Aurelia Butler

Ever wonder if the latest and greatest artificial intelligence (AI) tool you read about in the morning paper is going to save your life? A new study published in JAMA led by John W. Ayers, Ph.D., of the Qualcomm Institute within the University of California San Diego, finds that question can be difficult to answer since AI products in healthcare do not universally undergo any externally evaluated approval process assessing how it might benefit patient outcomes before coming to market.
The research team evaluated the recent White House Executive Order that instructed the Department of Health and Human Services to develop new AI-specific regulatory strategies addressing equity, safety, privacy, and quality for AI in healthcare before April 27, 2024. However, team members were surprised to find the order did not once mention patient outcomes, the standard metric by which healthcare products are judged before being allowed to access the healthcare marketplace.
“The goal of medicine is to save lives,” said Davey Smith, M.D., head of the Division of Infectious Disease and Global Public Health at UC San Diego School of Medicine, co-director of the university’s Altman Clinical and Translational Research Institute, and study senior author. “AI tools should prove clinically significant improvements in patient outcomes before they are widely adopted.”
According to the team, AI-powered early warning systems for sepsis, a fatal acute illness among hospitalized patients that affects 1.7 million Americans each year, demonstrates the consequences of inadequate prioritization of patient outcomes in regulations. A third-party evaluation of the most widely adopted AI sepsis prediction model revealed 67% of patients who developed sepsis were not identified by the system. Would hospital administrators have chosen this sepsis prediction system if trials assessing patient outcomes data were mandated, the team wondered, considering the array of available early warning systems for sepsis?
“We are calling for a revision to the White House Executive Order that prioritizes patient outcomes when regulating AI products,” added John W. Ayers, Ph.D., who is deputy director of informatics in Altman Clinical and Translational Research Institute in addition to his Qualcomm Institute affiliation. “Similar to pharmaceutical products, AI tools that impact patient care should be evaluated by federal agencies for how they improve patients’ feeling, function, and survival.”
The team points to its 2023 study in JAMA Internal Medicine on using AI-powered chatbots to respond to patient messages as an example of what patient outcome-centric regulations can achieve. “A study comparing standard care versus standard care enhanced by AI conversational agents found differences in downstream care utilization in some patient populations, such as heart failure patients,” said Nimit Desai, B.S., who is a research affiliate at the Qualcomm Institute, UC San Diego School of Medicine student, and study coauthor. “But studies like this don’t just happen unless regulators appropriately incentivize them. With a patient outcomes-centric approach, AI for patient messaging and all other clinical applications can truly enhance people’s lives.”
The team recognizes that its proposed regulatory strategy can be a significant lift for AI and healthcare industry partners and may not be necessary for every flavor of AI use case in healthcare. However, the researchers say, excluding patient outcomes-centric rules in the White House Executive Order is a serious omission. More

A new system that brings together real-world sensing and virtual reality would make it easier for building maintenance personnel to identify and fix issues in commercial buildings that are in operation. The system was developed by computer scientists at the University of California San Diego and Carnegie Mellon University.
The system, dubbed BRICK, consists of a handheld device equipped with a suite of sensors to monitor temperature, CO2 and airflow. It is also equipped with a virtual reality environment that has access to the sensor data and metadata in a specific building while being connected to the building’s electronic control system.
When an issue is reported in a specific location, a building manager can go on-site with the device and quickly scan the space with the Lidar tool on their smartphone, creating a virtual reality version of the space. The scanning can also occur ahead of time. Once they open this mixed reality recreation of the space on a smartphone or laptop, building managers can locate sensors, as well as the data gathered from the handheld device, overlaid onto that mixed reality environment.
The goal is to allow building managers to quickly identify issues by inspecting hardware and gathering and logging relevant data.
“Modern buildings are complex arrangements of multiple systems from climate control, lighting and security to occupant management. BRICK enables their efficient operation, much like a modern computer system,” said Rajesh K. Gupta, one of the paper’s senior authors, director of the UC San Diego Halicioglu Data Science Institute and a professor in the UC San Diego Department of Computer Science and Engineering.
Currently, when building managers receive reports of a problem, they first have to consult the building management database for that specific location. But the system doesn’t tell them where the sensors and hardware are located exactly in that space. So managers have to go to the location, gather more data with cumbersome sensors, then compare that data against the information in the building management system and try to deduce what the issue is. It’s also difficult to log the data gathered at various spatial locations in a precise way.
By contrast, with BRICK, the building manager can directly go to the location equipped with a handheld device and a laptop or smartphone. They will immediately have access on location to all the building management system data, the location of the sensors and the data from the handheld device all overlapping in one mixed reality environment. Using this system, the operators can also detect faults in the building equipment from stuck air-control valves to poorly operating handling systems.

In the future, researchers hope to find CO2, temperature and airflow sensors that can directly connect to a smartphone, to enable occupants to take part in managing local environments as well as to simplify building operations.
A team at Carnegie Mellon built the handheld device. Xiaohan Fu, a computer science Ph.D. student in the research group of Rajesh Gupta, director of the Halicioglu Data Science Institute, built the backend and VR components that build upon their earlier work on BRICK metadata schema that has been adopted by many commercial vendors.
Ensuring that the location used in the VR environment was accurate was a major challenge. GPS is only accurate to a radius of about a meter. In this case, the system needs to be accurate within a few inches. The researchers’ solution was to post a (few) AprilTags-similar to QR codes — in every room that would be read by the handheld device’s camera and recalibrate the system to the correct location.
“It’s an intricate system,” Fu said. “The mixed reality itself is not easy to build. From a software standpoint, connecting the building management system, where hardware, sensors and actuators are controlled, was a complex task that requires safety and security guarantees in a commercial environment. Our system architecture enables us to do it in an interactive and programmable way.”
The team presented their work at the BuildSys 23 Conference on Nov. 15 and 16 in Istanbul, Turkey.
The work was sponsored by the CONIX Research Center, one of the six centers in JUMP, a Semiconductor Research Corporation program sponsored by DARPA. More

Carbon nanostructures could become easier to design and synthesize thanks to a machine learning method that predicts how they grow on metal surfaces. The new approach, developed by researchers at Japan’s Tohoku University and China’s Shanghai Jiao Tong University, will make it easier to exploit the unique chemical versatility of carbon nanotechnology. The method was published in the journal Nature Communications.
The growth of carbon nanostructures on a variety of surfaces, including as atomically thin films, has been widely studied, but little is known about the dynamics and atomic-level factors governing the quality of the resulting materials. “Our work addresses a crucial challenge for realizing the potential of carbon nanostructures in electronics or energy processing devices,” says Hao Li of the Tohoku University team.
The wide range of possible surfaces and the sensitivity of the process to several variables make direct experimental investigation challenging. The researchers therefore turned to machine learning simulations as a more effective way to explore these systems.
With machine learning, various theoretical models can be combined with data from chemistry experiments to predict the dynamics of carbon crystalline growth and determine how it can be controlled to achieve specific results. The simulation program explores strategies and identifies which ones work and which don’t, without the need for humans to guide every step of the process.
The researchers tested this approach by investigating simulations of the growth of graphene, a form of carbon, on a copper surface. After establishing the basic framework, they showed how their approach could also be applied to other metallic surfaces, such as titanium, chromium and copper contaminated with oxygen.
The distribution of electrons around the nuclei of atoms in different forms of graphene crystals can vary. These subtle differences in atomic structure and electron arrangement affect the overall chemical and electrochemical properties of the material. The machine learning approach can test how these differences affect the diffusion of individual atoms and bonded atoms and the formation of carbon chains, arches and ring structures.
The team validated the results of the simulations through experiments and found that they closely matched. “Overall, our work provides a practical and efficient method for designing metallic or alloy substrates to achieve desired carbon nanostructures and explore further opportunities,” Li says.
He adds that future work will build on this to investigate topics such as the interfaces between solids and liquids in advanced catalysts and the chemical properties of materials used for processing and storing energy. More

At low enough temperatures, certain metals lose their electrical resistance and they conduct electricity without loss. This effect of superconductivity is known for more than hundred years and is well understood for so-called conventional superconductors. More recent, however, are unconventional superconductors, for which it is unclear yet how they work. A team from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), together with colleagues from the French research institution CEA (Commissariat à l’énergie atomique et aux énergies alternatives), from Tohoku University in Japan, and the Max Planck Institute for Chemical Physics of Solids in Dresden, has now gained new insights. The researchers report their recent findings in the journal Nature Communications. They could explain why a new material remains superconducting even at extremely high magnetic fields — a property that is missing in conventional superconductors, with the potential to enable previously unconceivable technological applications.
“Uranium ditelluride, or UTe2 for short, is a high-flyer among superconducting materials,” says Dr. Toni Helm from the Dresden High Magnetic Field Laboratory (HLD) at HZDR. “As discovered in 2019, the compound conducts electricity without loss, however, in a different way than conventional superconductors do.” Since then, research groups around the world have become interested in the material. This includes Helm’s team, which has come a step closer to understanding the material.
“To fully appreciate the hype surrounding the material, we need to take a closer look at superconductivity,” explains the physicist. “This phenomenon results from the movement of electrons in the material. Whenever they collide with atoms, they lose energy in form of heat. This manifests itself as electrical resistance. Electrons can avoid this by arranging themselves in pair formations, so-called Cooper pairs.” This is when two electrons combine at low temperatures to move through a solid without friction. They then make use of the atomic vibrations around them as a kind of wave on which they can surf without losing energy. These atomic vibrations explain conventional superconductivity.
“For some years now, however, superconductors have also been known in which Cooper pairs are formed by effects that are not yet fully understood,” says the physicist. One possible form of unconventional superconductivity is spin-triplet superconductivity. It is believed to make use of magnetic fluctuations. “There are also metals in which the conduction electrons come together collectively,” explains Helm. “Together, they can shield the magnetism of the material, behaving as a single particle with — for electrons — an extremely high mass.” Such superconducting materials are known as heavy-fermion superconductors. UTe2, therefore, could be both a spin-triplet and a heavy-fermion superconductor, as current experiments suggest. On top of all, it is the heavyweight world champion: To date, no other heavy-fermion superconductor is known that is still superconducting at similar or higher magnetic fields. This too was confirmed by the present study.
Extremely robust against magnetic fields
Superconductivity depends on two factors: the critical transition temperature and the critical magnetic field. If the temperature falls below the critical transition temperature, the resistance drops to zero and the material becomes superconducting. External magnetic fields also influence superconductivity. If these exceed a critical value, the effect collapses. “Physicists have a rule of thumb for this,” reports Helm: “In many conventional superconductors, the value of the transition temperature in Kelvin is roughly one to two times the value of the critical magnetic-field strength in tesla. In spin-triplet superconductors, this ratio is often much higher.” With their studies on the heavyweight UTe2, the researchers have now been able to raise the bar even higher: At a transition temperature of 1.6 kelvin (-271.55°C), the critical magnetic-field strength reaches 73 tesla, setting the ratio at 45 — a record.
“Until now, heavy-fermion superconductors were of little interest for technical applications,” explains the physicist. “They have a very low transition temperature and the effort required to cool them is comparatively high.” Nevertheless, their insensitivity to external magnetic fields could compensate for this shortcoming. This is because lossless current transport is mainly used today in superconducting magnets, for example in magnetic-resonance-imaging (MRI) scanners. However, the magnetic fields also influence the superconductor itself. A material that can withstand very high magnetic fields and still conducts electricity without loss would represent a major step forward.
Special treatment for a demanding material
“Of course, UTe2 cannot be used to make leads for a superconducting electromagnet,” says Helm. “Firstly, the material’s properties make it unsuitable for this endeavor, and secondly, it is radioactive. But it is perfectly suited for the exploration of the physics behind spin-triplet superconductivity.” Based on their experiments, the researchers developed a model that could serve as an explanation for superconductivity with extremely high stability against magnetic fields. To do this, they worked on samples with thicknesses of a few micrometers — only a fraction of the thickness of a human hair (approximately 70 micrometers). The radioactive radiation emitted by the samples, therefore, remains much lower than that of the natural background.
In order to obtain and shape such a tiny sample, Helm used a high-precision ion beam with a diameter of just a few nanometers as a cutting tool. UTe2 is an air-sensitive material. Consequently, Helm carries out the sample preparation in vacuum and seals them in epoxide glue afterwards. “For the final proof that our material is a spin-triplet superconductor, we would have to examine it spectroscopically while it is exposed to strong magnetic fields. However, current spectroscopy methods still struggle at magnetic fields above 40 tesla. Alongside other teams, we are also working on developing novel techniques. Eventually, this will enable us to provide definitive proof,” says Helm confidently. More

Poison ivy ranks among the most medically problematic plants. Up to 50 million people worldwide suffer annually from rashes caused by contact with the plant, a climbing, woody vine native to the United States, Canada, Mexico, Bermuda, the Western Bahamas and several areas in Asia.
It’s found on farms, in woods, landscapes, fields, hiking trails and other open spaces. So, if you go to those places, you’re susceptible to irritation caused by poison ivy, which can lead to reactions that require medical attention. Worse, most people don’t know poison ivy when they see it.
To find poison ivy before it finds you, University of Florida scientists published a new study in which they use artificial intelligence to confirm that an app can identify poison ivy.
Nathan Boyd, a professor of horticultural sciences at the UF/IFAS Gulf Coast Research and Education Center near Tampa, led the research. Renato Herrig, a post-doctoral researcher in Boyd’s lab, designed the app.
“We were the first to do this, and it was designed as a tool for hikers or others working outdoors,” Boyd said. “The app uses a camera to identify in real-time if poison ivy is present and provides you with a measure of certainty for the detection. It also functions even if you don’t have connectivity to the internet.”
The next step is to make the app commercially available, and there’s no timetable for that yet, Boyd said.
For the study, researchers collected thousands of images of poison ivy from five locations: Alderman’s Ford Conservation Park and Hillsborough River State Park, both in Florida; Eufala National Wildlife Refuge in Alabama; York River State Park in Virgina and Fall Creek Falls State Park in Tennessee.
They labeled images, and in each image, scientists put boxes around the leaves and stems of the plant. The boxed images were critical because poison ivy has a unique leaf arrangement and shape. Scientists use those characteristics to identify the plant.
They then ran the images through AI programs and taught a computer to recognize which plants are poison ivy. They also included images of plants that are not poison ivy or plants that look like poison ivy to be certain the computer learns to distinguish them.
“We believe that by integrating an object-detection algorithm, public health and plant science, our research can encourage and support further investigations to understand poison ivy distribution and minimize health concerns,” Boyd said. In their future work UF/IFAS researchers hope to expand the use of the app to identify more noxious plants. More

An international team that includes Rutgers University-New Brunswick scientists has developed a new method to make and manipulate a widely studied class of high-temperature superconductors.
This technique should pave the way for the creation of unusual forms of superconductivity in previously unattainable materials.
When cooled to a critical temperature, superconductors can conduct electricity without resistance or energy loss. These materials have intrigued physicists for decades because they can achieve a state of perfect conductivity allowing an electric current to flow indefinitely. But most superconductors only exhibit this peculiarity at temperatures so low — a few degrees above absolute zero — which renders them impractical.
The new work, published in Science, describes experiments that grew out of theoretical calculations that included those by a Rutgers team led by Jedediah Pixley, an associate professor in the Department of Physics and Astronomy in the Rutgers School of Arts and Sciences.
The experiments confirmed predictions by Pixley and Pavel Volkov, who at the time was a postdoctoral fellow at the Rutgers Center for Materials Theory. These predictions, based on mathematical models Pixley and Volkov (now at the University of Connecticut) devised to represent the underlying quantum physical behavior, projected how cuprate superconductors would behave if they were placed in proximity in specific configurations and at varying angles.
Superconductors are already in use today. Since the 1970s, scientists have employed superconducting magnets to generate the powerful magnetic fields needed for the operation of magnetic resonance imaging (MRI) machines. Maglev trains using the technology were introduced in the 1980s. More recently, scientists have harnessed the power of superconducting magnets to guide electron beams in experimental devices such as synchrotrons and accelerators.
In the future, scientists envision a world where ultra-efficient electricity grids, ultrafast and energy-efficient computer chips, and even quantum computers are powered by new kinds of superconducting materials.

The new experiments that validated Pixley and Volkov’s ideas were conducted by a team at Harvard University led by professor and physicist Philip Kim.
“We took two cuprate superconductors — materials that already were interesting — and, in placing them together and twisting them in a precise way, made something else that was very interesting: another superconductor which could have lots of technological applications,” said Pixley, a condensed matter theorist.
Because of its unique properties, the new superconductor is a promising candidate for the world’s first high-temperature, superconducting diode, essentially a switch that controls the flow of electrical current, the researchers said.
Such a device could potentially fuel fledgling industries such as quantum computing, which rely on fleeting phenomena produced in materials like superconductors, they added.
Pixley, who joined the Rutgers faculty in 2017, earned his doctoral degree by studying the conditions involved in producing superconductivity in unconventional materials. The latest research extends the field of “twistronics,” which involves twisting flat layers of two-dimensional materials to produce physical effects at the subatomic level that are observable on the macroscopic scale.
To Pixley, the study enlarges the paradigm of what materials can exhibit superconducting properties when twisted. The work yields other insights, as well.

“At the same time, we have found that this leads to a novel type of ‘magnetic’ superconducting state that has been long sought after, showing definitively that different superconducting phases can be reached via a twist,” he said.
The experimentalists first split an extremely thin film of a superconductive cuprate — nicknamed “BSCCO” and made of bismuth strontium calcium copper oxide — into two layers. Then, maintaining frigid conditions, they stacked the layers at a 45-degree twist, like an ice cream sandwich with askew wafers, retaining superconductivity at the fragile interface.
Cuprates are copper oxides that, decades ago, upended the physics world by showing they become superconducting at much higher temperatures than theorists had thought possible. BSCCO is considered a high-temperature superconductor because it starts superconducting at about -288 Fahrenheit. That is very cold by practical standards, but astonishingly high among classical superconductors, which typically must be cooled to about -400 Fahrenheit.
The work opens the door to more experiments, Pixley said.
“It will be very exciting to extend these experiments to other configurations of superconductors — twisted monolayers and a few twisted multilayers of superconductors at small twist angles,” Pixley said.
Other researchers on the study included scientists from the University of British Columbia, Brookhaven National Laboratory, the Leibniz Institute for Solid State and Materials Research in Germany, Seoul National University in South Korea and the National Institute for Materials Science in Japan. More

Plasmonics are special optical phenomena that are understood as interactions between light and matter and possess diverse shapes, material compositions, and symmetry-related behavior. The design of such plasmonic structures at the nanoscale level can pave the way for optical materials that respond to the orientation of light (polarization), which is not easily achievable in bulk size and existing materials. In this regard, “shadow growth” is a technique that utilizes vacuum deposition to produce nanoparticles from a wide range of 2D and 3D shapes in nanoscale. Recent research progress in controlling this shadow effect has broadened the possibilities for the creation of different nanostructures.
Now, in twin studies led by Assistant Professor Hyeon-Ho Jeong from the Gwangju Institute of Science and Technology (GIST), Republic of Korea, researchers have comprehensively shed light on the recent advances in shadow growth techniques for hybrid plasmonic nanomaterials, including clock-inspired designs containing magnesium (Mg). The studies were published in Advanced Materials on 25 March 2022 (with Jang-Hwan Han and Doeun Kim as co-first authors and Professor Peer Fischer and Dr. Jeong as co-corresponding authors) and Advanced Optical Materials on 20 November 2023 (with Juhwan Kim and Jang-Hwan Han as co-first authors and Dr. Jeong as the corresponding author), respectively.
The shadow effect here refers to the presence of “dark” areas on a surface that are concealed by “seed” molecules, and hence, inaccessible for the deposition of vaporized materials, much like shadow areas where light cannot reach. Elaborating on this further, Dr. Jeong says, “Since these shadowed areas are the regions where the material cannot be deposited, an array of three-dimensional nanostructures can be formed. This formation depends on the size of the seed, spacing between the seeds, and the inclination of the substrate.” Adding further, Doeun Kim, a Ph.D. student, says, “Creation of unique nanostructures is influenced by the introduction of rotation during the process, based on rotation speed, time, and angle, ultimately forming three-dimensional nanostructures.”
In the first study (featured as a cover-page article), the team showcased the production of various nanostructures using a specific shadow growth technique known as glancing angle deposition. These structures exhibit tunable optical properties achieved through suitable modifications to their material, shape, and surrounding environment. Their review also emphasizes a broad range of potential applications, including nano- and micro-robots for wound healing and drug delivery in the human body, photonic devices, and chiral spectroscopy, among others.
For the subsequent study, the team created 3D rotamers (molecules with specific rotational arrangements) capable of both linear and circular polarization, as well as of storing a significant amount of information. This clock-inspired design involves placing two nanorods made of Mg at a certain modifiable angle, resembling the hour and minute hands of a clock. These nanostructures also hold promise for various applications, such as the secure verification of items like banknotes, anti-counterfeiting devices, and displays capable of transitioning to desired optical states, as needed.
Talking about these developments and envisioning the future of plasmonics, Dr. Jeong says, “These rotamers can have potential utilization in physically unclonable functions, an area currently under intensive research for ensuring robust security levels of hardware, such as PCs or servers.” Explaining further, Ph.D. student Juhwan Kim says, “In particular, the ability to selectively filter UV light sources and specific visible wavelengths depending on the polarization state can also be used in glasses and windows to protect eyes and skin by blocking UV rays from sunlight.” More

High-entropy alloys can withstand extreme heat and stress, making them suitable for a variety of specific applications. A new study at the X-ray synchrotron radiation source BESSY II has now provided deeper insights into the ordering processes and diffusion phenomena in these materials. The study involved teams from HZB, the Federal Institute for Materials Research and Testing, the University of Latvia and the University of Münster.
The team analysed samples of a so-called Cantor alloy, which consists of five 3D elements: chromium, manganese, iron, cobalt and nickel. The samples of crystalline structures (face-centred cubic, fcc) were annealed at two different temperatures and then shock frozen.
The study focussed on unravelling local atomic structures in single crystalline samples cooled from either a high-temperature (HT) state annealed at 1373 Kelvin or a low-temperature (LT) state annealed at 993 Kelvin. To analyse the local environments of the individual elements in the samples, the team used a well established method: element-specific multi-edge X-ray absorption spectroscopy (EXAFS). To interpret the measurement data in the most precise and unbiased manner, the team carried out a Reverse Monte Carlo (RMC) based analysis.
“In this way, we have been able to reveal, both qualitatively and quantitatively, the peculiarities of the characteristic local environments of each principal components of the alloy at the atomic scale,” explains Dr Alevtina Smekhova from HZB. In particular, the spectroscopic results also provide insights into the diffusion processes in HEAs. For example, it was directly demonstrated why the element manganese diffuses fastest in the HT samples, while the element nickel diffuses faster in the LT samples as it was found earlier from diffusion experiments.
“These results help us to better understand the relationship between the local atomic environment and the macroscopic properties in these alloys,” explains Smekhova. More