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Category: climatology – Page 13

Moving pictures: Researchers use movies to diagnose EV battery failure

Charging electric-vehicle batteries in Ithaca’s frigid winter can be tough, and freezing temperatures also decrease the driving range. Hot weather can be just as challenging, leading to decomposition of battery materials and, possibly, catastrophic failure.

For (EVs) to be widely accepted, safe and fast-charging lithium-ion batteries need to be able to operate in extreme temperatures. But to achieve this, scientists need to understand how materials used in EVs change during temperature-related chemical reactions, a so-far elusive goal.

Now, Cornell chemists led by Yao Yang, Ph.D. ‘21, assistant professor of chemistry and chemical biology in the College of Arts and Sciences, have developed a way to diagnose the mechanisms behind battery failure in extreme climates using electron microscopy. Their first-of-its-kind operando (“operating”) electrochemical transmission electron microscopy (TEM) enables them to watch chemistry in action and collect real-time movies showing what happens to energy materials during temperature changes.

How ‘spin currents’ can be used to control magnetic states in advanced materials

A new study reveals a fresh way to control and track the motion of skyrmions—tiny, tornado-like magnetic swirls that could power future electronics. Using electric currents in a special magnetic material called Fe₃Sn₂, the team got these skyrmions to “vibrate” in specific ways, unlocking clues about how invisible spin currents flow through complex materials.

The discovery not only confirms what theory had predicted but also points to a powerful new method for detecting spin currents—a discovery that could one day lead to more efficient memory and sensing devices in future electronics. The findings are published in the journal Nature Communications.

Led by Assistant Prof. Amir Capua and Ph.D. Candidate Nirel Bernstein from the Institute of Applied Physics and Nano Center at Hebrew University in collaboration with Prof. Wenhong Wang and Dr. Hang Li from Tiangong University, the team explored how skyrmions behave in a special magnetic material called Fe₃Sn₂ (iron tin).

Novel equation predicts how crystals and bubbles in magma alter seismic waves

A recent study has mathematically clarified how the presence of crystals and gas bubbles in magma affects the propagation of seismic P-waves. The researchers derived a new equation that characterizes the travel of these waves through magma, revealing how the relative proportions of crystals and bubbles influence wave velocity and waveform properties.

The ratio of crystals to bubbles in subterranean magma reservoirs is crucial for forecasting . Due to the inaccessibility of direct observations, scientists analyze seismic P-waves recorded at the surface to infer these internal characteristics.

Previous studies have predominantly focused on the influence of , with limited consideration given to crystal content. Moreover, conventional models have primarily addressed variations in wave velocity and amplitude decay, without capturing detailed waveform transformations.

Seismic Waves From Intense Storms Can Ripple Through Earth’s Core

Researchers have now ‘heard’ the echo of cyclones whirling ocean waters from all the way on the other side of our planet.

Microseismic waves generated by interactions between the ocean and Earth’s crust might be able to help us peer into otherwise hidden parts of Earth’s geological structure, such as regions left shrouded by a scarcity of high-energy earthquakes in the North Atlantic.

“Our research uses these microseismic phenomena as an alternative data source to study the Earth’s structure beneath Australia,” says seismologist Hrvoje Tkalčić from Australian National University.

Loss of Genetic Plant Diversity Is Now Visible From Space

A new study combining satellite imagery with genetic analysis reveals that climate and land use changes are driving increased vegetation growth in Europe’s mountain regions, ultimately leading to a decline in the genetic diversity of medicinal plants such as Greek mountain tea. Mountain regions a.

A supercomputer figured out when all life on Earth will end

NASA scientists, in collaboration with researchers from Japan’s University of Toho, have used supercomputers to model the far future of Earth’s habitability. Their findings offer a clear—if distant—timeline for the end of life on our planet.

According to the study, the Sun will be the ultimate cause of the end of life on Earth. Over the next billion years, its output will continue to increase, gradually heating the planet beyond the threshold of life. The research estimates that life on Earth will end around the year 1,000,002,021, when surface conditions become too extreme to support even the most resilient organisms.

But the decline will begin much earlier. As the Sun grows hotter, Earth’s atmosphere will undergo significant changes. Oxygen levels will fall, temperatures will rise exponentially, and air quality will worsen. These shifts, projected through detailed climate change and solar radiation models, map out when life on Earth will end, not as a sudden collapse but as a slow and irreversible decline.

Near Space Labs nabs $20M to take its high-res imaging Swift robots into the stratosphere

When it comes to creating images of the earth from above, satellites, drones, planes and spacecraft are what tend to come to mind. But a startup called Near Space Labs is taking a very different approach to taking high-resolution photos from up high.

Near Space Labs is building aircraft that are raised by helium balloons and then rely on air currents to stay up, move around to take pictures from the stratosphere, and eventually glide back down to earth. On the back of significant traction with customers using its images, the startup has now raised $20 million to expand its business.

Bold Capital Partners (a VC firm founded by Peter Diamandis of XPRIZE and Singularity University fame), is leading the Series B round. Strategic backer USAA (the U.S. Automobile Association) is also investing alongside Climate Capital, Gaingels, River Park Ventures, and previous backers Crosslink Capital, Third Sphere, Draper Associates, and others that are not being named. Near Space Labs has now raised over $40 million, including a $13 million Series A in 2021.

How can we optimize solid-state batteries? Try asking AI

Scientists are racing against time to try and create revolutionary, sustainable energy sources (such as solid-state batteries) to combat climate change. However, this race is more like a marathon, as conventional approaches are trial-and-error in nature, typically focusing on testing individual materials and set pathways one by one.

To get us to the finish line faster, researchers at Tohoku University developed a data-driven AI framework that points out potential solid-state electrolyte (SSE) candidates that could be “the one” to create the ideal sustainable energy solution.

This model does not only select optimal candidates, but can also predict how the reaction will occur and why this candidate is a good choice—providing interesting insights into potential mechanisms and giving researchers a huge head start without even stepping foot into the lab.

AI model based on neural oscillations delivers stable, efficient long-sequence predictions

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence (AI) model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data.

AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model called “state-space models” has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges—they can become unstable or require a significant amount of computational resources when processing long data sequences.

To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators—a concept deeply rooted in physics and observed in .

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