Aurelia Butler

A team at Pohang University of Science and Technology (POSTECH), spearheaded by Professor Su Seok Choi and Ph.D. candidate Seungmin Nam from the Department of Electrical Engineering, has developed a novel stretchable photonic device that can control light wavelengths in all directions. This pioneering study was published in Light: Science & Applications on May 22.
Structural colors are produced through the interaction of light with microscopic nanostructures, creating vibrant hues without relying on traditional color mixing methods. Conventional displays and image sensors blend the three primary colors (red, green, and blue), while structural color technology leverages the inherent wavelengths of light, resulting in more vivid and diverse color displays. This innovative approach is gaining recognition as a promising technology in the nano-optics and photonics industries.
Traditional color mixing techniques, which use dyes or luminescent materials, are limited to passive and fixed color representation. In contrast, tunable color technology dynamically controls nanostructures corresponding to specific light wavelengths, allowing for the free adjustment of pure colors. Previous research has primarily been limited to unidirectional color tuning, typically shifting colors from red to blue. Reversing this shift — from blue to red, which has a longer wavelength — has been a significant challenge. Current technology only allows adjustments towards shorter wavelengths, making it difficult to achieve diverse color representation in the ideal free wavelength direction. Therefore, a new optical device capable of bidirectional and omnidirectional wavelength adjustment is needed to maximize the utilization of wavelength control technology.
Professor Choi’s team addressed these challenges by integrating chiral liquid crystal elastomers (CLCEs) with dielectric elastomer actuators (DEAs). CLCEs are flexible materials capable of structural color changes, while DEAs induce flexible deformation of dielectrics in response to electrical stimuli. The team optimized the actuator structure to allow both expansion and contraction, combining it with CLCEs, and developed a highly adaptable stretchable device. This device can freely adjust the wavelength position across the visible spectrum, from shorter to longer wavelengths and vice versa.
In their experiments, the researchers demonstrated that their CLCE-based photonic device could control structural colors over a broad range of visible wavelengths (from blue at 450nm to red at 650nm) using electrical stimuli. This represents a significant advancement over previous technologies, which were limited to unidirectional wavelength tuning.
This research not only establishes a foundational technology for advanced photonic devices but also highlights its potential for various industrial applications.
Professor Choi remarked, “This technology can be applied in displays, optical sensors, optical camouflage, direct optical analogue encryption, biomimetic sensors, and smart wearable devices, among many other applications involving light, color, and further broadband electromagnetic waves beyond visible band. We aim to expand its application scope through ongoing research.” More

Scientists at the Terasaki Institute for Biomedical Innovation (TIBI), have employed artificial intelligence techniques to improve the design and production of nanofibers used in wearable nanofiber acoustic energy harvesters (NAEH). These acoustic devices capture sound energy from the environment and convert it into electrical energy, which can then be applied in useful devices, such as hearing aids.
Many efforts have been made to capture naturally occurring and abundant energy sources from our surrounding environment. Relatively recent advances such as solar panels and wind turbines allow us to efficiently harvest energy from the sun and wind, convert it into electrical energy, and store it for various applications. Similarly, conversions of acoustic energy can be seen in amplifying devices such as microphones, as well as in wearable, flexible electronic devices for personalized healthcare.
Currently, there has been much interest in using piezoelectric nanogenerators — devices that convert mechanical vibrations, stress, or strain into electrical power — as acoustic energy harvesters. These nanogenerators can convert mechanical energy from sound waves to generate electricity; however, this conversion of sound waves is inefficient, as it occurs mainly in the high frequency sound range, and most environmental sound waves are in the low frequency range. Additionally, choosing optimal materials, structural design, and fabrication parameters make the production of piezoelectric nanogenerators challenging.
As described in their paper in Nano Research, the TIBI scientists’ approach to these challenges was two-fold: first, they chose their materials strategically and elected to fabricate nanofibers using polyvinylfluoride (PVDF), which are known for their ability to capture acoustic energy efficiently. When making the nanofiber mixture, polyurethane (PU) was added to the PVDF solution to impart flexibility, and electrospinning (a technique for generating ultrathin fibers) was used to produce the composite PVDF/PU nanofibers.
Secondly, the team applied artificial intelligence (AI) techniques to determine the best fabrication parameters involved in electrospinning the PVDF/polyurethane nanofibers; these parameters included the applied voltage, electrospinning time, and drum rotation speed. Employing these techniques allowed the team to tune the parameter values to obtain maximum power generation from their PVDF/PU nanofibers.
To make their nanoacoustic energy harvester, the TIBI scientists fashioned their PVDF/PU nanofibers into a nanofibrous mat and sandwiched it between aluminum mesh layers that functioned as electrodes. The entire assembly was then encased by two flexible frames.
In tests against conventionally fabricated NAEHs, the resultant AI-generated PVDF/PU NAEHs were found to have better overall performance, yielding a power density level more than 2.5 times higher and a significantly higher energy conversion efficiency (66% vs 42%). Furthermore, the AI-generated PVDF/PU NAEHs were able to obtain these results when tested with a wide range of low-frequency sound — well within the levels found in ambient background noise. This allows for excellent sound recognition and the ability to distinguish words with high resolution.
“Models using artificial intelligence optimization, such as the one described here, minimize time spent on trial and error and maximize the effectiveness of the finished product,” said Ali Khademhosseini, Ph.D., TIBI’s director and CEO. “This can have far-reaching effects on the fabrication of medical devices with significant practicability.” More

For survivors of strokes, which afflict nearly 800,000 Americans each year, regaining fine motor skills like writing and using utensils is critical for recovering independence and quality of life. But getting intensive, frequent rehabilitation therapy can be challenging and expensive.
Now, researchers at NYU Tandon School of Engineering are developing a new technology that could allow stroke patients to undergo rehabilitation exercises at home by tracking their wrist movements through a simple setup: a smartphone strapped to the forearm and a low-cost gaming controller called the Novint Falcon.
The Novint Falcon, a desktop robot typically used for video games, can guide users through specific arm motions and track the trajectory of its controller. But it cannot directly measure the angle of the user’s wrist, which is essential data for therapists providing remote rehabilitation.
In a paper presented at SPIE Smart Structures + Nondestructive Evaluation 2024, the researchers proposed using the Falcon in tandem with a smartphone’s built-in motion sensors to precisely monitor wrist angles during rehab exercises.
“Patients would strap their phone to their forearm and manipulate this robot,” said Maurizio Porfiri, NYU Tandon Institute Professor and director of its Center for Urban Science + Progress (CUSP), who is the paper’s senior author. “Data from the phone’s inertial sensors can then be combined with the robot’s measurements through machine learning to infer the patient’s wrist angle.”
The researchers collected data from a healthy subject performing tasks with the Falcon while wearing motion sensors on the forearm and hand to capture the true wrist angle. They then trained an algorithm to predict the wrist angles based on the sensor data and Falcon controller movements.
The resulting algorithm could predict wrist angles with over 90% accuracy, a promising initial step toward enabling remote therapy with real-time feedback in the absence of an in-person therapist.

“This technology could allow patients to undergo rehabilitation exercises at home while providing detailed data to therapists remotely assessing their progress,” Roni Barak Ventura, the paper’s lead author who was an NYU Tandon postdoctoral fellow at the time of the study. “It’s a low-cost, user-friendly approach to increasing access to crucial post-stroke care.”
The researchers plan to further refine the algorithm using data from more subjects. Ultimately, they hope the system could help stroke survivors stick to intensive rehab regimens from the comfort of their homes.
“The ability to do rehabilitation exercises at home with automatic tracking could dramatically improve quality of life for stroke patients,” said Barak Ventura. “This portable, affordable technology has great potential for making a difficult recovery process much more accessible.”
This study adds to NYU Tandon’s body of work that aims to improve stroke recovery. In 2022, Researchers from NYU Tandon began collaborating with the FDA to design a regulatory science tool based on biomarkers to objectively assess the efficacy of rehabilitation devices for post-stroke motor recovery and guide their optimal usage. A study from earlier this year unveiled advances in technology that uses implanted brain electrodes to recreate the speaking voice of someone who has lost speech ability, which can be an outcome from stroke. More

There’s a hot new BEC in town that has nothing to do with bacon, egg, and cheese. You won’t find it at your local bodega, but in the coldest place in New York: the lab of Columbia physicist Sebastian Will, whose experimental group specializes in pushing atoms and molecules to temperatures just fractions of a degree above absolute zero.
Writing in Nature, the Will lab, supported by theoretical collaborator Tijs Karman at Radboud University in the Netherlands, has successfully created a unique quantum state of matter called a Bose-Einstein Condensate (BEC) out of molecules.
Their BEC, cooled to just five nanoKelvin, or about -459.66 °F, and stable for a strikingly long two seconds, is made from sodium-cesium molecules. Like water molecules, these molecules are polar, meaning they carry both a positive and a negative charge. The imbalanced distribution of electric charge facilitates the long-range interactions that make for the most interesting physics, noted Will.
Research the Will lab is excited to pursue with their molecular BECs includes exploring a number of different quantum phenomena, including new types of superfluidity, a state of matter that flows without experiencing any friction. They also hope to turn their BECs into simulators that can recreate the enigmatic quantum properties of more complex materials, like solid crystals.
“Molecular Bose-Einstein condensates open up whole new areas of research, from understanding truly fundamental physics to advancing powerful quantum simulations,” he said. “This is an exciting achievement, but it’s really just the beginning.”
It’s a dream come true for the Will lab and one that’s been decades in the making for the larger ultracold research community.
To Go Colder, Add Microwaves
Microwaves are a form of electromagnetic radiation with a long history at Columbia. In the 1930s, physicist Isidor Isaac Rabi, who would go on to the Nobel Prize in Physics, did pioneering work on microwaves that led to the development of airborne radar systems. “Rabi was one of the first to control the quantum states of molecules and was a pioneer of microwave research,” said Will. “Our work follows in that 90-year-long tradition.”

While you may be familiar with the role of microwaves in heating up your food, it turns out they can also facilitate cooling. Individual molecules have a tendency to bump into each other and will, as a result, form bigger complexes that disappear from the samples. Microwaves can create small shields around each molecule that prevent them from colliding, an idea proposed by Karman, their collaborator in the Netherlands. With the molecules shielded against lossy collisions, only the hottest ones can be preferentially removed from the sample — the same physics principle that cools your cup of coffee when you blow along the top of it, explained author Niccolò Bigagli. Those molecules that remain will be cooler, and the overall temperature of the sample will drop.
The team came close to creating molecular BEC last fall in work published in Nature Physics that introduced the microwave shielding method. But another experimental twist was necessary.When they added a second microwave field, cooling became even more efficient and sodium-cesium finally crossed the BEC threshold — a goal the Will lab had harbored since it opened at Columbia in 2018.
“This was fantastic closure for me,” said Bigagli, who graduated with his PhD in physics this spring and was a founding lab member. “We went from not having a lab set up yet to these fantastic results.”
In addition to reducing collisions, the second microwave field can also manipulate the molecules’ orientation. That in turn is a means to control how they interact, which the lab is currently exploring. “By controlling these dipolar interactions, we hope to create new quantum states and phases of matter,” said co-author and Columbia postdoc Ian Stevenson.
A New World for Quantum Physics Opens
Ye, a pioneer of ultracold science based in Boulder, considers the results a beautiful piece of science. “The work will have important impacts on a number of scientific fields, including the study of quantum chemistry and exploration of strongly correlated quantum materials,” he commented. “Will’s experiment features precise control of molecular interactions to steer the system toward a desired outcome — a marvelous achievement in quantum control technology.”
The Columbia team, meanwhile, is excited to have a theoretical description of interactions between molecules that have been validated experimentally. “We really have a good idea of the interactions in this system, which is also critical for the next steps, like exploring dipolar many-body physics,” said Karman. “We’ve come up with schemes to control interactions, tested these in theory, and implemented them in the experiment. It’s been really an amazing experience to see these ideas for microwave ‘shielding’ being realized in the lab.”

There are dozens of theoretical predictions that can now be tested experimentally with the molecular BECs, which co-first author and PhD student Siwei Zhang noted, are quite stable. Most ultracold experiments take place within a second — some as short as a few milliseconds — but the lab’s molecular BECs last upwards of two seconds. “That will really let us investigate open questions in quantum physics,” he said.
One idea is to create artificial crystals with the BECs trapped in an optical lattice made from lasers. This would enable powerful quantum simulations that mimic the interactions in natural crystals, noted Will, which is a focus area of condensed matter physics. Quantum simulators are routinely made with atoms, but atoms have short-range interactions — they practically have to be on top of one another — which limits how well they can model more complicated materials. “The molecular BEC will introduce more flavor,” said Will.
That includes dimensionality, said co-first author and PhD student Weijun Yuan. “We would like to use the BECs in a 2D system. When you go from three dimensions to two, you can always expect new physics to emerge,” he said. 2D materials are a major area of research at Columbia; having a model system made of molecular BECs could help Will and his condensed matter colleagues explore quantum phenomena including superconductivity, superfluidity, and more.
“It seems like a whole new world of possibilities is opening up,” Will said. More

A novel, human-inspired approach to training artificial intelligence (AI) systems to identify objects and navigate their surroundings could set the stage for the development of more advanced AI systems to explore extreme environments or distant worlds, according to research from an interdisciplinary team at Penn State.
In the first two years of life, children experience a somewhat narrow set of objects and faces, but with many different viewpoints and under varying lighting conditions. Inspired by this developmental insight, the researchers introduced a new machine learning approach that uses information about spatial position to train AI visual systems more efficiently. They found that AI models trained on the new method outperformed base models by up to 14.99%. They reported their findings in the May issue of the journal Patterns.
“Current approaches in AI use massive sets of randomly shuffled photographs from the internet for training. In contrast, our strategy is informed by developmental psychology, which studies how children perceive the world,” said Lizhen Zhu, the lead author and doctoral candidate in the College of Information Sciences and Technology at Penn State.
The researchers developed a new contrastive learning algorithm, which is a type of self-supervised learning method in which an AI system learns to detect visual patterns to identify when two images are derivations of the same base image, resulting in a positive pair. These algorithms, however, often treat images of the same object taken from different perspectives as separate entities rather than as positive pairs. Taking into account environmental data, including location, allows the AI system to overcome these challenges and detect positive pairs regardless of changes in camera position or rotation, lighting angle or condition and focal length, or zoom, according to the researchers.
“We hypothesize that infants’ visual learning depends on location perception. In order to generate an egocentric dataset with spatiotemporal information, we set up virtual environments in the ThreeDWorld platform, which is a high-fidelity, interactive, 3D physical simulation environment. This allowed us to manipulate and measure the location of viewing cameras as if a child was walking through a house,” Zhu added.
The scientists created three simulation environments — House14K, House100K and Apartment14K, with ’14K’ and ‘100K’ referring to the approximate number of sample images taken in each environment. Then they ran base contrastive learning models and models with the new algorithm through the simulations three times to see how well each classified images. The team found that models trained on their algorithm outperformed the base models on a variety of tasks. For example, on a task of recognizing the room in the virtual apartment, the augmented model performed on average at 99.35%, a 14.99% improvement over the base model. These new datasets are available for other scientists to use in training through www.child-view.com.
“It’s always hard for models to learn in a new environment with a small amount of data. Our work represents one of the first attempts at more energy-efficient and flexible AI training using visual content,” said James Wang, distinguished professor of information sciences and technology and advisor of Zhu.
The research has implications for the future development of advanced AI systems meant to navigate and learn from new environments, according to the scientists.
“This approach would be particularly beneficial in situations where a team of autonomous robots with limited resources needs to learn how to navigate in a completely unfamiliar environment,” Wang said. “To pave the way for future applications, we plan to refine our model to better leverage spatial information and incorporate more diverse environments.”
Collaborators from Penn State’s Department of Psychology and Department of Computer Science and Engineering also contributed to this study. This work was supported by the U.S. National Science Foundation, as well as the Institute for Computational and Data Sciences at Penn State. More

MRI scans are commonly used to diagnose a variety of conditions, anything from liver disease to brain tumors. But, as anyone who has been through one knows, patients must remain completely still to avoid blurring the images and requiring a new scan. A prototype device described in ACS Sensors could change that. The self-powered sensor detects movement and shuts down an MRI scan in real time, improving the process for patients and technicians.
During an MRI scan, a patient must stay entirely still for several minutes at a time, otherwise “motion artifacts” could appear and blur the final image. To ensure a clear picture, patient movement needs to be identified as soon as it happens, allowing the scan to stop and for the technician to take a new one. Motion tracking could be achieved using sensors embedded into the MRI table; however, magnetic materials can’t be used because metals interfere with the MRI technology itself. One technology that’s well-suited for this unique situation, and avoids the need for metal or magnetic components, is the triboelectric nanogenerator (TENG), which powers itself using static electricity generated by friction between polymers. So, Li Tao, Zhiyi Wu and colleagues wanted to design a TENG-based sensor that could be incorporated into an MRI machine to help prevent motion artifacts.
The team created the TENG by sandwiching two layers of plastic film painted with graphite-based conductive ink around a central layer of silicone. These materials were specifically chosen as they would not interfere with an MRI scan. When pressed together, electrostatic charges from the plastic film moved to the conductive ink, creating a current that could then flow out through a wire.
This sensor was incorporated into an MRI table designed to lay under a patient’s head. In tests, when a person turned their head from side to side or raised it off the table, the sensor detected these movements and transmitted a signal to a computer. Then, an audible alert played, a pop-up window on the technician’s computer appeared and the MRI scan ceased. The researchers say that this work could help make MRI scans more efficient and less frustrating for patients and technicians alike by producing better images during a single procedure. More

As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study from Chalmers University of Technology in Sweden shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalised prices, and thus minimise both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.
Today’s commercial charging infrastructure can be a jungle. The market is dynamic and complex with a variety of subscriptions and free competition between providers. At some fast charging stations, congestion and long queues may even occur. In a new study, researchers at Chalmers have created a mathematical model to investigate how fast charging stations controlled by artificial intelligence, AI, can help by offering electric car drivers personalised prices, which the drivers can choose to accept or refuse. The AI uses algorithms that can adjust prices based on individual factors, such as battery level and the car’s geographic location.
“The electric car drivers can choose to share information with the charging station providers and receive a personal price proposal from a smart charging station. In our study, we could show how rational and self-serving drivers react by only accepting offers that are beneficial to themselves. This leads to both price and waiting times being minimized,” says Balázs Kulcsár, professor at the department of electrical engineering at Chalmers.
In the study, the drivers always had the option to refuse the personal price, and choose a conventional charging station with a fixed price instead. The personal prices received by the drivers could differ significantly from each other, but were almost always lower than the market prices. For the providers of charging stations, the iterative AI algorithm can find out which individual prices are accepted by the buyer, and under which conditions. However, during the course of the study, the researchers noted that on some occasions the algorithm raised the price significantly when the electric car’s batteries were almost completely empty, and the driver consequently had no choice but to accept the offer.
“Smart charging stations can solve complex pricing in a competitive market, but our study shows that they need to be developed and introduced with privacy protection for consumers, well in line with responsible-ethical AI paradigms,” says Balázs Kulcsár.
More about the study
The researchers created a mathematical model of the interaction between profit-maximising fast charging stations and electric car users. The “charging stations” could offer public market prices or AI-driven profit-maximising personal prices, which the “electric car users” could then accept or reject based on their own conditions and needs. In most cases, the results were promising, as the AI-generated prices were lower than the market prices. More

Researchers in George Mason University’s College of Public Health have leveraged the power of artificial intelligence (AI) analytical models to match a patient’s medical history to the most effective antidepressant, allowing patients to find symptom relief sooner. The free website, MeAgainMeds.com, provides evidence-based recommendations, allowing clinicians and patients to find the optimal antidepressant the first time.
“Many people with depression must try multiple antidepressants before finding the right one that alleviates their symptoms. Our website reduces the number of medications that patients are asked to try. The system recommends to the patient what has worked for at least 100 other patients with the same exact relevant medical history,” said Farrokh Alemi, principal investigator and professor of health informatics at George Mason University’s College of Public Health.
AI helped to simplify the very complex task of making thousands of guidelines easily accessible to patients and clinicians. The guidelines that researchers created are complicated because of the amount of clinical information that is relevant in prescribing an antidepressant; AI seamlessly simplifies the task.
With AI at its core, MeAgainMeds.com analyzes clinician or patient responses to a few anonymous medical history questions to determine which oral antidepressant would best meet the specific needs. The website does not ask for any personal identifiable information and it does not prescribe medication changes. Patients are advised to visit their primary health care provider for any changes in medication.
In 2018, the Centers for Disease Control reported that more than 13% of adults use antidepressants, and the number has only increased since the pandemic and other epidemics since 2020. This website could help millions of people find relief more quickly.
Alemi and his team analyzed 3,678,082 patients who took 10,221,145 antidepressants. The oral antidepressants analyzed were amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine. From the data, they created 16,770 subgroups of at least 100 cases, using reactions to prior antidepressants, current medication, history of physical illness, history of mental illness, key procedures, and other information. The subgroups and remission rates drive the AI to produce an evidence-based medication recommendation.
“By matching patients to the subgroups, clinicians can prescribe the medication that works best for people with similar medical history,” said Alemi. The researchers and website recommend that patients who use the site take the information to their clinicians, who will ultimately decide whether to prescribe the recommended medicine.
Alemi and his team tested a protype version of the site in 2023, which they advertised on social media. At that time, 1,500 patients used the website. Their goal is to improve the website and expand its user base. The initial research was funded by the Commonwealth of Virginia and by the Robert Wood Johnson Foundation.
The researchers’ most recent paper in a series of papers on response to antidepressants analyzed 2,467 subgroups of patients who had received psychotherapy. “Effectiveness of Antidepressants in Combination with Psychotherapy” was published online in The Journal of Mental Health Policy and Economics in March 2024. Additional authors include Tulay G Soylu from Temple University, and Mary Cannon and Conor McCandless from Royal College of Surgeons in Dublin, Ireland. More