken the record for energy-efficient decoding, performing between 10 and 100 times better than other hardware.
This advance could enable mobile devices with fewer chips, since they would no longer need separate hardware for multiple codes. This would reduce the amount of material needed for fabrication, cutting costs and improving sustainability. By making the decoding process less energy intensive, the chip could also improve device performance and lengthen battery life. It could be especially useful for demanding applications like augmented and virtual reality and 5G networks.
“This is the first time anyone has broken below the 1 picojoule-per-bit barrier for decoding. That is roughly the same amount of energy you need to transmit a bit inside the system. It had been a big symbolic threshold, but it also changes the balance in the receiver of what might be the most pressing part from an energy perspective — we can move that away from the decoder to other elements,” says Muriel Médard, the School of Science NEC Professor of Software Science and Engineering, a professor in the Department of Electrical Engineering and Computer Science, and a co-author of a paper presenting the new chip.

Médard’s co-authors include lead author Arslan Riaz, a graduate student at Boston University (BU); Rabia Tugce Yazicigil, assistant professor of electrical and computer engineering at BU; and Ken R. Duffy, then director of the Hamilton Institute at Maynooth University and now a professor at Northeastern University, as well as others from MIT, BU, and Maynooth University. The work is being presented at the International Solid-States Circuits Conference.
Smarter sorting
Digital data are transmitted over a network in the form of bits (0s and 1s). A sender encodes data by adding an error-correcting code, which is a redundant string of 0s and 1s that can be viewed as a hash. Information about this hash is held in a specific code book. A decoding algorithm at the receiver, designed for this particular code, uses its code book and the hash structure to retrieve the original information, which may have been jumbled by noise. Since each algorithm is code-specific, and most require dedicated hardware, a device would need many chips to decode different codes.
The researchers previously demonstrated GRAND (Guessing Random Additive Noise Decoding), a universal decoding algorithm that can crack any code. GRAND works by guessing the noise that affected the transmission, subtracting that noise pattern from the received data, and then checking what remains in a code book. It guesses a series of noise patterns in the order they are likely to occur.
Data are often received with reliability information, also called soft information, that helps a decoder figure out which pieces are errors. The new decoding chip, called ORBGRAND (Ordered Reliability Bits GRAND), uses this reliability information to sort data based on how likely each bit is to be an error.

But it isn’t as simple as ordering single bits. While the most unreliable bit might be the likeliest error, perhaps the third and fourth most unreliable bits together are as likely to be an error as the seventh-most unreliable bit. ORBGRAND uses a new statistical model that can sort bits in this fashion, considering that multiple bits together are as likely to be an error as some single bits.
“If your car isn’t working, soft information might tell you that it is probably the battery. But if it isn’t the battery alone, maybe it is the battery and the alternator together that are causing the problem. This is how a rational person would troubleshoot — you’d say that it could actually be these two things together before going down the list to something that is much less likely,” Médard says.
This is a much more efficient approach than traditional decoders, which would instead look at the code structure and have a performance that is generally designed for the worst-case.
“With a traditional decoder, you’d pull out the blueprint of the car and examine each and every piece. You’ll find the problem, but it will take you a long time and you’ll get very frustrated,” Médard explains.
ORBGRAND stops sorting as soon as a code word is found, which is often very soon. The chip also employs parallelization, generating and testing multiple noise patterns simultaneously so it finds the code word faster. Because the decoder stops working once it finds the code word, its energy consumption stays low even though it runs multiple processes simultaneously.
Record-breaking efficiency
When they compared their approach to other chips, ORBGRAND decoded with maximum accuracy while consuming only 0.76 picojoules of energy per bit, breaking the previous performance record. ORBGRAND consumes between 10 and 100 times less energy than other devices.
One of the biggest challenges of developing the new chip came from this reduced energy consumption, Médard says. With ORBGRAND, generating noise sequences is now so energy-efficient that other processes the researchers hadn’t focused on before, like checking the code word in a code book, consume most of the effort.
“Now, this checking process, which is like turning on the car to see if it works, is the hardest part. So, we need to find more efficient ways to do that,” she says.
The team is also exploring ways to change the modulation of transmissions so they can take advantage of the improved efficiency of the ORBGRAND chip. They also plan to see how their technique could be utilized to more efficiently manage multiple transmissions that overlap.
The research is funded, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA) and Science Foundation Ireland. More

High-resolution microscopy techniques, for example electron microscopy or super-resolution microscopy, produce huge amounts of data. The visualization, analysis and dissemination of such large imaging data sets poses significant challenges. Now, these tasks can be carried out using MoBIE, which stands for Multimodal Big Image Data Exploration, a new user-friendly, freely available tool developed by researchers from the University of Göttingen and EMBL Heidelberg. This means that researchers such as biologists, who rely on high-resolution microscopy techniques, can incorporate multiple data sets to study the processes of life at the very smallest scales. Their method has now been published in Nature Methods.

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In the very near future, quantum computers are expected to revolutionize the way we compute, with new approaches to database searches, AI systems, simulations and more. But to achieve such novel quantum technology applications, photonic integrated circuits which can effectively control photonic quantum states — the so-called qubits — are needed. Physicists from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), TU Dresden and Leibniz-Institut für Kristallzüchtung (IKZ) have made a breakthrough in this effort: for the first time, they demonstrated the controlled creation of single-photon emitters in silicon at the nanoscale, as they report in Nature Communications.
Photonic integrated circuits, or in short, PICs, utilize particles of light, better known as photons, as opposed to electrons that run in electronic integrated circuits. The main difference between the two: A photonic integrated circuit provides functions for information signals imposed on optical wavelengths typically in the near infrared spectrum. “Actually, these PICs with many integrated photonic components are able to generate, route, process and detect light on a single chip,” says Dr. Georgy Astakhov, Head of Quantum Technologies at HZDR’s Institute of Ion Beam Physics and Materials Research, and adds: “This modality is poised to play a key role in upcoming future technology, such as quantum computing. And PICs will lead the way.”
Before, quantum photonics experiments were notorious for the massive use of “bulk optics” distributed across the optical table and occupying the entire lab. Now, photonic chips are radically changing this landscape. Miniaturization, stability and suitability for mass production might turn them into the workhorse of modern-day quantum photonics.
From random to control mode
Monolithic integration of single-photon sources in a controllable way would give a resource-efficient route to implement millions of photonic qubits in PICs. To run quantum computation protocols, these photons must be indistinguishable. With this, industrial-scale photonic quantum processor production would become feasible.
However, the currently established fabrication method stands in the way of the compatibility of this promising concept with today’s semiconductor technology.
In a first attempt reported about two years ago, the researchers were already able to generate single photons on a silicon wafer, but only in a random and non-scalable way. Since then, they have come far. “Now, we show how focused ion beams from liquid metal alloy ion sources are used to place single-photon emitters at desired positions on the wafer while obtaining a high creation yield and high spectral quality,” says Dr. Nico Klingner, physicist.
Furthermore, the scientists at HZDR subjected the same single-photon emitters to a rigorous material testing program: After several cooling-down and warming-up cycles, they did not observe any degradation of their optical properties. These findings meet the preconditions required for mass production later on.
To translate this achievement into a widespread technology, and allow for wafer-scale engineering of individual photon emitters on the atomic scale compatible with established foundry manufacturing, the team implemented broad-beam implantation in a commercial implanter through a lithographically defined mask. “This work really allowed us to take advantage of the state-of-the-art silicon processing cleanroom and electron beam lithography machines at the Nano Fabrication facility Rossendorf,” explains Dr. Ciarán Fowley, Cleanroom group leader and Head of Nanofabrication and Analysis.
Using both methods, the team can create dozens of telecom single-photon emitters at predefined locations with a spatial accuracy of about 50 nm. They emit in the strategically important telecommunication O-band and exhibit stable operation over days under continuous-wave excitation.
The scientists are convinced that the realization of controllable fabrication of single-photon emitters in silicon makes them a highly promising candidate for photonic quantum technologies, with a fabrication pathway compatible with very large-scale integration. These single-photon emitters are now technologically ready for production in semiconductor fabs and incorporation into the existing telecommunication infrastructure. More

It’s not easy to make sense of quantum-scale motion, but a new mathematical theory developed by scientists at Rice University and Oxford University could help — and may provide insight into improving a variety of computing, electrochemical and biological systems.
The theory developed by Rice theorist Peter Wolynes and Oxford theoretical chemist David Logan gives a simple prediction for the threshold at which large quantum systems switch from orderly motion like a clock to random, erratic motion like asteroids moving around in the early solar system. Using a computational analysis of a photosynthesis model, collaborators at the University of Illinois Urbana-Champaign showed that the theory can predict the nature of the motions in a chlorophyll molecule when it absorbs energy from sunlight.
The theory applies to any sufficiently complex quantum system and may give insights into building better quantum computers. It could also, for instance, help design features of next-generation solar cells or perhaps make batteries last longer.
The study is published this week in the Proceedings of the National Academy of Sciences.
Nothing is ever completely still on the molecular level, especially when quantum physics plays a role. A water droplet gleaming on a leaf may look motionless, but inside, over a sextillion molecules are vibrating nonstop. Hydrogen and oxygen atoms and the subatomic particles within them — the nuclei and electrons — constantly move and interact.
“In thinking about the motions of individual molecules at quantum scale, there is often this comparison to the way we think of the solar system,” Wolynes said. “You learn that there are eight planets in our solar system, each one with a well-defined orbit. But in fact, the orbits interact with each other. Nevertheless, the orbits are very predictable. You can go to a planetarium, and they’ll show you what the sky looked like 2,000 years ago. A lot of the motions of the atoms in molecules are exactly that regular or clocklike.”
When Wolynes and Logan first posed the question of predicting the regularity or randomness of quantum motion, they tested their math against observations of vibrational motions in individual molecules.

“You only have to know two things about a molecule to be able to analyze its quantum motion patterns,” Wolynes said. “First, you need to know the vibrational frequencies of its particles, that’s to say the frequencies at which the vibrations occur which are like the orbits, and, second, how these vibrations nonlinearly interact with each other. These anharmonic interactions depend mostly on the mass of atoms. For organic molecules, you can predict how strongly those vibrational orbits would interact with one other.”
Things are more complicated when the molecules also dramatically change structure, for instance as a result of a chemical reaction.
“As soon as we start looking at molecules that chemically react or rearrange their structure, we know that there’s at least some element of unpredictability or randomness in the process because, even in classical terms, the reaction either happens, or it doesn’t happen,” Wolynes said. “When we try to understand how chemical changes occur, there’s this question: Is the overall motion more clocklike or is it more irregular?”
Aside from their nonstop vibrations, which happen without light, electrons can have quantum-level interactions that sometimes lead to a more dramatic turn.
“Because they’re very light, electrons normally move thousands of times faster than the centers of the atoms, the nuclei,” he said. “So though they are constantly moving, the electrons’ orbits smoothly adjust to what the nuclei do. But every now and again, the nuclei come to a place where the electronic energies will almost be equal whether the excitation is on one molecule or on the other. That’s what’s called a surface crossing. At that point, the excitation has a chance to jump from one electronic level to another.”
Predicting at which point the transfer of energy that takes place during photosynthesis turns from orderly motion to randomness or dissipation would take a significant amount of time and effort by direct computation.

“It is very nice that we have a very simple formula that determines when this happens,” said Martin Gruebele, a chemist at the University of Illinois Urbana-Champaign and co-author on the study who is a part of the joint Rice-Illinois Center for Adapting Flaws into Features (CAFF) funded by the National Science Foundation. “That’s something we just didn’t have before and figuring it out required very lengthy calculations.”
The Logan-Wolynes theory opens up a wide array of scientific inquiry ranging from the theoretical exploration of the fundamentals of quantum mechanics to practical applications.
“The Logan-Wolynes theory did pretty well in terms of telling you at roughly what energy input you’d get a change in quantum-system behavior,” Wolynes said. “But one of the interesting things that the large-scale computations of (co-author Chenghao) Zhang and Gruebele found is that there are these exceptions that stand out from all the possible orbiting patterns you might have. Occasionally there’s a few stragglers where simple motions persist for long times and don’t seem to get randomized. One of the questions we’re going to pursue in the future is how much that persistent regularity is actually influencing processes like photosynthesis.
“Another direction that is being pursued at Rice where this theory can help is the problem of making a quantum computer that behaves as much as possible in a clocklike way,” he said. “You don’t want your computers to be randomly changing information. The larger and more sophisticated you make a computer, the likelier it is that you’ll run into some kind of randomization effects.”
Gruebele and collaborators at Illinois also plan to use these ideas in other scientific contexts. “One of our goals, for instance, is to design better human-built light-harvesting molecules that might consist of carbon dots that can transfer the energy to their periphery where it can be harvested,” Gruebele said.
Wolynes is Rice’s Bullard-Welch Foundation Professor of Science and a professor of chemistry, of biochemistry and cell biology, of physics and astronomy and of materials science and nanoengineering and co-director of its Center for Theoretical Biological Physics (CTBP), which is funded by the National Science Foundation. Logan is the Coulson Professor of Theoretical Chemistry at Oxford. Gruebele is the James R. Eiszner Endowed Chair in Chemistry and Zhang is a graduate student in physics at the University of Illinois Urbana-Champaign.
The James R. Eiszner Chair in Chemistry and the Physics Department at Illinois, the Bullard-Welch Chair at Rice (C-0016) and the National Science Foundation (PHY-2019745) supported the research. More