Computers Math

Can AI be trusted? The question pops up wherever AI is used or discussed — which, these days, is everywhere.
It’s a question that even some AI systems ask themselves.
Many machine-learning systems create what experts call a “confidence score,” a value that reflects how confident the system is in its decisions. A low score tells the human user that there is some uncertainty about the recommendation; a high score indicates to the human user that the system, at least, is quite sure of its decisions. Savvy humans know to check the confidence score when deciding whether to trust the recommendation of a machine-learning system.
Scientists at the Department of Energy’s Pacific Northwest National Laboratory have put forth a new way to evaluate an AI system’s recommendations. They bring human experts into the loop to view how the ML performed on a set of data. The expert learns which types of data the machine-learning system typically classifies correctly, and which data types lead to confusion and system errors. Armed with this knowledge, the experts then offer their own confidence score on future system recommendations.
The result of having a human look over the shoulder of the AI system? Humans predicted the AI system’s performance more accurately.
Minimal human effort — just a few hours — evaluating some of the decisions made by the AI program allowed researchers to vastly improve on the AI program’s ability to assess its decisions. In some analyses by the team, the accuracy of the confidence score doubled when a human provided the score.
The PNNL team presented its results at a recent meeting of the Human Factors and Ergonomics Society in Washington, D.C., part of a session on human-AI robot teaming.

“If you didn’t develop the machine-learning algorithm in the first place, then it can seem like a black box,” said Corey Fallon, the lead author of the study and an expert in human-machine interaction. “In some cases, the decisions seem fine. In other cases, you might get a recommendation that is a real head-scratcher. You may not understand why it’s making the decisions it is.”
The grid and AI
It’s a dilemma that power engineers working with the electric grid face. Their decisions based on reams of data that change every instant keep the lights on and the nation running. But power engineers may be reluctant to turn over decision-making authority to machine-learning systems.
“There are hundreds of research papers about the use of machine learning in power systems, but almost none of them are applied in the real world. Many operators simply don’t trust ML. They have domain experience — something that ML can’t learn,” said coauthor Tianzhixi “Tim” Yin.
The researchers at PNNL, which has a world-class team modernizing the grid, took a closer look at one machine-learning algorithm applied to power systems. They trained the SVM (support-vector machine) algorithm on real data from the grid’s Eastern Interconnection in the U.S. The program looked at 124 events, deciding whether a generator was malfunctioning, or whether the data was showing other types of events that are less noteworthy.
The algorithm was 85% reliable in its decisions. Many of its errors occurred when there were complex power bumps or frequency shifts. Confidence scores created with a human in the loop were a marked improvement over the system’s assessment of its own decisions. The human expert’s input predicted the algorithm’s decisions with much greater accuracy.

More human, better machine learning
Fallon and Yin call the new score an “Expert-Derived Confidence” score, or EDC score.
They found that, on average, when humans weighed in on the data, their EDC scores predicted model behavior that the algorithm’s confidence scores couldn’t predict.
“The human expert fills in gaps in the ML’s knowledge,” said Yin. “The human provides information that the ML did not have, and we show that that information is significant. The bottom line is that we’ve shown that if you add human expertise to the ML results, you get much better confidence.”
The work by Fallon and Yin was funded by PNNL through an initiative known as MARS — Mathematics for Artificial Reasoning in Science. The effort is part of a broader effort in artificial intelligence at PNNL. The initiative brought together Fallon, an expert on human-machine teaming and human factors research, and Yin, a data scientist and an expert on machine learning.
“This is the type of research needed to prepare and equip an AI-ready workforce,” said Fallon. “If people don’t trust the tool, then you’ve wasted your time and money. You’ve got to know what will happen when you take a machine learning model out of the laboratory and put it to work in the real world.
“I’m a big fan of human expertise and of human-machine teaming. Our EDC scores allow the human to better assess the situation and make the ultimate decision.” More

Top Olympic achievers are awarded the gold medal, a symbol revered for wealth and honor both in the East and the West. This metal also serves as a key element in diverse fields due to its stability in air, exceptional electrical conductivity, and biocompatibility. It’s highly favored in medical and energy sectors as the ‘preferred catalyst’ and is increasingly finding application in cutting-edge wearable technologies.
A research team led by Professor Sei Kwang Hahn and Dr. Tae Yeon Kim from the Department of Materials Science and Engineering at Pohang University of Science and Technology (POSTECH) developed an integrated wearable sensor device that effectively measures and processes two bio-signals simultaneously. Their research findings were featured in Advanced Materials, an international top journal in the materials field.
Wearable devices, available in various forms like attachments and patches, play a pivotal role in detecting physical, chemical, and electrophysiological signals for disease diagnosis and management. Recent strides in research focus on devising wearables capable of measuring multiple bio-signals concurrently. However, a major challenge has been the disparate materials needed for each signal measurement, leading to interface damage, complex fabrication, and reduced device stability. Additionally, these varied signals analysis requires further signal processing systems and algorithms.
The team tackled this challenge using various shapes of gold (Au) nanowires. While silver (Ag) nanowires, known for their extreme thinness, lightness, and conductivity, are commonly used in wearable devices, the team fused them with gold. Initially, they developed bulk gold nanowires by coating the exterior of the silver nanowires, suppressing the galvanic phenomenon. Subsequently, they created hollow gold nanowires by selectively etching the silver from the gold-coated nanowires. The bulk gold nanowires responded sensitively to temperature variations, whereas the hollow gold nanowires showed high sensitivity to minute changes in strain.
These nanowires were then patterned onto a substrate made of styrene-ethylene-butylene-styrene (SEBS) polymer, seamlessly integrated without separations. By leveraging two types of gold nanowires, each with distinct properties, they engineered an integrated sensor capable of measuring both temperature and strain. Additionally, they engineered a logic circuit for signal analysis, utilizing the negative gauge factor resulting from introducing micrometer-scale corrugations into the pattern. This approach led to the successful creation of an intelligent wearable device system that not only captures but also analyzes signals simultaneously, all using a single material of Au.
The team’s sensors exhibited remarkable performance in detecting subtle muscle tremors, identifying heartbeat patterns, recognizing speech through vocal cord tremors, and monitoring changes in body temperature. Notably, these sensors maintained high stability without causing damage to the material interfaces. Their flexibility and excellent stretchability enabled them to conform to curved skin seamlessly.
Professor Sei Kwang Hahn stated, “This research underscores the potential for the development of a futuristic bioelectronics platform capable of analyzing a diverse range of bio-signals.” He added, “We envision new prospects across various industries including healthcare and integrated electronic systems.”
The research was sponsored by the Basic Research Program and the Biomedical Technology Development Program of the National Research Foundation of Korea, and POSCO Holdings.

Top Olympic achievers are awarded the gold medal, a symbol revered for wealth and honor both in the East and the West. This metal also serves as a key element in diverse fields due to its stability in air, exceptional electrical conductivity, and biocompatibility. It’s highly favored in medical and energy sectors as the ‘preferred catalyst’ and is increasingly finding application in cutting-edge wearable technologies.
A research team led by Professor Sei Kwang Hahn and Dr. Tae Yeon Kim from the Department of Materials Science and Engineering at Pohang University of Science and Technology (POSTECH) developed an integrated wearable sensor device that effectively measures and processes two bio-signals simultaneously. Their research findings were featured in Advanced Materials, an international top journal in the materials field.
Wearable devices, available in various forms like attachments and patches, play a pivotal role in detecting physical, chemical, and electrophysiological signals for disease diagnosis and management. Recent strides in research focus on devising wearables capable of measuring multiple bio-signals concurrently. However, a major challenge has been the disparate materials needed for each signal measurement, leading to interface damage, complex fabrication, and reduced device stability. Additionally, these varied signals analysis requires further signal processing systems and algorithms.
The team tackled this challenge using various shapes of gold (Au) nanowires. While silver (Ag) nanowires, known for their extreme thinness, lightness, and conductivity, are commonly used in wearable devices, the team fused them with gold. Initially, they developed bulk gold nanowires by coating the exterior of the silver nanowires, suppressing the galvanic phenomenon. Subsequently, they created hollow gold nanowires by selectively etching the silver from the gold-coated nanowires. The bulk gold nanowires responded sensitively to temperature variations, whereas the hollow gold nanowires showed high sensitivity to minute changes in strain.
These nanowires were then patterned onto a substrate made of styrene-ethylene-butylene-styrene (SEBS) polymer, seamlessly integrated without separations. By leveraging two types of gold nanowires, each with distinct properties, they engineered an integrated sensor capable of measuring both temperature and strain. Additionally, they engineered a logic circuit for signal analysis, utilizing the negative gauge factor resulting from introducing micrometer-scale corrugations into the pattern. This approach led to the successful creation of an intelligent wearable device system that not only captures but also analyzes signals simultaneously, all using a single material of Au.
The team’s sensors exhibited remarkable performance in detecting subtle muscle tremors, identifying heartbeat patterns, recognizing speech through vocal cord tremors, and monitoring changes in body temperature. Notably, these sensors maintained high stability without causing damage to the material interfaces. Their flexibility and excellent stretchability enabled them to conform to curved skin seamlessly.
Professor Sei Kwang Hahn stated, “This research underscores the potential for the development of a futuristic bioelectronics platform capable of analyzing a diverse range of bio-signals.” He added, “We envision new prospects across various industries including healthcare and integrated electronic systems.”
The research was sponsored by the Basic Research Program and the Biomedical Technology Development Program of the National Research Foundation of Korea, and POSCO Holdings. More

A new artificial intelligence (AI) software is now able to decipher difficult-to-read texts on cuneiform tablets. It was developed by a team from Martin Luther University Halle-Wittenberg (MLU), Johannes Gutenberg University Mainz, and Mainz University of Applied Sciences. Instead of photos, the AI system uses 3D models of the tablets, delivering significantly more reliable results than previous methods. This makes it possible to search through the contents of multiple tablets to compare them with each other. It also paves the way for entirely new research questions.
In their new approach, the researchers used 3D models of nearly 2,000 cuneiform tablets, including around 50 from a collection at MLU. According to estimates, around one million such tablets still exist worldwide. Many of them are over 5,000 years old and are thus among humankind’s oldest surviving written records. They cover an extremely wide range of topics: “Everything can be found on them: from shopping lists to court rulings. The tablets provide a glimpse into humankind’s past several millennia ago. However, they are heavily weathered and thus difficult to decipher even for trained eyes,” says Hubert Mara, an assistant professor at MLU.
This is because the cuneiform tablets are unfired chunks of clay into which writing has been pressed. To complicate matters, the writing system back then was very complex and encompassed several languages. Therefore, not only are optimal lighting conditions needed to recognise the symbols correctly, a lot of background knowledge is required as well. “Up until now it has been difficult to access the content of many cuneiform tablets at once — you sort of need to know exactly what you are looking for and where,” Mara adds.
His lab came up with the idea of developing a system of artificial intelligence which is based on 3D models. The new system deciphers characters better than previous methods. In principle, the AI system works along the same lines as OCR software (optical character recognition), which converts the images of writing and text in into machine-readable text. This has many advantages. Once converted into computer text, the writing can be more easily read or searched through. “OCR usually works with photographs or scans. This is no problem for ink on paper or parchment. In the case of cuneiform tablets, however, things are more difficult because the light and the viewing angle greatly influence how well certain characters can be identified,” explains Ernst Stötzner from MLU. He developed the new AI system as part of his master’s thesis under Hubert Mara.
The team trained the new AI software using three-dimensional scans and additional data. Much of this data was provided by Mainz University of Applied Sciences, which is overseeing a large edition project for 3D models of clay tablets. The AI system subsequently did succeed in reliably recognising the symbols on the tablets. “We were surprised to find that our system even works well with photographs, which are actually a poorer source material,” says Stötzner.
The work by the researchers from Halle and Mainz provides new access to what has hitherto been a relatively exclusive material and opens up many new lines of inquiry. Up until now it has only been a prototype which is able to reliably discern symbols from two languages. However, a total of twelve cuneiform languages are known to exist. In the future, the software could also help to decipher weathered inscriptions, for example in cemeteries, which are three-dimensional like the cuneiform script. More

Quantum scientists have discovered a rare phenomenon that could hold the key to creating a ‘perfect switch’ in quantum devices which flips between being an insulator and superconductor.
The research, led by the University of Bristol and published in Science, found these two opposing electronic states exist within purple bronze, a unique one-dimensional metal composed of individual conducting chains of atoms.
Tiny changes in the material, for instance prompted by a small stimulus like heat or light, may trigger an instant transition from an insulating state with zero conductivity to a superconductor with unlimited conductivity, and vice versa. This polarised versatility, known as ’emergent symmetry’, has the potential to offer an ideal On/Off switch in future quantum technology developments.
Lead author Nigel Hussey, Professor of Physics at the University of Bristol, said: “It’s a really exciting discovery which could provide a perfect switch for quantum devices of tomorrow.
“The remarkable journey started 13 years ago in my lab when two PhD students, Xiaofeng Xu and Nick Wakeham, measured the magnetoresistance — the change in resistance caused by a magnetic field — of purple bronze.”
In the absence of a magnetic field, the resistance of purple bronze was highly dependent on the direction in which the electrical current is introduced. Its temperature dependence was also rather complicated. Around room temperature, the resistance is metallic, but as the temperature is lowered, this reverses and the material appears to be turning into an insulator. Then, at the lowest temperatures, the resistance plummets again as it transitions into a superconductor. Despite this complexity, surprisingly, the magnetoresistance was found to be extremely simple. It was essentially the same irrespective of the direction in which the current or field were aligned and followed a perfect linear temperature dependence all the way from room temperature down to the superconducting transition temperature.
“Finding no coherent explanation for this puzzling behaviour, the data lay dormant and published unpublished for the next seven years. A hiatus like this is unusual in quantum research, though the reason for it was not a lack of statistics,” Prof Hussey explained.

“Such simplicity in the magnetic response invariably belies a complex origin and as it turns out, its possible resolution would only come about through a chance encounter.”
In 2017, Prof Hussey was working at Radboud University and saw advertised a seminar by physicist Dr Piotr Chudzinski on the subject of purple bronze. At the time few researchers were devoting an entire seminar to this little-known material, so his interest was piqued.
Prof Hussey said: “In the seminar Chudzinski proposed that the resistive upturn may be caused by interference between the conduction electrons and elusive, composite particles known as ‘dark excitons’. We chatted after the seminar and together proposed an experiment to test his theory. Our subsequent measurements essentially confirmed it.”
Buoyed by this success, Prof Hussey resurrected Xu and Wakeham’s magnetoresistance data and showed them to Dr Chudzinski. The two central features of the data — the linearity with temperature and the independence on the orientation of current and field — intrigued Chudzinski, as did the fact that the material itself could exhibit both insulating and superconducting behaviour depending on how the material was grown.
Dr Chudzinski wondered whether rather than transforming completely into an insulator, the interaction between the charge carriers and the excitons he’d introduced earlier could cause the former to gravitate towards the boundary between the insulating and superconducting states as the temperature is lowered. At the boundary itself, the probability of the system being an insulator or a superconductor is essentially the same.
Prof Hussey said: “Such physical symmetry is an unusual state of affairs and to develop such symmetry in a metal as the temperature is lowered, hence the term ’emergent symmetry’, would constitute a world-first.”
Physicists are well versed in the phenomenon of symmetry breaking: lowering the symmetry of an electron system upon cooling. The complex arrangement of water molecules in an ice crystal is an example of such broken symmetry. But the converse is an extremely rare, if not unique, occurrence. Returning to the water/ice analogy, it is as though upon cooling the ice further, the complexity of the ice crystals ‘melts’ once again into something as symmetric and smooth as the water droplet.

Dr Chudzinski, now a Research Fellow at Queen’s University Belfast, said: “Imagine a magic trick where a dull, distorted figure transforms into a beautiful, perfectly symmetric sphere. This is, in a nutshell, the essence of emergent symmetry. The figure in question is our material, purple bronze, while our magician is nature itself.”
To further test whether the theory held water, an additional 100 individual crystals, some insulating and others superconducting, were investigated by another PhD student, Maarten Berben, working at Radboud University.
Prof Hussey added: “After Maarten’s Herculean effort, the story was complete and the reason why different crystals exhibited such wildly different ground states became apparent. Looking ahead, it might be possible to exploit this ‘edginess’ to create switches in quantum circuits whereby tiny stimuli induce profound, orders-of-magnitude changes in the switch resistance.” More

As possibilities have changed and technology has advanced, memories and nostalgia are now a significant part of our use of social media. This is shown in a study from the University of Gothenburg and University West.
Researchers at the University of Gothenburg and University West have been following a group of eleven active social media users for ten years, allowing them to describe and reflect on how they use the platforms to document and share their lives. The study provides insight into the role of technology in creating experiences and reliving meaningful moments.
“These types of studies help us look back and understand the culture as it was in the 2010s and 2020s when social media was a central part of it,” says Beata Jungselius, senior lecturer of informatics at University West and one of the researchers behind the study.
Social media users engage in what researchers define as “social media nostalgizing,” meaning they actively seek out content that evokes feelings of nostalgia.
Alexandra Weilenmann, professor of interaction design at the University of Gothenburg, explains that participants in the study have described it as “treating themselves” to a nostalgia trip now and then.
“Going back and remembering what has happened earlier in life becomes a bigger part of it over time than posting new content,” she says, and explains that in later interviews, it becomes clear that the platforms often serve as diary-like tools that allow memories to be relived.
Social media platforms are introducing increasingly advanced features to help users interact with older content. Personal, music-infused photo albums generated for us or reminders of pictures we posted on the same date one, three, or ten years ago allow for nostalgic experiences, which are often seen as positive. The study describes how these features can lead to users reconnecting with old friends by “tagging” them in a shared memory. Alexandra Weilenmann and Beata Jungselius believe this could be a deliberate move by social media platforms to encourage users to stay active since the publication of new content has decreased.
The researchers have noted that it’s not just the content itself that evokes feelings of nostalgia but also memories of the actual usage of social media play a significant role. For example, one of the interviewees reminisces about how rewarding the intense communication in forums was and how it often led to real-life meetings and interactions.
“It’s only now that we’ve lived with social media long enough to make and draw conclusions from a study like this. Through our method of studying the same users over ten years, we’ve been able to follow how their usage and attitudes toward the platforms have changed as they have evolved,” says Beata Jungselius. More

Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called “active matter.” The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.
Scientists from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden, the Center for Systems Biology Dresden (CSBD), and the TU Dresden have now developed an algorithm, implemented in an open-source supercomputer code, that can for the first time solve the equations of active matter theory in realistic scenarios. These solutions bring us a big step closer to solving the century-old riddle of how cells and tissues attain their shape and to designing artificial biological machines.
Biological processes and behaviors are often very complex. Physical theories provide a precise and quantitative framework for understanding them. The active matter theory offers a framework to understand and describe the behavior of active matter — materials composed of individual components capable of converting a chemical fuel (“food”) into mechanical forces. Several scientists from Dresden were key in developing this theory, among others Frank Jülicher, director at the Max Planck Institute for the Physics of Complex Systems, and Stephan Grill, director at the MPI-CBG. With these principles of physics, the dynamics of active living matter can be described and predicted by mathematical equations. However, these equations are extremely complex and hard to solve. Therefore, scientists require the power of supercomputers to comprehend and analyze living materials. There are different ways to predict the behavior of active matter, with some focusing on the tiny individual particles, others studying active matter at the molecular level, and yet others studying active fluids on a large scale. These studies help scientists see how active matter behaves at different scales in space and over time.
Solving complex mathematical equations
Scientists from the research group of Ivo Sbalzarini, TU Dresden Professor at the Center for Systems Biology Dresden (CSBD), research group leader at the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), and Dean of the Faculty of Computer Science at TU Dresden, have now developed a computer algorithm to solve the equations of active matter. Their work was published in the journal “Physics of Fluids” and was featured on the cover. They present an algorithm that can solve the complex equations of active matter in three dimensions and in complex-shaped spaces. “Our approach can handle different shapes in three dimensions over time,” says one of the first authors of the study, Abhinav Singh, a studied mathematician. He continues, “Even when the data points are not regularly distributed, our algorithm employs a novel numerical approach that works seamlessly for complex biologically realistic scenarios to accurately solve the theory’s equations. Using our approach, we can finally understand the long-term behavior of active materials in both moving and non-moving scenarios for predicting their dynamics. Further, the theory and simulations could be used to program biological materials or create engines at the nano-scale to extract useful work.” The other first author, Philipp Suhrcke, a graduate of TU Dresden’s Computational Modeling and Simulation M.Sc. program, adds, “thanks to our work, scientists can now, for example, predict the shape of a tissue or when a biological material is going to become unstable or dysregulated, with far-reaching implications in understanding the mechanisms of growth and disease.”
A powerful code for everyone to use
The scientists implemented their software using the open-source library OpenFPM, meaning that it is freely available for others to use. OpenFPM is developed by the Sbalzarini group for democratizing large-scale scientific computing. The authors first developed a custom computer language that allows computational scientists to write supercomputer codes by specifying the equations in mathematical notation and let the computer do the work to create a correct program code. As a result, they do not have to start from scratch every time they write a code, effectively reducing code development times in scientific research from months or years to days or weeks, providing enormous productivity gains. Due to the tremendous computational demands of studying three-dimensional active materials, the new code is scalable on shared and distributed-memory multi-processor parallel supercomputers, thanks to the use of OpenFPM. Although the application is designed to run on powerful supercomputers, it can also run on regular office computers for studying two-dimensional materials.

The Principal Investigator of the study, Ivo Sbalzarini, summarizes: “Ten years of our research went into creating this simulation framework and enhancing the productivity of computational science. This now all comes together in a tool for understanding the three-dimensional behavior of living materials. Open-source, scalable, and capable of handling complex scenarios, our code opens new avenues for modeling active materials. This may finally lead us to understand how cells and tissues attain their shape, addressing the fundamental question of morphogenesis that has puzzled scientist for centuries. But it may also help us design artificial biological machines with minimal numbers of components.”
The computer code that support the findings of this study are openly available in the 3Dactive-hydrodynamics github repository located at https://github.com/mosaic-group/3Dactive-hydrodynamics
The open source framework OpenFPM is available at https://github.com/mosaic-group/openfpm_pdata
Related Publications for the embedded computer language and the OpenFPM software library: https://doi.org/10.1016/j.cpc.2019.03.007 and https://doi.org/10.1140/epje/s10189-021-00121-x More

A novel technology to manage demands on mobile networks from multiple users using Terahertz frequencies has been developed by University of Leicester computer scientists.
As we see an explosion of devices joining the ‘internet of things’, this solution could not only improve speed and power consumption for users of mobile devices, but could also help reap the benefits from the next generation of mobile technologies, 6G.
They have detailed the technology in a new study in IEEE Transactions on Communications.
Demands on the UK’s mobile telecommunications network are growing, with Mobile UK estimating that twenty-five million devices are connected to mobile networks, a number expected to rise to thirty billion by 2030. As the ‘internet of things’ grows, more and more technology will be competing for access to those networks.
State-of-the-art telecommunication technologies have been established for current applications in 5G, but with increasing demands of more users and devices, these systems demonstrate slower connections and costly energy consumption. These systems suffer from the self-interference problem that severely affects communication quality and efficiency. To deal with these challenges, a technique known as multicarrier-division duplex (MDD) has been recently proposed and studied, which allows a receiver in the network to be nearly free of self-interference in the digital domain by relying only on the fast Fourier transform (FFT) processing.
This project proposed a novel technology to optimise the assignment of subcarrier set and the number of access point clusters and improve the communication quality in different networks. The team tested their technology in a simulation based on a real-world industrial setting, finding that it out-performed existing technologies. A 10% power consumption reduction can be achieved, compared to other state of the art technologies.
Lead Principal Investigator Professor Huiyu Zhou from the University of Leicester School of Computing and Mathematical Sciences said: “With our proposed technology, 5G/6G systems require less energy consumption, have faster device selection and less resource allocation. Users may feel their mobile communication is quicker, wider and with reduced power demands.
“The University of Leicester is leading the development of AI solutions for device selection and access point clustering. AI technologies, reinforcement learning in particular, help us to search for the best parameters used in the proposed wireless communication systems quickly and effectively. This helps to save power, resources and human labour. Without using AI technologies, we will spend much more time on rendering the best parameters for system set-up and device selection in the network.”
The team is now continuing work on the optimising the proposed technologies and reducing the computational complexity of the technique. The source code of the proposed method has been published and shared with the entire world for promoting the research.
The study forms part of the EU-funded 6G BRAINS project, which will develop an AI-driven self-learning platform to intelligently and dynamically allocate resources, enhancing capacity and reliability, and improving positioning accuracy while decreasing latency of response for future industrial applications of massive scale and varying demands. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017226. More

The remarkable proton and oxide-ion (dual-ion) conductivities of hexagonal perovskite-related oxide Ba7Nb3.8Mo1.2O20.1 are promising for next-generation electrochemical devices, as reported by scientists at Tokyo Tech. The unique ion-transport mechanisms they unveiled will hopefully pave the way for better dual-ion conductors, which could play an essential role in tomorrow’s clean energy technologies.
Clean energy technologies are the cornerstone of sustainable societies, and solid-oxide fuel cells (SOFCs) and proton ceramic fuel cells (PCFCs) are among the most promising types of electrochemical devices for green power generation. These devices, however, still face challenges that hinder their development and adoption.
Ideally, SOFCs should be operated at low temperatures to prevent unwanted chemical reactions from degrading their constituent materials. Unfortunately, most known oxide-ion conductors, a key component of SOFCs, only exhibit decent ionic conductivity at elevated temperatures. As for PCFCs, not only are they chemically unstable under carbon dioxide atmospheres, but they also require energy-intensive, high-temperature processing steps during manufacture.
Fortunately, there is a type of material that can solve these problems by combining the benefits of both SOFCs and PCFCs: dual-ion conductors. By supporting the diffusion of both protons and oxide ions, dual-ion conductors can realize high total conductivity at lower temperatures and improve the performance of electrochemical devices. Although some perovskite-related dual-ion conducting materials such as Ba7Nb4MoO20 have been reported, their conductivities are not high enough for practical applications, and their underlying conducting mechanisms are not well understood.
Against this backdrop, a research team led by Professor Masatomo Yashima from Tokyo Institute of Technology, Japan, decided to investigate the conductivity of materials similar to 7Nb4MoO20 but with a higher Mo fraction (that is, Ba7Nb4-xMo1+xO20+x/2). Their latest study, which was conducted in collaboration with the Australian Nuclear Science and Technology Organisation (ANSTO), the High Energy Accelerator Research Organization (KEK), and Tohoku University, was published in Chemistry of Materials.
After screening various Ba7Nb4-xMo1+xO20+x/2 compositions, the team found that Ba7Nb3.8Mo1.2O20.1 had remarkable proton and oxide-ion conductivities. “Ba7Nb3.8Mo1.2O20.1 exhibited bulk conductivities of 11 mS/cm at 537 ℃ under wet air and 10 mS/cm at 593 ℃ under dry air. Total direct current conductivity at 400 ℃ in wet air of Ba7Nb3.8Mo1.2O20.1 was 13 times higher than that of Ba7Nb4MoO20, and the bulk conductivity in dry air at 306 ℃ is 175 times higher than that of the conventional yttria-stabilized zirconia (YSZ),” highlights Prof. Yashima.
Next, the researchers sought to shed light on the underlying mechanisms behind these high conductivity values. To this end, they conducted ab initio molecular dynamics (AIMD) simulations, neutron diffraction experiments, and neutron scattering length density analyses. These techniques enabled them to study the structure of Ba7Nb3.8Mo1.2O20.1 in greater detail and determine what makes it special as a dual-ion conductor.
Interestingly, the team found that the high oxide-ion conductivity of Ba7Nb3.8Mo1.2O20.1 originates from a unique phenomenon. It turns out that adjacent MO5 monomers in Ba7Nb3.8Mo1.2O20.1 can form M2O9 dimers by sharing an oxygen atom on one of their corners (M = Nb or Mo cation). The breaking and reforming of these dimers gives rise to ultrafast oxide-ion movement in a manner analogous to a long line of people relaying buckets of water (oxide ions) from one person to the next. Furthermore, the AIMD simulations revealed that the observed high proton conduction was due to efficient proton migration in the hexagonal close-packed BaO3 layers in the material.
Taken together, the results of this study highlight the potential of perovskite-related dual-ion conductors and could serve as guidelines for the rational design of these materials. “The present findings of high conductivities and unique ion migration mechanisms in Ba7Nb3.8Mo1.2O20.1 will help the development of science and engineering of oxide-ion, proton, and dual-ion conductors,” concludes a hopeful Prof. Yashima. More