Aurelia Butler

Many children announce their arrival in the delivery room with a piercing cry. As a newborn automatically takes its first breath, the lungs inflate, the blood vessels in the lungs widen, and the whole circulatory system reconfigures itself to life outside the womb. This process doesn’t always go to plan, however. Some infants — particularly those who are very sick or born prematurely — suffer from pulmonary hypertension, a serious disorder in which the arteries to the lungs remain narrowed after delivery or close up again in the first few days or weeks after birth. This constricts the flow of blood to the lungs, reducing the amount of oxygen in the blood.
Prompt diagnosis and treatment improve prognosis
Severe cases of pulmonary hypertension need to be detected and treated as rapidly as possible. The sooner treatment begins, the better the prognosis for the newborn infant. Yet making the correct diagnosis can be challenging. Only experienced paediatric cardiologists are able to diagnose pulmonary hypertension based on a comprehensive ultrasound examination of the heart. “Detecting pulmonary hypertension is time-consuming and requires a cardiologist with highly specific expertise and many years of experience. Only the largest paediatric clinics tend to have those skills on hand,” says Professor Sven Wellmann, Medical Director of the Department of Neonatology at KUNO Klinik St. Hedwig, part of the Hospital of the Order of St. John in Regensburg in Germany.
Researchers from the group led by Julia Vogt, who runs the Medical Data Science Group at ETH Zurich, recently teamed up with neonatologists at KUNO Klinik St. Hedwig to develop a computer model that provides reliable support in diagnosing the disease in newborn infants. Their results have now been published in the International Journal of Computer Vision.
Making AI reliable and explainable
The ETH researchers began by training their algorithm on hundreds of video recordings taken from ultrasound examinations of the hearts of 192 newborns. This dataset also included moving images of the beating heart taken from different angles as well as diagnoses by experienced paediatric cardiologists (is pulmonary hypertension present or not) and an evaluation of the disease’s severity (“mild” or “moderate to severe”). To determine the algorithm’s success at interpreting the images, the researchers subsequently added a second dataset of ultrasound images from 78 newborn infants, which the model had never seen before. The model suggested the correct diagnosis in around 80 to 90 percent of cases and was able to determine the correct level of disease severity in around 65 to 85 percent of cases.
“The key to using a machine-learning model in a medical context is not just the prediction accuracy, but also whether humans are able to understand the criteria the model uses to make decisions,” Vogt says. Her model makes this possible by highlighting the parts of the ultrasound image on which its categorisation is based. This allows doctors to see exactly which areas or characteristics of the heart and its blood vessels the model considered to be suspicious. When the paediatric cardiologists examined the datasets, they discovered that the model looks at the same characteristics as they do, even though it was not explicitly programmed to do so.
A human makes the diagnosis
This machine-learning model could potentially be extended to other organs and diseases, for example to diagnose heart septal defects or valvular heart disease.
It could also be useful in regions where no specialists are available: standardised ultrasound images could be taken by a healthcare professional, and the model could then provide a preliminary risk assessment and an indication of whether a specialist should be consulted. Medical facilities that do have access to highly qualified specialists could use the model to ease their workload and to help reach a better and more objective diagnosis. “AI has the potential to make significant improvements to healthcare. The crucial issue for us is that the final decision should always be made by a human, by a doctor. AI should simply be providing support to ensure that the maximum number of people can receive the best possible medical care,” Vogt says. More

Room-scale virtual reality (VR) is one where users explore a VR environment by physically walking through it. The technology provides many benefits given its highly immersive experience. Yet the drawbacks are that it requires large physical spaces. It can also lack the haptic feedback when touching objects.
Take for example opening a door. Implementing this seemingly menial task in the virtual world means recreating the haptics of grasping a doorknob whilst simultaneously preventing users from walking into actual walls in their surrounding areas.
Now, a research group has developed a new system to overcome this problem: RedirectedDoors+.
The group was led by Kazuyuki Fujita, Kazuki Takashima, and Yoshifumi Kitamura from Tohoku University and Morten Fjeld from Chalmers University of Technology and the University of Bergen.
“Our system, which built upon an existing visuo-haptic door-opening redirection technique, allows participants to subtly manipulate the walking direction while opening doors in VR, guiding them away from real walls,” points out Professor Fujita, who is based at Tohoku University’s Research Institute of Electrical Communication (RIEC). “At the same time, our system reproduces the realistic haptics of touching a doorknob, enhancing the quality of the experience.”
To provide users with that experience, RedirectedDoors+ employs a small number of ‘door robots.’ The robots have a doorknob-shaped attachment and can move in any direction, giving immediate touch feedback when the user interacts with the doorknob. In addition, the VR environment rotates in sync with the door movement, ensuring the user stays within the physical space limits.
A simulation study conducted to evaluate the performance of the system demonstrated the physical space size could be significantly reduced in six different VR environments. A validation study with 12 users walking with the system likewise demonstrated that this system works safely in real-world environments.
“RedirectDoors+ has redefined the boundaries of VR exploration, offering unprecedented freedom and realism in virtual environments,” adds Fujita. “It has a wide range of applicability, such as in VR vocational training, architectural design, and urban planning.” More

Existing ship control systems using Model Predictive Control for Maritime Autonomous Surface Ships (MASS) do not consider the various forces acting on ships in real sea conditions. Addressing this gap, in a new study, researchers developed a novel time-optimal control method, that accounts for the real wave loads acting on a ship, enabling effective planning and control of MASS at sea.
The study of ship manoeuvring at sea has long been the central focus of the shipping industry. With the rapid advancements in remote control, communication technologies and artificial intelligence, the concept of Maritime Autonomous Surface Ships (MASS) has emerged as a promising solution for autonomous marine navigation. This shift highlights the growing need for optimal control models for autonomous ship manoeuvring.
Designing a control system for time-efficient ship manoeuvring is one of the most difficult challenges in autonomous ship control. While many studies have investigated this problem and proposed various control methods, including Model Predictive Control (MPC), most have focused on control in calm waters, which do not represent real operating conditions. At sea, ships are continuously affected by different external loads, with loads from sea waves being the most significant factor affecting manoeuvring performance.
To address this gap, a team of researchers, led by Assistant Professor Daejeong Kim from the Division of Navigation Convergence Studies at the Korea Maritime & Ocean University in South Korea, designed a novel time-optimal control method for MASS. “Our control model accounts for various forces that act on the ship, enabling MASS to better navigate and track targets in dynamic sea conditions,” says Dr. Kim. Their study was made available online on January 05, 2024, and published in Volume 293 of the journal Ocean Engineering on February 1, 2024.
At the heart of this innovative control system is a comprehensive mathematical ship model that accounts for various forces in the sea, including wave loads, acting on key parts of a ship such as the hull, propellers, and rudders. However, this model cannot be directly used to optimise the manoeuvring time. For this, the researchers developed a novel time optimisation model that transforms the mathematical model from a temporal formulation to a spatial one. This successfully optimises the manoeuvring time.
These two models were integrated into a nonlinear MPC controller to achieve time-optimal control. They tested this controller by simulating a real ship model navigating in the sea with different wave loads. Additionally, for effective course planning and tracking researchers proposed three control strategies: Strategy A excluded wave loads during both the planning and tracking stages, serving as a reference; Strategy B included wave loads only in the planning stage, and Strategy C included wave loads in both stages, measuring their influence on both propulsion and steering.
Experiments revealed that wave loads increased the expected manoeuvring time on both strategies B and C. Comparing the two strategies, the researchers found strategy B to be simpler with lower performance than strategy C, with the latter being more reliable. However, strategy C places an additional burden on the controller by including wave load prediction in the planning stage.
“Our method enhances the efficiency and safety of autonomous vessel operations and potentially reduces shipping costs and carbon emissions, benefiting various sectors of the economy,” remarks Dr. Kim, highlighting the potential of this study. “Overall, our study addresses a critical gap in autonomous ship manoeuvring which could contribute to the development of a more technologically advanced maritime industry.” More

A new tool being developed by the University of Copenhagen and 3Shape will help orthodontists correctly fit braces onto teeth. Using artificial intelligence and virtual patients, the tool predicts how teeth will move, so as to ensure that braces are neither too loose nor too tight.
Many of us remember the feeling of having our braces regularly adjusted and retightened at the orthodontist’s office. And every year, about 30 percent of Danish youth up to the age of 15 wear braces to align crooked teeth. Orthodontists use the knowledge gained from their educations and experience to perform their jobs, but without the possibilities that a computer can provide for predicting final results.
A new tool, developed in a collaboration between the University of Copenhagen’s Department of Computer Science and the company 3Shape, makes it possible to simulate how braces should fit to give the best result without too many unnecessary inconveniences.
The tool has been developed with the help of scanned imagery of teeth and bone structures from human jaws, which artificial intelligence then uses to predict how sets of braces should be designed to best straighten a patient’s teeth.
“Our simulation is able to let an orthodontist know where braces should and shouldn’t exert pressure to straighten teeth. Currently, these interventions are based entirely upon the discretion of orthodontists and involve a great deal of trial and error. This can lead to many adjustments and visits to the orthodontist’s office, which our simulation can help reduce in the long run,” says Professor Kenny Erleben, who heads IMAGE (Image Analysis, Computational Modelling and Geometry), a research section at UCPH’s Department of Computer Science.
Helps predict tooth movement
It’s no wonder that it can be difficult to predict exactly how braces will move teeth, because teeth continue shifting slightly throughout a person’s life. And, these movements are very different from mouth to mouth.

“The fact that tooth movements vary from one patient to another makes it even more challenging to accurately predict how teeth will move for different people. Which is why we’ve developed a new tool and a dataset of different models to help overcome these challenges,” explains Torkan Gholamalizadeh, from 3Shape and a PhD from the Department of Computer Science.
As an alternative to the classic bracket and braces, a new generation of clear braces, known as aligners, has gained ground. Aligners are designed as a transparent plastic cast of the teeth that patients fit over their teeth.
Patients must wear aligners for at least 22 hours a day and they need to be swapped for new and tighter sets every two weeks. Because aligners are made of plastic, a person’s teeth also change the contours of the aligner itself, something that the new tool also takes into account.
“As transparent aligners are softer than metal braces, calculating how much force it takes to move the teeth becomes even more complicated. But it’s a factor that we’ve taught our model to take into account, so that one can predict tooth movements when using aligners as well,” says Torkan Gholamalizadeh.
Digital twins can improve treatment
Researchers created a computer model that creates accurate 3D simulations of an individual patient’s jaw, and which dentists and technicians can use to plan the best possible treatment.

To create these simulations, researchers mapped sets of human teeth using detailed CT scans of teeth and of the small, fine structures between the jawbone and the teeth known as peridontal ligaments — a kind of fiber-rich connective tissue that holds teeth firmly in the jaw.
This type of precise digital imitation is referred to as a digital twin — and in this context, the researchers built up a database of ‘digital dental patients’.
But they didn’t stop there. The researchers’ database also contains other digital patient types that could one day be of use elsewhere in the healthcare sector:
“Right now, we have a database of digital patients that, besides simulating aligner designs, can be used for hip implants, among other things. In the long run, this could make life easier for patients and save resources for society,” says Kenny Erleben.
The area of research that makes use of digital twins is relatively new and, for the time being, Professor Erleben’s database of virtual patients is a world leader. However, the database will need to get even bigger if digital twins are to really take root and have benefit the healthcare sector and society.
“More data will allow us to simulate treatments and adapt medical devices so as to more precisely target patients across entire populations,” says Professor Erleben.
Furthermore, the tool must clear various regulatory hurdles before it is rolled out for orthodontists. This is something that the researchers hope to see in the foreseeable future.
Box: Digital twins
A digital twin is a virtual model that lives in the cloud, and is designed to accurately mirror a human being, physical object, system, or real-world process.
“The virtual model can answer what’s happening in the real world, and do so instantly. For example, one can ask what would happen if you pushed on one tooth and get answers with regards to where it would move and how it would affect other teeth. This can be done quickly, so that you know what’s happening. Today, weeks must pass before finding out whether a desired effect has been achieved,” says Professor Kenny Erleben.
Digital twins can be used to plan, design and optimize — and can therefore be used to operate companies, robots, factories and used much more in the energy, healthcare and other sectors.
One of the goals of working with digital twins at the Department of Computer Science is to be able to create simulations of populations, for example, in the healthcare sector. If working with a medical product, virtual people must be exposed to and tested for their reactions in various situations. A simulation provides a picture of what would happen to an individual — and finally, to an entire population.
About the study
In their study, the researchers developed a simulation tool using CT scans of teeth, which can predict how a dental brace should best be designed and attached.
The research is described in the studies: “Deep-learning-based segmentation of individual tooth and bone with periodontal ligament interface details for simulation purposes” and “Open-Full-Jaw: An open-access dataset and pipeline for finite element models of human jaw.”
The research is part of the EU research project Rainbow, which conducts research into computer-simulated medicine across seven European universities in collaboration with government agencies and industry.
The research was conducted in collaboration with the company 3Shape, which manufactures intraoral scanners and provides medical software for digital dentistry purposes. More

Is there a way to stick hard and soft materials together without any tape, glue or epoxy? A new study published in ACS Central Science shows that applying a small voltage to certain objects forms chemical bonds that securely link the objects together. Reversing the direction of electron flow easily separates the two materials. This electroadhesion effect could help create biohybrid robots, improve biomedical implants and enable new battery technologies.
When an adhesive is used to attach two things, it binds the surfaces either through mechanical or electrostatic forces. But sometimes those attractions or bonds are difficult, if not impossible, to undo. As an alternative, reversible adhesion methods are being explored, including electroadhesion (EA). Though the term is used to describe a few different phenomena, one definition involves running an electric current through two materials causing them to stick together, thanks to attractions or chemical bonds. Previously, Srinivasa Raghavan and colleagues demonstrated that EA can hold soft, oppositely charged materials together, and even be used to build simple structures. This time, they wanted to see if EA could reversibly bind a hard material, such as graphite, to a soft material, such as animal tissue.
The team first tested EA using two graphite electrodes and an acrylamide gel. A small voltage (5 volts) was applied for a few minutes, causing the gel to permanently adhere to the positively charged electrode. The resulting chemical bond was so strong that, when one of the researchers tried to wrench the two pieces apart, the gel tore before it disconnected from the electrode. Notably, when the current’s direction was reversed, the graphite and gel easily separated — and the gel instead adhered to the other electrode, which was now positively charged. Similar tests were run on a variety of materials — metals, various gel compositions, animal tissues, fruits and veggies — to determine the phenomenon’s ubiquity.
For EA to occur, the authors found that the hard material needs to conduct electrons, and the soft material needs to contain salt ions They hypothesize that the adhesion arises from chemical bonds that form between the surfaces after an exchange of electrons. This may explain why some metals that hold onto their electrons strongly, including titanium, and some fruits that contain more sugar than salts, including grapes, failed to adhere in some situations. A final experiment showed that EA can occur completely underwater, revealing an even wider range of possible applications. The team says that this work could help create new batteries, enable biohybrid robotics, enhance biomedical implants and much more. More

Computers work in digits — 0s and 1s to be exact. Their calculations are digital; their processes are digital; even their memories are digital. All of which requires extraordinary power resources. As we look to the next evolution of computing and developing neuromorphic or “brain like” computing, those power requirements are unfeasible.
To advance neuromorphic computing, some researchers are looking at analog improvements. In other words, not just advancing software, but advancing hardware too. Research from the University of California San Diego and UC Riverside shows a promising new way to store and transmit information using disordered superconducting loops.
The team’s research, which appears in the Proceedings of the National Academy of Sciences, offers the ability of superconducting loops to demonstrate associative memory, which, in humans, allows the brain to remember the relationship between two unrelated items.
“I hope what we’re designing, simulating and building will be able to do that kind of associative processing really fast,” stated UC San Diego Professor of Physics Robert C. Dynes, who is one of the paper’s co-authors.
Creating lasting memories
Picture it: you’re at a party and run into someone you haven’t seen in a while. You know their name but can’t quite recall it. Your brain starts to root around for the information: where did I meet this person? How were we introduced? If you’re lucky, your brain finds the pathway to retrieve what was missing. Sometimes, of course, you’re unlucky.
Dynes believes that short-term memory moves into long-term memory with repetition. In the case of a name, the more you see the person and use the name, the more deeply it is written into memory. This is why we still remember a song from when we were ten years old but can’t remember what we had for lunch yesterday.

“Our brains have this remarkable gift of associative memory, which we don’t really understand,” stated Dynes, who is also president emeritus of the University of California and former UC San Diego chancellor. “It can work through the probability of answers because it’s so highly interconnected. This computer brain we built and modeled is also highly interactive. If you input a signal, the whole computer brain knows you did it.”
Staying in the loop
How do disordered superconducting loops work? You need a superconducting material — in this case, the team used yttrium barium copper oxide (YBCO). Known as a high-temperature superconductor, YBCO becomes superconducting around 90 Kelvin (-297 F), which in the world of physics, is not that cold. This made it relatively easy to modify. The YBCO thin films (about 10 microns wide) were manipulated with a combination of magnetic fields and currents to create a single flux quantum on the loop. When the current was removed, the flux quantum stayed in the loop. Think of this as a piece of information or memory.
This is one loop, but associative memory and processing require at least two pieces of information. For this, Dynes used disordered loops, meaning the loops are different sizes and follow different patterns — essentially random.
A Josephson juncture, or “weak link,” as it is sometimes known, in each loop acted as a gate through which the flux quanta could pass. This is how information is transferred and the associations are built.
Although traditional computing architecture has continuous high-energy requirements, not just for processing but also for memory storage, these superconducting loops show significant power savings — on the scale of a million times less. This is because the loops only require power when performing logic tasks. Memories are stored in the physical superconducting material and can remain there permanently, as long as the loop remains superconducting.

The number of memory locations available increases exponentially with more loops: one loop has three locations, but three loops have 27. For this research, the team built four loops with 81 locations. Next, Dynes would like to expand the number of loops and the number memory locations.
“We know these loops can store memories. We know the associative memory works. We just don’t know how stable it is with a higher number of loops,” he said.
This work is not only noteworthy to physicists and computer engineers; it may also be important to neuroscientists. Dynes talked to another University of California president emeritus, Richard Atkinson, a world-renowned cognitive scientist who helped create a seminal model of human memory called the Atkinson-Shiffrin model.
Atkinson, who is also former UC San Diego chancellor and professor emeritus in the School of Social Sciences, was excited about the possibilities he saw: “Bob and I have had some great discussions trying to determine if his physics-based neural network could be used to model the Atkinson-Shiffrin theory of memory. His system is quite different from other proposed physics-based neural networks, and is rich enough that it could be used to explain the workings of the brain’s memory system in terms of the underlying physical process. It’s a very exciting prospect.”
Full list of authors: Uday S. Goteti and Robert C. Dynes (both UC San Diego); Shane A. Cybart (UC Riverside).
This work was primarily supported as part of the Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) (Department of Energy DE-SC0019273). Other support was provided by the Department of Energy National Nuclear Security Agency (DE-NA0004106) and the Air Force Office of Scientific Research (FA9550-20-1-0144). More

Through steady advances in the development of quantum computers and their ever-improving performance, it will be possible in the future to crack our current encryption processes. To address this challenge, researchers at the Technical University of Munich (TUM) are participating in an international research consortium to develop encryption methods that will apply physical laws to prevent the interception of messages. To safeguard communications over long distances, the QUICK³ space mission will deploy satellites.
How can it be ensured that data transmitted through the internet can be read only by the intended recipient? At present our data are encrypted with mathematical methods that rely on the idea that the factorization of large numbers is a difficult task. With the increasing power of quantum computers, however, these mathematical codes will probably no longer be secure in the future.
Encryption by means of physical laws
Tobias Vogl, a professor of Quantum Communication Systems Engineering, is working on an encryption process that relies on principles of physics. “Security will be based on the information being encoded into individual light particles and then transmitted. The laws of physics do not permit this information to be extracted or copied. When the information is intercepted, the light particles change their characteristics. Because we can measure these state changes, any attempt to intercept the transmitted data will be recognized immediately, regardless of future advances in technology,” says Tobias Vogl.
The big challenge in so-called quantum cryptography lies in the transmission of data over long distances. In classical communications, information is encoded in many light particles and transmitted through optical fibers. However, the information in a single particle cannot be copied. As a result, the light signal cannot be repeatedly amplified, as with current optical fiber transmissions. This limits the transmission distance for the information to a few hundred kilometers.
To send information to other cities or continents, the structure of the atmosphere will be used. At altitudes higher than around 10 kilometers, the atmosphere is so thin that light is neither scattered nor absorbed. This will make it possible to use satellites in order to extend quantum communications over longer distances.
Satellites for quantum communications
As part of the QUICK³ mission, Tobias Vogl and his team are developing an entire system, including all of the components needed to build a satellite for quantum communications. In a first step, the team tested each of the satellite components. The next step will be to try out the entire system in space. The researchers will investigate whether the technology can withstand outer space conditions and how the individual system components interact. The satellite launch is scheduled for 2025. To create an overarching network for quantum communications, however, hundreds or perhaps thousands of satellites will be needed.
Hybrid network for encryption
The concept does not necessarily require all information to be transmitted using this method, which is highly complex and costly. It is conceivable that a hybrid network could be implemented in which data can be encrypted either physically or mathematically. Antonia Wachter-Zeh, a professor of Coding and Cryptography, is working to develop algorithms sufficiently complex that not even quantum computers can solve them. In the future it will still be enough to encrypt most information using mathematical algorithms. Quantum cryptography will be an option only for documents requiring special protection, for example in communications between banks. More

Scientists from Ames National Laboratory have identified the first unconventional superconductor with a chemical composition also found in nature. Miassite is one of only four minerals found in nature that act as a superconductor when grown in the lab. The team’s investigation of miassite revealed that it is an unconventional superconductor with properties similar to high-temperature superconductors. Their findings further scientists’ understanding of this type of superconductivity, which could lead to more sustainable and economical superconductor-based technology in the future.
Superconductivity is when a material can conduct electricity without energy loss. Superconductors have applications including medical MRI machines, power cables, and quantum computers. Conventional superconductors are well understood but have low critical temperatures. The critical temperature is the highest temperature at which a material acts as a superconductor.
In the 1980s, scientists discovered unconventional superconductors, many of which have much higher critical temperatures. According to Ruslan Prozorov, a scientist at Ames Lab, all these materials are grown in the lab. This fact has led to the general belief that unconventional superconductivity is not a natural phenomenon.
Prozorov explained that it is difficult to find superconductors in nature because most superconducting elements and compounds are metals and tend to react with other elements, like oxygen. He said that miassite (Rh17S15) is an interesting mineral for several reasons, one of which is its complex chemical formula. “Intuitively, you think that this is something which is produced deliberately during a focused search, and it cannot possibly exist in nature,” said Prozorov, “But it turns out it does.”
Paul Canfield, Distinguished Professor of Physics and Astronomy at Iowa State University and a scientist at Ames Lab, has expertise in design, discovery, growth, and characterization of novel crystalline materials. He synthesized high quality miassite crystals for this project. “Although miassite is a mineral that was discovered near the Miass River in Chelyabinsk Oblast, Russia,” said Canfield, “it is a rare one that generally does not grow as well-formed crystals.”
Growing the miassite crystals was part of a larger effort to discover compounds that combine very high melting elements (like Rh) and volatile elements (like S). “Contrary to the nature of the pure elements, we have been mastering the use of mixtures of these elements that allow for low temperature growth of crystals with minimal vapor pressure,” said Canfield. “It’s like finding a hidden fishing hole that is full of big fat fish. In the Rh-S system we discovered three new superconductors. And, through Ruslan’s detailed measurements, we discovered that the miassite is an unconventional superconductor.”
Prozorov’s group specializes in advanced techniques to study superconductors at low temperatures. He said the material needed to be as cold as 50 millikelvins, which is about -460 degrees Fahrenheit.

Prozorov’s team used three different tests to determine the nature of miassite’s superconductivity. The main test is called the “London penetration depth.” It determines how far a weak magnetic field can penetrate the superconductor bulk from the surface. In a conventional superconductor, this length is basically constant at low temperature. However, in unconventional superconductors, it varies linearly with the temperature. This test showed that miassite behaves as an unconventional superconductor.
Another test the team performed was introducing defects into the material. Prozorov said that this test is a signature technique his team has employed over the past decade. It involves bombarding the material with high-energy electrons. This process knocks-out ions from their positions, thus creating defects in the crystal structure. This disorder can cause changes in the material’s critical temperature.
Conventional superconductors are not sensitive to non-magnetic disorder, so this test would show no or very little change in the critical temperature. Unconventional superconductors have a high sensitivity to disorder, and introducing defects changes or suppresses the critical temperature. It also affects the critical magnetic field of the material. In miassite, the team found that both the critical temperature and the critical magnetic field behaved as predicted in unconventional superconductors.
Investigating unconventional superconductors improves scientists understanding of how they work. Prozorov explained that this is important because, “Uncovering the mechanisms behind unconventional superconductivity is key to economically sound applications of superconductors.” More