Aurelia Butler

For the first time, scientists at the Cavendish Laboratory have found that a single ‘atomic defect’ in a thin material, Hexagonal Boron Nitride (hBN), exhibits spin coherence under ambient conditions, and that these spins can be controlled with light. Spin coherence refers to an electronic spin being capable of retaining quantum information over time. The discovery is significant because materials that can host quantum properties under ambient conditions is quite rare.
The findings published in Nature Materials, further confirm that the accessible spin coherence at room temperature is longer than the researchers initially imagined it could be. “The results show that once we write a certain quantum state onto the spin of these electrons, this information is stored for ~1 millionth of a second, making this system a very promising platform for quantum applications,” said Carmem M. Gilardoni, co-author of the paper and Rubicon postdoctoral fellow at the Cavendish Laboratory.
“This may seem short, but the interesting thing is that this system does not require special conditions — it can store the spin quantum state even at room temperature and with no requirement for large magnets.”
Hexagonal Boron Nitride (hBN) is an ultra-thin material made up of stacked one-atom-thick layers, kind of like sheets of paper. These layers are held together by forces between molecules. But sometimes, there are ‘atomic defects’ withinthese layers, similar to a crystal with molecules trapped inside it. These defects can absorb and emit light in the visible range with well-defined optical transitions, and they can act as local traps for electrons. Because of these ‘atomic defects’ within hBN, scientists can now study how these trapped electrons behave. They can study the spin property, which allows electrons to interact with magnetic fields. What’s truly exciting is that researchers can control and manipulate the electron spins using light within these defects at room temperature.
This finding paves the way for future technological applications particularly in sensing technology.
However, since this is the first time anyone has reported the spin coherence of the system, there is a lot to investigate before it is mature enough for technological applications. The scientists are still figuring out how to make these defects even better and more reliable. They are currently probing how far we can extend the spin storage time, and whether we can optimise the system and material parameters that are important for quantum-technological applications, such as defect stability over time and the quality of the light emitted by this defect.
“Working with this system has highlighted to us the power of the fundamental investigation of materials. As for the hBN system, as a field we can harness excited state dynamics in other new material platforms for use in future quantum technologies,” said Dr. Hannah Stern, first author of the paper, who conducted this research at the Cavendish Laboratory and is now a Royal Society University Research Fellow and Lecturer at University of Manchester.
In future the researchers are looking at developing the system further, exploring many different directions from quantum sensors to secure communications.
“Each new promising system will broaden the toolkit of available materials, and every new step in this direction will advance the scalable implementation of quantum technologies. These results substantiate the promise of layered materials towards these goals,” concluded Professor Mete Atatüre, Head of the Cavendish Laboratory, who led the project. More

Using more robots to close labor gaps in the hospitality industry may backfire and cause more human workers to quit, according to a Washington State University study.
The study, involving more than 620 lodging and food service employees, found that “robot-phobia” — specifically the fear that robots and technology will take human jobs — increased workers’ job insecurity and stress, leading to greater intentions to leave their jobs. The impact was more pronounced with employees who had real experience working with robotic technology. It also affected managers in addition to frontline workers. The findings were published in theInternational Journal of Contemporary Hospitality Management.
“The turnover rate in the hospitality industry ranks among the highest across all non-farm sectors, so this is an issue that companies need to take seriously,” said lead author Bamboo Chen, a hospitality researcher in WSU’s Carson College of Business. “The findings seem to be consistent across sectors and across both frontline employees and managers. For everyone, regardless of their position or sector, robot-phobia has a real impact.”
Food service and lodging industries were hit particularly hard by the pandemic lockdowns, and many businesses are still struggling to find enough workers. For example, the accommodation workforce in April 2024 was still 9.2% below what it was in February 2020, according to U.S. Bureau of Labor Statistics. The ongoing labor shortage has inspired some employers to turn to robotic technology to fill the gap.
While other studies have focused on customers’ comfort with robots, this study focuses on how the technology impacted hospitality workers. Chen and WSU colleague Ruying Cai surveyed 321 lodging and 308 food service employees from across the U.S., asking a range of questions about their jobs and attitudes toward robots. The survey defined “robots” broadly to include a range of robotic and automation technologies, such as human-like robot servers and automated robotic arms as well as self-service kiosks and tabletop devices.
Analyzing the survey data, the researchers found that having a higher degree of robot-phobia was connected to greater feelings of job insecurity and stress — which were then correlated with “turnover intention” or workers’ plans to leave their jobs. Those fears did not decrease with familiarity: employees who had more actual engagement with robotic technology in their daily jobs had higher fears that it would make human workers obsolete.
Perception also played a role. The employees who viewed robots as being more capable and efficient also ranked higher in turnover intention.
Robots and automation can be good ways to help augment service, Chen said, as they can handle tedious tasks humans typically do not like doing such as washing dishes or handling loads of hotel laundry. But the danger comes if the robotic additions cause more human workers to quit. The authors point out this can create a “negative feedback loop” that can make the hospitality labor shortage worse.
Chen recommended that employers communicate not only the benefits but the limitations of the technology — and place a particular emphasis on the role human workers play.
“When you’re introducing a new technology, make sure not to focus just on how good or efficient it will be. Instead, focus on how people and the technology can work together,” he said. More

A new advanced artificial intelligence (AI) algorithm may lead to better — and earlier — predictions and novel therapies for autoimmune diseases, which involve the immune system mistakenly attacking their body’s own healthy cells and tissues. The algorithm digs into the genetic code underlying the conditions to more accurately model how genes associated with specific autoimmune diseases are expressed and regulated and to identify additional genes of risk.
The work, developed by a team led by Penn State College of Medicine researchers, outperforms existing methodologies and identified 26% more novel gene and trait associations, the researchers said. They published their work today (May 20) in Nature Communications.
“We all carry some DNA mutations, and we need to figure out how any one of these mutations may influence gene expression linked to disease so we can predict disease risk early. This is especially important for autoimmune disease,” said Dajiang Liu, distinguished professor, vice chair for research, and director of artificial intelligence and biomedical informatics at the Penn State College of Medicine and co-senior author of the study. “If an AI algorithm can more accurately predict disease risk, it means we can carry out interventions earlier.”
Genetics often underpin disease development. Variations in DNA can influence gene expression, or the process by which the information in DNA is converted into functional products like a protein. How much or how little a gene is expressed can influence disease risk.
Genome-wide association studies (GWAS), a popular approach in human genetics research, can home in on regions of the genome associated with a particular disease or trait but can’t pinpoint the specific genes that affect disease risks. It’s like sharing your location with a friend with the precise location setting turned off on your smartphone — the city might be obvious, but the address is obscured. Existing methods are also limited in the granularity of its analysis. Gene expression can be specific to certain types of cells. If the analysis doesn’t distinguish between distinct cell types, the results may overlook real causal relationships between genetic variants and gene expression.
The research team’s method, dubbed EXPRESSO for EXpression PREdiction with Summary Statistics Only, applies a more advanced artificial intelligence algorithm and analyzes data from single-cell expression quantitative trait loci, a type of data that links genetic variants to the genes they regulate. It also integrates 3D genomic data and epigenetics — which measures how genes may be modified by environment to influence disease — into its modeling. The team applied EXPRESSO to GWAS datasets for 14 autoimmune diseases, including lupus, Crohn’s disease, ulcerative colitis and rheumatoid arthritis.
“With this new method, we were able to identify many more risk genes for autoimmune disease that actually have cell-type specific effects, meaning that they only have effects in a particular cell type and not others,” said Bibo Jiang, assistant professor at the Penn State College of Medicine and senior author of the study.

The team then used this information to identify potential therapeutics for autoimmune disease. Currently, there aren’t good long-term treatment options, they said.
“Most treatments are designed to mitigate symptoms, not cure the disease. It’s a dilemma knowing that autoimmune disease needs long-term treatment, but the existing treatments often have such bad side effects that they can’t be used for long. Yet, genomics and AI offer a promising route to develop novel therapeutics,” said Laura Carrel, professor of biochemistry and molecular biology at the Penn State College of Medicine and co-senior author of the study.
The team’s work pointed to drug compounds that could reverse gene expression in cell types associated with an autoimmune disease, such as vitamin K for ulcerative colitis and metformin, which is typically prescribed for type 2 diabetes, for type 1 diabetes. These drugs, already approved by the Food and Drug Administration as safe and effective for treating other diseases, could potentially be repurposed.
The research team is working with collaborators to validate their findings in a laboratory setting and, ultimately, in clinical trials.
Lida Wang, a doctoral student in the biostatistics program, and Chachrit Khunsriraksakul, who earned a doctorate in bioinformatics and geonomics in 2022 and his medical degree in May from Penn State, co-led the study. Other Penn State College of Medicine authors on the paper include: Havell Markus, who is pursuing a doctorate and a medical degree; Dieyi Chen, doctoral candidate; Fan Zhang, graduate student; and Fang Chen, postdoctoral scholar. Xiaowei Zhan, associate professor at UT Southwestern Medical Center, also contributed to the paper.
Funding from the National Institutes of Health (grant numbers R01HG011035, R01AI174108 and R01ES036042) and the Artificial Intelligence and Biomedical Informatics pilot grant from the Penn State College of Medicine supported this work. More

In a May 15 paper released in the journal Physical Review Letters, Virginia Tech physicists revealed a microscopic phenomenon that could greatly improve the performance of soft devices, such as agile flexible robots or microscopic capsules for drug delivery.
The paper, written by doctoral candidate Chinmay Katke, assistant professor C. Nadir Kaplan, and co-author Peter A. Korevaar from Radboud University in the Netherlands, proposes a new physical mechanism that could speed up the expansion and contraction of hydrogels. For one thing, this opens up the possibility for hydrogels to replace rubber-based materials used to make flexible robots — enabling these fabricated materials to perhaps move with a speed and dexterity close to that of human hands.
Soft robots are already being used in manufacturing, where a hand-like device is programmed to grab an item from a conveyer belt — picture a hot dog or piece of soap — and place it in a container to be packaged. But the ones in use now lean on hydraulics or pneumatics to change the shape of the “hand” to pick up the item.
Akin to our own body, hydrogels mostly contain water and are everywhere around us, e.g., food jelly and shaving gel. Katke, Korevaar, and Kaplan’s research appears to have found a method that allows hydrogels to swell and contract much more quickly, which would improve their flexibility and capability to function in different settings.
Living organisms use osmosis for such activities as bursting seed dispersing fruits in plants or absorbing water in the intestine. Normally, we think of osmosis as a flow of water moving through a membrane, with bigger molecules like polymers unable to move through. Such membranes are called semi-permeable membranes and were thought to be necessary to trigger osmosis.
Previously, Korevaar and Kaplan had done experiments by using a thin layer of hydrogel film comprised of polyacrylic acid. They had observed that even though the hydrogel film allows both water and ions to pass through and is not selective, the hydrogel rapidly swells due to osmosis when ions are released inside the hydrogel and shrinks back again.
Katke, Korevaar, and Kaplan developed a new theory to explain the above observation. This theory tells that microscopic interactions between ions and polyacrylic acid can make hydrogel swell when the released ions inside the hydrogel are unevenly spread out. They called this “diffusio-phoretic swelling of the hydrogels.” Furthermore, this newly discovered mechanism allows hydrogels to swell much faster than what has been previously possible.

Why is that change important?
Kaplan explained: Soft agile robots are currently made with rubber, which “does the job but their shapes are changed hydraulically or pneumatically. This is not desired because it is difficult to imprint a network of tubes into these robots to deliver air or fluid into them.”
Imagine, Kaplan said, how many different things you can do with your hand and how fast you can do them owing to your neural network and the motion of ions under your skin. Because the rubber and hydraulics are not as versatile as your biological tissues, which is a hydrogel, state-of-the-art soft robots can only do a limited number of movements.”
Katke explained that the process they have researched allows the hydrogels to change shape then change back to their original form “significantly faster this way” in soft robots that are larger than ever before.
At present, only microscopic-sized hydrogel robots can respond to a chemical signal quickly enough to be useful and larger ones require hours to change shape, Katke said. By using the new diffusio-phoresis method, soft robots as large as a centimeter may be able to transform in just a few seconds, which is subject to further studies.
Larger agile soft robots that could respond quickly could improve assistive devices in healthcare, “pick-and-place” functions in manufacturing, search and rescue operations, cosmetics used for skincare, and contact lenses. More

Researchers at the University of Bristol have made an important breakthrough in scaling quantum technology by integrating the world’s tiniest quantum light detector onto a silicon chip.
A critical moment in unlocking the information age was when scientists and engineers were first able to miniaturise transistors onto cheap micro-chips in the 1960s.
Now, for the first time, University of Bristol academics have demonstrated the integration of a quantum light detector — smaller than a human hair — onto a silicon chip, moving us one step closer to the age of quantum technologies using light.
Making high performance electronics and photonics at scale is fundamental to realizing the next generation of advanced information technologies. Figuring out how to make quantum technologies in existing commercial facilities is an ongoing international effort being tackled by university research and companies around the world.
It could prove crucial for quantum computing to be able to make high performance quantum hardware at scale due to the vast amount of components anticipated to build even a single machine.
In pursuit of this goal, researchers at the University of Bristol have demonstrated a type of quantum light detector that is implemented on a chip with a circuit that occupies 80 micrometers by 220 micrometers.
Critically, the small size means the quantum light detector can be fast, which is key to unlocking high speed quantum communications and enabling high speed operation of optical quantum computers.

The use of established and commercially accessible fabrication techniques helps the prospects for early incorporation into other technologies such as sensing and communications.
“These types of detectors are called homodyne detectors, and they pop up everywhere in applications across quantum optics” explains Professor Jonathan Matthews, who led the research and is Director of the Quantum Engineering Technology Labs. “They operate at room temperature, and you can use them for quantum communications, in incredibly sensitive sensors — like state-of-the-art gravitational wave detectors — and there are designs of quantum computers that would use these detectors.”
In 2021 the Bristol team showed how linking a photonics chip with a separate electronics chip can increase speed of quantum light detectors — now with a single electronic-photonic integrated chip, the team have further increased speed by a factor of 10 whilst reducing footprint by a factor of 50.
While these detectors are fast and small, they are also sensitive.
“The key to measuring quantum light is sensitivity to quantum noise” explains author Dr Giacomo Ferranti. “Quantum mechanics is responsible for a minute, fundamental level of noise in all optical systems. The behaviour of this noise reveals information about what kind of quantum light is travelling in the system, it can determine how sensitive an optical sensor can be, and it can be used to mathematically reconstruct quantum states. In our study it was important to show that making the detector smaller and faster did not block its sensitivity for measuring quantum states.”
The authors note that there is more exciting research to do in integrating other disruptive quantum technology hardware down to the chip scale. With the new detector, the efficiency needs to improve, and there is work to be done to trial the detector in lots of different applications.
Professor Matthews added: “We built the detector with a commercially accessible foundry in order to make its applications more accessible. While we are incredibly excited by the implications across a range of quantum technology, it is critical that we as a community continue to tackle the challenge of scalable fabrication of quantum technology. Without demonstrating truly scalable fabrication of quantum hardware, the impact and benefits of quantum technology will be delayed and limited.” More

Using DNA origami, LMU researchers have built a diamond lattice with a periodicity of hundreds of nanometers — a new approach for manufacturing semiconductors for visible light.
The shimmering of butterfly wings in bright colors does not emerge from pigments. Rather, it is photonic crystals that are responsible for the play of colors. Their periodic nanostructure allows light at certain wavelengths to pass through while reflecting other wavelengths. This causes the wing scales, which are in fact transparent, to appear so magnificently colored. For research teams, the manufacture of artificial photonic crystals for visible light wavelengths has been a major challenge and motivation ever since they were predicted by theorists more than 35 years ago. “Photonic crystals have a versatile range of applications. They have been employed to develop more efficient solar cells, innovative optical waveguides, and materials for quantum communication. However, they have been very laborious to manufacture,” explains Dr. Gregor Posnjak. The physicist is a postdoc in the research group of LMU Professor Tim Liedl, whose work is funded by the “e-conversion” Cluster of Excellence and the European Research Council. Using DNA nanotechnology, the team has developed a new approach for the manufacture of photonic crystals. Their results have now been published in the journal Science.
Diamond structure out of strands of DNA
In contrast to lithographic techniques, the LMU team uses a method called DNA origami to design and synthesize building blocks, which then self-assemble into a specific lattice structure. “It’s long been known that the diamond lattice theoretically has an optimal geometry for photonic crystals. In diamonds, each carbon atom is bonded to four other carbon atoms. Our challenge consisted in enlarging the structure of a diamond crystal by a factor of 500, so that the spaces between the building blocks correspond with the wavelength of light,” explains Tim Liedl. “We increased the periodicity of the lattice to 170 nanometers by replacing the individual atoms with larger building blocks — in our case, through DNA origami,” says Posnjak.
The perfect molecule folding technique
What sounds like magic is actually a specialty of the Liedl group, one of the world’s leading research teams in DNA origami and self-assembly. For this purpose, the scientists use a long, ring-shaped DNA strand (consisting of around 8,000 bases) and a set of 200 short DNA staples. “The latter control the folding of the longer DNA strand into virtually any shape at all — akin to origami masters, who fold pieces of paper into intricate objects. As such, the clamps are a means of determining how the DNA origami objects combine to form the desired diamond lattice,” says the LMU postdoctoral researcher. The DNA origami building blocks form crystals of approximately ten micrometers in size, which are deposited on a substrate and then passed on to a cooperating research group from the Walter Schottky Institute at the Technical University of Munich (TUM): The team led by Professor Ian Sharp (also funded by the “e-conversion” Cluster of Excellence) is able to deposit individual atomic layers of titanium dioxide on all surfaces of the DNA origami crystals. “The DNA origami diamond lattice serves as scaffolding for titanium dioxide, which, on account of its high index of refraction, determines the photonic properties of the lattice. After coating, our photonic crystal does not allow UV light with a wavelength of about 300 nanometers to pass through, but rather reflects it,” explains Posnjak. The wavelength of the reflected light can be controlled via the thickness of the titanium dioxide layer.
DNA origami could boost photonics
For photonic crystals that work in the infrared range, classic lithographic techniques are suitable but laborious and expensive. In the wavelength range of visible and UV light, lithographic methods have not been successful to date. “Consequently, the comparatively easy manufacturing process using the self-assembly of DNA origami in an aqueous solution offers a powerful alternative for producing structures in the desired size cost-effectively and in larger quantities,” says Prof. Tim Liedl. He is convinced that the unique structure with its large pores, which are chemically addressable, will stimulate further research — for example, in the domain of energy harvesting and storage. In the same issue of Science, a collaboration led by prof. Petr Šulc of Arizona State University and TUM presents a theoretical framework for designing diverse crystalline lattices from patchy colloids, and experimentally demonstrates the method by utilizing DNA origami building blocks to form a pyrochlore lattice, which potentially also could be used for photonic applications. More

A new AI tool to more quickly and accurately classify brain tumours has been developed by researchers at The Australian National University (ANU).
According to Dr Danh-Tai Hoang, precision in diagnosing and categorising tumours is crucial for effective patient treatment.
“The current gold standard for identifying different kinds of brain tumours is DNA methylation-based profiling,” Dr Hoang said.
“DNA methylation acts like a switch to control gene activity, and which genes are turned on or off.
“But the time it takes to do this kind of testing can be a major drawback, often requiring several weeks or more when patients might be relying on quick decisions on therapies.
“There’s also a lack of availability of these tests in nearly all hospitals worldwide.”
To address these challenges, the ANU researchers, in collaboration with experts from the National Cancer Institute in the United States (US), developed DEPLOY, a way to predict DNA methylation and subsequently classify brain tumours into 10 major subtypes.

DEPLOY draws on microscopic pictures of a patient’s tissue called histopathology images.
The model was trained and validated on large datasets of approximately 4,000 patients from across the US and Europe.
“Remarkably, DEPLOY achieved an unprecedented accuracy of 95 per cent,” Dr Hoang said.
“Furthermore, when given a subset of 309 particularly difficult to classify samples, DEPLOY was able to provide a diagnosis that was more clinically relevant than what was initially provided by pathologists.
“This shows the potential future role of DEPLOY as a complementary tool, adding to a pathologist’s initial diagnosis, or even prompting re-evaluation in the case of disparities.”
The researchers believe DEPLOY could eventually be used to help classify other types of cancer as well. More