Aurelia Butler

Lithium metal batteries are among the most promising candidates of the next generation of high-energy batteries. They can store at least twice as much energy per unit of volume as the lithium-ion batteries that are in widespread use today. This will mean, for example, that an electric car can travel twice as far on a single charge, or that a smartphone will not have to be recharged so often.
At present, there is still one crucial drawback with lithium metal batteries: the liquid electrolyte requires the addition of significant amounts of fluorinated solvents and fluorinated salts, which increases its environmental footprint. Without the addition of fluorine, however, lithium metal batteries would be unstable, they would stop working after very few charging cycles and be prone to short circuits as well as overheating and igniting. A research group led by Maria Lukatskaya, Professor of Electrochemical Energy Systems at ETH Zurich, has now developed a new method that dramatically reduces the amount of fluorine required in lithium metal batteries, thereby rendering them more environmentally friendly and more stable as well as cost-effective.
A stable protective layer increases battery safety and efficiency
The fluorinated compounds from electrolyte help the formation of a protective layer around the metallic lithium at the negative electrode of the battery. “This protective layer can be compared to the enamel of a tooth,” Lukatskaya explains. “It protects the metallic lithium from continuous reaction with electrolyte components.” Without it, the electrolyte would quickly get depleted during cycling, the cell would fail, and the lack of a stable layer would result in the formation of lithium metal whiskers — ‘dendrites’ — during the recharging process instead of a conformal flat layer.
Should these dendrites touch the positive electrode, this would cause a short circuit with the risk that the battery heats up so much that it ignites. The ability to control the properties of this protective layer is therefore crucial for battery performance. A stable protective layer increases battery efficiency, safety and service life.
Minimising fluorine content
“The question was how to reduce the amount of added fluorine without compromising the protective layer’s stability,” says doctoral student Nathan Hong. The group’s new method uses electrostatic attraction to achieve the desired reaction. Here, electrically charged fluorinated molecules serve as a vehicle to transport the fluorine to the protective layer. This means that only 0.1 percent by weight of fluorine is required in the liquid electrolyte, which is at least 20 times lower than in prior studies.
Optimised method makes batteries greener
The ETH Zurich research group describes the new method and its underlying principles in a paper recently published in the journal Energy & Environmental Science. An application for a patent has been made.
One of the biggest challenges was to find the right molecule to which fluorine could be attached and that would also decompose again under the right conditions once it had reached the lithium metal. As the group explains, a key advantage of this method is that it can be seamlessly integrated into the existing battery production process without generating additional costs to change the production setup. The batteries used in the lab were the size of a coin. In a next step, the researchers plan to test the method’s scalability and apply it to pouch cells as used in smartphones. More

To perform quantum computations, quantum bits (qubits) must be cooled down to temperatures in the millikelvin range (close to -273 Celsius), to slow down atomic motion and minimize noise. However, the electronics used to manage these quantum circuits generate heat, which is difficult to remove at such low temperatures. Most current technologies must therefore separate quantum circuits from their electronic components, causing noise and inefficiencies that hinder the realization of larger quantum systems beyond the lab.
Researchers in EPFL’s Laboratory of Nanoscale Electronics and Structures (LANES), led by Andras Kis, in the School of Engineering have now fabricated a device that not only operates at extremely low temperatures, but does so with efficiency comparable to current technologies at room temperature.
“We are the first to create a device that matches the conversion efficiency of current technologies, but that operates at the low magnetic fields and ultra-low temperatures required for quantum systems. This work is truly a step ahead,” says LANES PhD student Gabriele Pasquale.
The innovative device combines the excellent electrical conductivity of graphene with the semiconductor properties of indium selenide. Only a few atoms thick, it behaves as a two-dimensional object, and this novel combination of materials and structure yields its unprecedented performance. The achievement has been published in Nature Nanotechnology.
Harnessing the Nernst effect
The device exploits the Nernst effect: a complex thermoelectric phenomenon that generates an electrical voltage when a magnetic field is applied perpendicular to an object with a varying temperature. The two-dimensional nature of the lab’s device allows the efficiency of this mechanism to be controlled electrically.
The 2D structure was fabricated at the EPFL Center for MicroNanoTechnology and the LANES lab. Experiments involved using a laser as a heat source, and a specialized dilution refrigerator to reach 100 millikelvin — a temperature even colder than outer space. Converting heat to voltage at such low temperatures is usually extremely challenging, but the novel device and its harnessing of the Nernst effect make this possible, filling a critical gap in quantum technology.
“If you think of a laptop in a cold office, the laptop will still heat up as it operates, causing the temperature of the room to increase as well. In quantum computing systems, there is currently no mechanism to prevent this heat from disturbing the qubits. Our device could provide this necessary cooling,” Pasquale says.
A physicist by training, Pasquale emphasizes that this research is significant because it sheds light on thermopower conversion at low temperatures — an underexplored phenomenon until now. Given the high conversion efficiency and the use of potentially manufacturable electronic components, the LANES team also believes their device could already be integrated into existing low-temperature quantum circuits.
“These findings represent a major advancement in nanotechnology and hold promise for developing advanced cooling technologies essential for quantum computing at millikelvin temperatures,” Pasquale says. “We believe this achievement could revolutionize cooling systems for future technologies.” More

Drosophila — commonly known as fruit flies — are a valuable model for human heart pathophysiology, including cardiac aging and cardiomyopathy. However, a choke point in evaluating fruit fly hearts is the need for human intervention to measure the heart at moments of its largest expansion or its greatest contraction, measurements that allow calculations of cardiac dynamics.
Researchers at the University of Alabama at Birmingham now show a way to significantly cut the time needed for that analysis while utilizing more of the heart region, using deep learning and high-speed video microscopy for each heartbeat in the fly.
“Our machine learning method is not just fast; it minimizes human error because you don’t have to manually mark each heart wall under systolic and diastolic conditions,” said Girish Melkani, Ph.D., associate professor in the UAB Department of Pathology, Division of Molecular and Cellular Pathology. “Furthermore, you can run the analyses of several hundred hearts and look at the analyses when done for all the hearts.”
This can expand the ability to test how different environmental or genetic factors affect heart aging or pathology. Melkani envisions using deep learning-assisted studies to explore cardiac mutation models and other small animal models, such as zebrafish and mice. “Additionally, our techniques could be adapted for human heart models, providing valuable insights into cardiac health and disease. Incorporating uncertainty quantification methods could further enhance the reliability of our analyses. Moreover, the machine learning approach can predict cardiac aging with high accuracy.”
The fruit fly model has already been tremendously powerful for understanding the pathophysiological bases for several human cardiovascular diseases, Melkani says. Cardiovascular disease continues to be one of the leading causes of death and disability in the United States.
Melkani and UAB colleagues assessed their trained model on heart performance both in fruit fly cardiac aging and in a fruit fly model of dilated cardiomyopathy caused by the knockdown of a pivotal TCA cycle enzyme, oxoglutarate dehydrogenase. These automated assessments were then validated against existing experimental datasets. For example, for aging of fruit flies at one week versus five weeks of age, which is about halfway through a fruit fly’s life span, the UAB team used 54 hearts for model training and then validated their measurements against an experimental aging model with 177 hearts. Their trained model was able to reconstruct expected trends in cardiac parameters with aging.
Melkani says his team’s model can be applied to readily available consumer hardware, and his team’s code can provide calculated statistics including diastolic and systolic diameters/intervals, fractional shortening, ejection fraction, heart period/rate, and quantified heartbeat arrhythmicity.
“To our knowledge, this innovative platform for deep learning-assisted segmentation is the first of its kind to be applied to standard high-resolution high-speed optical microscopy of Drosophila hearts while also quantifying all relevant parameters,” Melkani said.
“By automating the process and providing detailed cardiac statistics, we pave the way for more accurate, efficient and comprehensive studies of heart function in Drosophila. This method holds tremendous potential — not only for understanding aging and disease in fruit flies — but also for translating these insights into human cardiovascular research.” More

A research team led by Director JO Moon-Ho of the Center for Van der Waals Quantum Solids within the Institute for Basic Science (IBS) has implemented a novel method to achieve epitaxial growth of 1D metallic materials with a width of less than 1 nm. The group applied this process to develop a new structure for 2D semiconductor logic circuits. Notably, they used the 1D metals as a gate electrode of the ultra-miniaturized transistor.
Integrated devices based on two-dimensional (2D) semiconductors, which exhibit excellent properties even at the ultimate limit of material thickness down to the atomic scale, are a major focus of basic and applied research worldwide. However, realizing such ultra-miniaturized transistor devices that can control the electron movement within a few nanometers, let alone developing the manufacturing process for these integrated circuits, has been met with significant technical challenges.
The degree of integration in semiconductor devices is determined by the width and control efficiency of the gate electrode, which controls the flow of electrons in the transistor. In conventional semiconductor fabrication processes, reducing the gate length below a few nanometers is impossible due to the limitations of lithography resolution. To solve this technical problem, the research team leveraged the fact that the mirror twin boundary (MTB) of molybdenum disulfide (MoS2), a 2D semiconductor, is a 1D metal with a width of only 0.4 nm. They used this as a gate electrode to overcome the limitations of the lithography process.
In this study, the 1D MTB metallic phase was achieved by controlling the crystal structure of the existing 2D semiconductor at the atomic level, transforming it into a 1D MTB. This represents a significant breakthrough not only for next-generation semiconductor technology but also for basic materials science, as it demonstrates the large-area synthesis of new material phases through artificial control of crystal structures.
The International Roadmap for Devices and Systems (IRDS) by the IEEE predicts semiconductor node technology to reach around 0.5 nm by 2037, with transistor gate lengths of 12 nm. The research team demonstrated that the channel width modulated by the electric field applied from the 1D MTB gate can be as small as 3.9 nm, significantly exceeding the futuristic prediction.
The 1D MTB-based transistor developed by the research team also offers advantages in circuit performance. Technologies like FinFET or Gate-All-Around, adopted for the miniaturization of silicon semiconductor devices, suffer from parasitic capacitance due to their complex device structures, leading to instability in highly integrated circuits. In contrast, the 1D MTB-based transistor can minimize parasitic capacitance due to its simple structure and extremely narrow gate width.
Director JO Moon-Ho commented, “The 1D metallic phase achieved through epitaxial growth is a new material process that can be applied to ultra-miniaturized semiconductor processes. It is expected to become a key technology for developing various low-power, high-performance electronic devices in the future.” More

As COVID-19 spread globally in 2020, many countries swiftly closed their borders to prevent the disease from entering. However, there was little scientific evidence to support the effectiveness of such measures.
While post-COVID research has extensively focused on the efficacy of internal travel restrictions, cross-border travel has received less attention due to challenges in accessing quality data. In a major multidisciplinary collaboration effort across Finland, Sweden, Norway, and Denmark, a group of researchers — including mathematicians, physicists and computer scientists — have published a pioneering study on the spread of infections across Nordic borders from spring until the end of 2020. The report sheds light on the efficacy of cross-border travel restrictions, helping us better understand which measures actually make a difference.
‘There have been many studies using data and modelling within countries, but this cross-border research is rather unique,’ says Associate Professor of Mathematics Lasse Leskelä from Finland’s Aalto University.
The researchers developed a sophisticated mathematical model relying on a long trail of footwork gathering travel data from the four neighbouring countries. Focus was on the short-term spread of the disease at a stage of the pandemic when infections had already started to spread within each country.
Border closures a blunt tool
The modelling revealed that cross-border closures were only likely to have significant impact in very specific scenarios. For example, a substantial disparity in disease prevalence between two countries would have to be accompanied by a high volume of cross-border traffic for restrictions to notably impact spread. It is notable that even though Sweden’s comparatively loose restrictions in 2020 contributed to the nation having vastly more case numbers than in neighbouring Finland, the overall impact of cross-border travel on the Finnish disease situation was low in absolute terms.
‘The way I see these results is that the closing of borders was mostly not very well justified. This was done out of uncertainty, because countries did not know what else to do. Since it has so many adverse effects, my take on this is that in the future, such drastic measures must be very carefully considered’, says Professor Tapio Ala-Nissilä from Aalto University.

However, the researchers point out that in different stages of a pandemic situation, there can be many layers of complexity. If a government must act, choosing between restricting local populations within its borders versus restricting travel across them, the latter may prove the better option.
‘According to our model, travellers from Sweden were over 10 times more likely to have COVID-19 in the summer of 2020 than the domestic Finnish population. So if you think about when the restrictions should hit and who should be affected, it would make more sense to place restrictions on these travellers at this time,’ Assistant Professor Mikko Kivelä from Aalto University points out.
The model also shows interesting differences between types of travel. Commuters, who may spend half a day in the destination country at a time, played a smaller role in spreading infections than vacationers who possibly spent their entire infectious periods in the country.
Preparing for the next pandemic
Kivelä emphasises that in spring 2020, decision-makers were faced with myriad uncertainties that made it impossible to reliably analyse and estimate the effects of their countermeasures. This is also where the current study makes its most significant contribution — as a predictive model for future use.
‘The really important part is that we have developed different ways of looking at this question: a mathematical machinery to answer questions about what border control interventions are necessary and when to apply,’ says university researcher Mikhail Shubin from the University of Helsinki.
Although the current study pertains to the Nordics, the researchers say that it can be applied to other countries as well. The main concern is getting reliable and comparable data. Often, even if the outward appearance of a particular data set is promising, details like reporting delays will complicate its usage.
‘Access to mobility is not easy to gain, and within the Schengen area in particular there is no detailed tracking for who moves where. You need to have access to lots of data sets, from road crossings to railroads, ferries and aeroplanes. We also used mobile phone data to validate our findings,’ explains Leskelä. ‘Usually, to do this detailed modelling, you need personal contacts and you need to build trust.’
The study is part of the NordicMathCovid project. The project includes teams from Finland, Sweden and Norway and involves a number of universities and public institutions across the Nordics. Supported by NordForsk, the project started in September 2020 and has produced research on pandemic flows and vaccination strategies from varying angles. More