Aurelia Butler

Significantly improved electric vehicle (EV) batteries could be a step closer thanks to a new study led by University of Oxford researchers, published today in Nature. Using advanced imaging techniques, this revealed mechanisms which cause lithium metal solid-state batteries (Li-SSBs) to fail. If these can be overcome, solid-state batteries using lithium metal anodes could deliver a step-change improvement in EV battery range, safety and performance, and help advance electrically powered aviation.
One of the co-lead authors of the study Dominic Melvin, a PhD student in the University of Oxford’s Department of Materials, said: ‘Progressing solid-state batteries with lithium metal anodes is one of the most important challenges facing the advancement of battery technologies. While lithium-ion batteries of today will continue to improve, research into solid-state batteries has the potential to be high-reward and a gamechanger technology.’
Li-SSBs are distinct from other batteries because they replace the flammable liquid electrolyte in conventional batteries with a solid electrolyte and use lithium metal as the anode (negative electrode). The use of the solid electrolyte improves the safety, and the use of lithium metal means more energy can be stored. A critical challenge with Li-SSBs, however, is that they are prone to short circuit when charging due to the growth of ‘dendrites’: filaments of lithium metal that crack through the ceramic electrolyte. As part of the Faraday Institution’s SOLBAT project, researchers from the University of Oxford’s Departments of Materials, Chemistry and Engineering Science, have led a series of in-depth investigations to understand more about how this short-circuiting happens.
In this latest study, the group used an advanced imaging technique called X-ray computed tomography at Diamond Light Source to visualise dendrite failure in unprecedented detail during the charging process. The new imaging study revealed that the initiation and propagation of the dendrite cracks are separate processes, driven by distinct underlying mechanisms. Dendrite cracks initiate when lithium accumulates in sub-surface pores. When the pores become full, further charging of the battery increases the pressure, leading to cracking. In contrast, propagation occurs with lithium only partially filling the crack, through a wedge-opening mechanism which drives the crack open from the rear.
This new understanding points the way forward to overcoming the technological challenges of Li-SSBs. Dominic Melvin said: ‘For instance, while pressure at the lithium anode can be good to avoid gaps developing at the interface with the solid electrolyte on discharge, our results demonstrate that too much pressure can be detrimental, making dendrite propagation and short-circuit on charging more likely.’
Sir Peter Bruce, Wolfson Chair, Professor of Materials at the University of Oxford, Chief Scientist of the Faraday Institution, and corresponding author of the study, said: ‘The process by which a soft metal such as lithium can penetrate a highly dense hard ceramic electrolyte has proved challenging to understand with many important contributions by excellent scientists around the world. We hope the additional insights we have gained will help the progress of solid-state battery research towards a practical device.’
According to a recent report by the Faraday Institution, SSBs may satisfy 50% of global demand for batteries in consumer electronics, 30% in transportation, and over 10% in aircraft by 2040.
Professor Pam Thomas, CEO, Faraday Institution, said: ‘SOLBAT researchers continue to develop a mechanistic understanding of solid-state battery failure — one hurdle that needs to be overcome before high-power batteries with commercially relevant performance could be realised for automotive applications. The project is informing strategies that cell manufacturers might use to avoid cell failure for this technology. This application-inspired research is a prime example of the type of scientific advances that the Faraday Institution was set up to drive.’ More

The debut of artificial intelligence chatbot ChatGPT has set the world abuzz with its ability to churn out human-like text and conversations. Still, many telltale signs can help us distinguish AI chatbots from humans, according to a study published on June 7 in the journal Cell Reports Physical Science. Based on the signs, the researchers developed a tool to identify AI-generated academic science writing with over 99% accuracy.
“We tried hard to create an accessible method so that with little guidance, even high school students could build an AI detector for different types of writing,” says first author Heather Desaire, a professor at the University of Kansas. “There is a need to address AI writing, and people don’t need a computer science degree to contribute to this field.”
“Right now, there are some pretty glaring problems with AI writing,” says Desaire. “One of the biggest problems is that it assembles text from many sources and there isn’t any kind of accuracy check — it’s kind of like the game Two Truths and a Lie.”
Although many AI text detectors are available online and perform fairly well, they weren’t built specifically for academic writing. To fill the gap, the team aimed to build a tool with better performance precisely for this purpose. They focused on a type of article called perspectives, which provide an overview of specific research topics written by scientists. The team selected 64 perspectives and created 128 ChatGPT-generated articles on the same research topics to train the model. When they compared the articles, they found an indicator of AI writing — predictability.
Contrary to AI, humans have more complex paragraph structures, varying in the number of sentences and total words per paragraph, as well as fluctuating sentence length. Preferences in punctuation marks and vocabulary are also a giveaway. For example, scientists gravitate towards words like “however,” “but” and “although,” while ChatGPT often uses “others” and “researchers” in writing. The team tallied 20 characteristics for the model to look out for.
When tested, the model aced a 100% accuracy rate at weeding out AI-generated full perspective articles from those written by humans. For identifying individual paragraphs within the article, the model had an accuracy rate of 92%. The research team’s model also outperformed an available AI text detector on the market by a wide margin on similar tests.
Next, the team plans to determine the scope of the model’s applicability. They want to test it on more extensive datasets and across different types of academic science writing. As AI chatbots advance and become more sophisticated, the researchers also want to know if their model will stand.
“The first thing people want to know when they hear about the research is ‘Can I use this to tell if my students actually wrote their paper?'” said Desaire. While the model is highly skilled at distinguishing between AI and scientists, Desaire says it was not designed to catch AI-generated student essays for educators. However, she notes that people can easily replicate their methods to build models for their own purposes. More

An international research team led by the Hong Kong University of Science and Technology (HKUST) has developed an artificial intelligence (AI)-based model that uses genetic information to predict an individual’s risk of developing Alzheimer’s disease (AD) well before symptoms occur. This groundbreaking study paves the way for using deep learning methods to predict the risks of diseases and uncover their molecular mechanisms; this could revolutionize the diagnosis of, interventions for, and clinical research on AD and other common diseases such as cardiovascular diseases.
Researchers led by HKUST’s President, Prof. Nancy IP, in collaboration with the Chair Professor and Director of HKUST’s Big Data Institute, Prof. CHEN Lei, investigated whether AI — specifically deep learning models — can model AD risk using genetic information. The team established one of the first deep learning models for estimating AD polygenic risks in both European-descent and Chinese populations. Compared to other models, these deep learning models more accurately classify patients with AD and stratify individuals into distinct groups based on disease risks associated with alterations of various biological processes.
In current daily practice, AD is diagnosed clinically, using various means including cognitive tests and brain imaging, but often when patients are showing symptoms, it is already well past the optimal intervention window. Therefore, early forecasting of AD risk can greatly aid diagnosis and the development of intervention strategies. By combining the new deep learning model with genetic testing, an individual’s lifetime risk of developing AD can be estimated with more than 70% accuracy.
AD is a heritable disorder that can be attributed to genomic variants. As these variants are present from birth and remain constant throughout life, examining an individual’s DNA information can help predict their relative risk of developing AD, thereby enabling early intervention and timely management. While FDA-approved genetic testing for the APOE-?4 genetic variant can estimate AD risk, it may be insufficient to identify high-risk individuals, because multiple genetic risks contribute to the disease. Therefore, it is essential to develop tests that integrate information from multiple AD risk genes to accurately determine an individual’s relative risk of developing AD over their lifetime.
“Our study demonstrates the efficacy of deep learning methods for genetic research and risk prediction for Alzheimer’s disease. This breakthrough will greatly accelerate population-scale screening and staging of Alzheimer’s disease risk. Besides risk prediction, this approach supports the grouping of individuals according to their disease risk and provides insights into the mechanisms that contribute to the onset and progression of the disease,” said Prof. Nancy Ip.
Meanwhile, Prof. Chen Lei remarked that, “this study exemplifies how the application of AI to the biological sciences can significantly benefit biomedical and disease-related studies. By utilizing a neural network, we effectively captured nonlinearity in high-dimensional genomic data, which improved the accuracy of Alzheimer’s disease risk prediction. In addition, through AI-based data analysis without human supervision, we categorized at-risk individuals into subgroups, which revealed insights into the underlying disease mechanisms. Our research also highlights how AI can elegantly, efficiently, and effectively address interdisciplinary challenges. I firmly believe that AI will play a vital role in various healthcare fields in the near future.”
The study was conducted in collaboration with researchers at the Shenzhen Institute of Advanced Technology and University College London as well as clinicians at local Hong Kong hospitals including Prince of Wales Hospital and Queen Elizabeth Hospital. The findings were recently published in Communications Medicine. The research team is now refining the model and aims to ultimately incorporate it into standard screening workflows.
AD, which affects over 50 million people worldwide, is a fatal disease that involves cognitive dysfunction and the loss of brain cells. Its symptoms include progressive memory loss as well as impaired movement, reasoning, and judgment. More

Researchers from University of St. Gallen and Columbia Business School published a new Journal of Marketing article that examines how the perceived meaning of manual labor can help predict the adoption of autonomous products.
The study, forthcoming in the Journal of Marketing, is titled “Meaning of Manual Labor Impedes Consumer Adoption of Autonomous Products” and is authored by Emanuel de Bellis, Gita Venkataramani Johar, and Nicola Poletti.
Whether it is cleaning homes or mowing lawns, consumers increasingly delegate manual tasks to autonomous products. These gadgets operate without human oversight and free consumers from mundane chores. However, anecdotal evidence suggests that people feel a sense of satisfaction when they complete household chores. Are autonomous products such as robot vacuums and cooking machines depriving consumers from meaningful experiences?
This new research shows that, despite unquestionable benefits such as gains in efficiency and convenience, autonomous products strip away a source of meaning in life. As a result, consumers are hesitant to buy these products.
The researchers argue that manual labor is an important source of meaning in life. This is in line with research showing that everyday tasks have value — chores such as cleaning may not make us happy, but they add meaning to our lives. As de Bellis explains, “Our studies show that ‘meaning of manual labor’ causes consumers to reject autonomous products. For example, these consumers have a more negative attitude toward autonomous products and are also more prone to believe in the disadvantages of autonomous products relative to their advantages.”
Highlight Saving Time for Other Meaningful Tasks
On one hand, autonomous products take over tasks from consumers, typically leading to a reduction in manual labor and hence in the ability to derive meaning from manual tasks. On the other hand, by taking over manual tasks, autonomous products provide consumers with the opportunity to spend time on other, potentially more meaningful, tasks and activities. “We suggest that companies highlight so-called alternative sources of meaning in life, which should reduce consumers’ need to derive meaning specifically from manual tasks. Highlighting other sources of meaning, such as through family or hobbies, at the time of the adoption decision should counteract the negative effect on autonomous product adoption,” says Johar.
In fact, a key value proposition for many of these technologies is that they free up time. iRobot claims that its robotic vacuum cleaner Roomba saves owners as much as 110 hours of cleaning a year. Some companies go even a step further by suggesting what consumers could do with their freed-up time. For example, German home appliance company Vorwerk promotes its cooking machine Thermomix with “more family time” and “Thermomix does the work so you can make time for what matters most.” Instead of promoting the quality of task completion (i.e., cooking a delicious meal), the company emphasizes that consumers can spend time on other, arguably more meaningful, activities.
This study demonstrates that the perceived meaning of manual labor (MML) — a novel concept introduced by the researchers — is key to predicting the adoption of autonomous products. Poletti says that “Consumers with a high MML tend to resist the delegation of manual tasks to autonomous products, irrespective of whether these tasks are central to one’s identity or not. Marketers can start by segmenting consumers into high and low MML consumers.” Unlike other personality variables that can only be reliably measured using complex psychometric scales, the extent of consumers’ MML might be assessed simply by observing their behavioral characteristics, such as whether consumers tend to do the dishes by hand, whether they prefer a manual car transmission, or what type of activities and hobbies they pursue. Activities like woodworking, cookery, painting, and fishing are likely predictors of high MML. Similarly, companies can measure likes on social media for specific activities and hobbies that involve manual labor. Finally, practitioners can ask consumers to rate the degree to which manual versus cognitive tasks are meaningful to them. Having segmented consumers according to their MML, marketers can better target and focus their messages and efforts.
In promotions, firms can highlight the meaningful time consumers gain with the use of autonomous products (e.g., “this product allows you to spend time on more meaningful tasks and pursuits than cleaning”). Such an intervention can prevent the detrimental effects of meaning of manual labor on autonomous product adoption. More

A simple sponge has improved how robots grasp, scientists from the University of Bristol have found.
This easy-to-make sponge-jamming device can help stiff robots handle delicate items carefully by mimicking the nuanced touch, or variable stiffness, of a human.
Robots can skip, jump and do somersaults, but they’re too rigid to hold an egg easily. Variable-stiffness devices are potential solutions for contact compliance on hard robots to reduce damage, or for improving the load capacity of soft robots.
This study, published at the IEEE International Conference on Robotics and Automation (ICRA) 2023, shows that variable stiffness can be achieved by a silicone sponge.
Lead author Tianqi Yue from Bristol’s Department of Engineering Mathematics explained: “Stiffness, also known as softness, is important in contact scenarios.
“Robotic arms are too rigid so they cannot make such a soft human-like grasp on delicate objects, for example, an egg.

“What makes humans different from robotic arms is that we have soft tissues enclosing rigid bones, which act as a natural mitigating mechanism.
“In this paper, we managed to develop a soft device with variable stiffness, to be mounted on the end robotic arm for making the robot-object contact safe.”
Silicone sponge is a cheap and easy-to-fabricate material. It is a porous elastomer just like the cleaning sponge used in everyday tasks.
By squeezing the sponge, the sponge stiffens which is why it can be transformed into a variable-stiffness device.
This device could be used in industrial robots in scenarios including gripping jellies, eggs and other fragile substances. It can also be used in service robots to make human-robot interaction safer.
Mr Yue added: “We managed to use a sponge to make a cheap and nimble but effective device that can help robots achieve soft contact with objects. The great potential comes from its low cost and light weight.
“We believe this silicone-sponge based variable-stiffness device will provide a novel solution in industry and healthcare, for example, tunable-stiffness requirement on robotic polishing and ultrasound imaging.”
The team will now look at making the device achieve variable stiffness in multiple directions, including rotation. More

A University of Minnesota Twin Cities-led team has developed a new superconducting diode, a key component in electronic devices, that could help scale up quantum computers for industry use and improve the performance of artificial intelligence systems. Compared to other superconducting diodes, the researchers’ device is more energy efficient; can process multiple electrical signals at a time; and contains a series of gates to control the flow of energy, a feature that has never before been integrated into a superconducting diode.
The paper is published in Nature Communications, a peer-reviewed scientific journal that covers the natural sciences and engineering.
A diode allows current to flow one way but not the other in an electrical circuit. It’s essentially half of a transistor, the main element in computer chips. Diodes are typically made with semiconductors, but researchers are interested in making them with superconductors, which have the ability to transfer energy without losing any power along the way.
“We want to make computers more powerful, but there are some hard limits we are going to hit soon with our current materials and fabrication methods,” said Vlad Pribiag, senior author of the paper and an associate professor in the University of Minnesota School of Physics and Astronomy. “We need new ways to develop computers, and one of the biggest challenges for increasing computing power right now is that they dissipate so much energy. So, we’re thinking of ways that superconducting technologies might help with that.”
The University of Minnesota researchers created the device using three Josephson junctions, which are made by sandwiching pieces of non-superconducting material between superconductors. In this case, the researchers connected the superconductors with layers of semiconductors. The device’s unique design allows the researchers to use voltage to control the behavior of the device.
Their device also has the ability to process multiple signal inputs, whereas typical diodes can only handle one input and one output. This feature could have applications in neuromorphic computing, a method of engineering electrical circuits to mimic the way neurons function in the brain to enhance the performance of artificial intelligence systems.
“The device we’ve made has close to the highest energy efficiency that has ever been shown, and for the first time, we’ve shown that you can add gates and apply electric fields to tune this effect,” explained Mohit Gupta, first author of the paper and a Ph.D. student in the University of Minnesota School of Physics and Astronomy. “Other researchers have made superconducting devices before, but the materials they’ve used have been very difficult to fabricate. Our design uses materials that are more industry-friendly and deliver new functionalities.”
The method the researchers used can, in principle, be used with any type of superconductor, making it more versatile and easier to use than other techniques in the field. Because of these qualities, their device is more compatible for industry applications and could help scale up the development of quantum computers for wider use.
“Right now, all the quantum computing machines out there are very basic relative to the needs of real-world applications,” Pribiag said. “Scaling up is necessary in order to have a computer that’s powerful enough to tackle useful, complex problems. A lot of people are researching algorithms and usage cases for computers or AI machines that could potentially outperform classical computers. Here, we’re developing the hardware that could enable quantum computers to implement these algorithms. This shows the power of universities seeding these ideas that eventually make their way to industry and are integrated into practical machines.”
This research was funded primarily by the United States Department of Energy with partial support from Microsoft Research and the National Science Foundation.
In addition to Pribiag and Gupta, the research team included University of Minnesota School of Physics and Astronomy graduate student Gino Graziano and University of California, Santa Barbara researchers Mihir Pendharkar, Jason Dong, Connor Dempsey, and Chris Palmstrøm. More

Researchers have developed a new artificial intelligence (AI) framework that is better than previous technologies at analyzing and categorizing dialogue between individuals, with the goal of improving team training technologies. The framework will enable training technologies to better understand how well individuals are coordinating with one another and working as part of a team.
“There is a great deal of interest in developing AI-powered training technologies that can understand teamwork dynamics and modify their training to foster improved collaboration among team members,” says Wookhee Min, co-author of a paper on the work and a research scientist at North Carolina State University. “However, previous AI architectures have struggled to accurately assess the content of what team members are sharing with each other when they communicate.”
“We’ve developed a new framework that significantly improves the ability of AI to analyze communication between team members,” says Jay Pande, first author of the paper and a Ph.D. student at NC State. “This is a significant step forward for the development of adaptive training technologies that aim to facilitate effective team communication and collaboration.”
The new AI framework builds on a powerful deep learning model that was trained on a large, text-based language dataset. This model, called the Text-to-Text Transfer Transformer (T5), was then customized using data collected during squad-level training exercises conducted by the U.S. Army.
“We modified the T5 model to use contextual features of the team — such as the speaker’s role — to more accurately analyze team communication,” Min says. “That context can be important. For example, something a team leader says may need to be viewed differently than something another team member says.”
To test the performance of the new framework, the researchers compared it to two previous AI technologies. Specifically, the researchers tested the ability of all three AI technologies to understand the dialogue within a squad of six soldiers during a training exercise.

The AI framework was tasked with two things: classify what sort of dialogue was taking place, and follow the flow of information within the squad. Classifying the dialogue refers to determining the purpose of what was being said. For example, was someone requesting information, providing information, or issuing a command? Following the flow of information refers to how information was being shared within the team. For example, was information being passed up or down the chain of command?
“We found that the new framework performed substantially better than the previous AI technologies,” Pande says.
“One of the things that was particularly promising was that we trained our framework using data from one training mission, but tested the model’s performance using data from a different training mission,” Min says. “And the boost in performance over the previous AI models was notable — even though we were testing the model in a new set of circumstances.”
The researchers also note that they were able to achieve these results using a relatively small version of the T5 model. That’s important, because it means that they can get analysis in fractions of a second without a supercomputer.
“One next step for this work includes exploring the extent to which the new framework can be applied to a variety of other training scenarios,” Pande says.
“We tested the new framework with training data that was transcribed from audio files into text by humans,” Min says. “Another next step will involve integrating the framework with an AI model that transcribes audio data into text, so that we can assess the ability of this technology to analyze team communication data in real time. This will likely involve improving the framework’s ability to deal with noises and errors as the AI transcribes audio data.”
The paper, “Robust Team Communication Analytics with Transformer-Based Dialogue Modeling,” will be presented at the 24th International Conference on Artificial Intelligence in Education (AIED 2023), which will be held July 3-7 in Tokyo, Japan. The paper was co-authored by Jason Saville, a former graduate student at NC State; James Lester, the Goodnight Distinguished University Professor in Artificial Intelligence and Machine Learning at NC State; and Randall Spain of the U.S. Army Combat Capabilities Development Command (DEVCOM). Soldier Center.
This research was sponsored by the U.S. Army DEVCOM, Soldier Center under cooperative agreement W912CG-19-2-0001. More

A research team co-led by researchers from City University of Hong Kong (CityU) recently invented a novel, wireless, skin-interfaced olfactory feedback system that can release various odours with miniaturised odour generators (OGs). The new technology integrates odours into virtual reality (VR)/augmented reality (AR) to provide a more immersive experience, with broad applications ranging from 4D movie watching and medical treatment to online teaching.
“Recent human machine interfaces highlight the importance of human sensation feedback, including vision, audio and haptics, associated with wide applications in entertainment, medical treatment and VR/AR. Olfaction also plays a significant role in human perceptual experiences,” said Dr Yu Xinge, Associate Professor in the Department of Biomedical Engineering at CityU, who co-led the study. “However, the current olfaction-generating technologies are associated mainly with big instruments to generate odours in a closed area or room, or an in-built bulky VR set.”
In view of this, Dr Yu and his collaborators from Beihang University developed a new-generation, wearable, olfaction feedback system with wireless, programmable capabilities based on arrays of flexible and miniaturised odour generators.
They created two designs to release odours on demand through the new olfaction feedback devices, which are made of soft, miniaturised, lightweight substrates. The first one is a small, skin-integrated, patch-like device comprising two OGs, which can be directly mounted on the human upper lip. With an extremely short distance between the OGs and the user’s nose, it can provide an ultra-fast olfaction response. Another design is a flexible facemask design with nine OGs of different odour types, which can provide hundreds of odour combinations.
The magic of odour generators is based on a subtle heating platform and a mechanical thermal actuator. By heating and melting odorous paraffin wax on OGs to cause phase change, different odours of adjustable concentration are released. To stop the odour, the odour generators can cool down the temperature of the wax by controlling the motion of the thermal actuator.
By using different paraffin waxes, the research team was able to make about 30 different scents in total, from herbal rosemary and fruity pineapple to sweet baked pancakes. Even less-than-pleasant scents, like stinky durian, can be created. The 11 volunteers were able to recognise the scents generated by the OGs with an average success rate of 93 percent.

The new system supports long-term utilisation without frequent replacement and maintenance, and enables interaction with users for various applications. Most importantly, the olfactory interface can support wireless and programmable operation, and can interact with users in various applications. It can respond rapidly to burst or suppress odours and for accurate odour concentration control. And the odour sources are easily accessible and biocompatible.
In their experiments, demonstrations in 4D movie watching, medical treatment, human emotion control and VR/AR experience in online teaching exhibited the great potential of the new olfaction interfaces in various applications.
For instance, the new wireless olfaction system can interact between the user and a virtual subject when the user is “walking” in a virtual garden by releasing various fruit fragrances. The new technology also showed potential for helping amnesic patients recall lost memories, as odour perception is modulated by experience, leading to the recall of emotional memories.
“The new olfaction systems provide a new alternative option for users to realise the olfaction display in a virtual environment. The fast response rate in releasing odours, the high odour generator integration density, and two wearable designs ensure great potential for olfaction interfaces in various applications, ranging from entertainment and education to healthcare and human machine interfaces,” said Dr Yu.
In the next step, he and his research team will focus on developing a next-generation olfaction system with a shorter response time, smaller size, and higher integration density for VR, AR and mixed reality (MR) applications.
The findings were published in the scientific journal Nature Communications under the title “Soft, Miniaturized, Wireless Olfactory Interface for Virtual Reality”.
The corresponding authors are Dr Yu and Dr Li Yuhang from the Institute of Solid Mechanics at Beihang University. The first co-authors are Dr Liu Yiming, a postdoc on Dr Yu’s research team, Mr Yiu Chunki and Mr Wooyoung Park, PhD students supervised by Dr Yu, and Dr Zhao Zhao, a postdoc on Dr Li’s research team.
The research was supported mainly by the National Natural Science Foundation of China, CityU, and the Research Grants Council of the HKSAR. More