Phys.org reports: Future fusion power plants aim to recreate the heart of a star here on Earth to power our future energy needs. While the core fusion plasma will burn at hundreds of millions of degrees, the surrounding structural components must handle sudden, punishing heat loads that rival the extreme temperatures faced by spacecraft upon reentry into Earth's atmosphere. Copper and its alloys are primary candidates for handling these intense heat fluctuations, making it vital to understand exactly how the metal behaves when pushed to its melting point. Now, researchers at the Department of Energy's SLAC National Accelerator Laboratory and collaborators have captured a detailed, step-by-step look at copper atoms as they underwent extreme heating. Published in Nature Communications, the results revealed a key parameter that allowed copper's crystal lattice to melt steadily rather than collapse instantaneously, as earlier simulations predicted. "These results greatly improve the simulations we use to predict which materials have the best shot at surviving the extreme conditions of future fusion reaction chambers," said Mianzhen Mo, a SLAC staff scientist who led the research. "They also demonstrate the incredible, atomic-scale resolution imaging we can achieve at SLAC's electron camera...." Researchers use computer simulations, aided by AI and machine learning, to sift through innumerable combinations of elements and identify promising candidate materials for real-world testing. "Whether the copper melts slowly or suddenly collapses, by the time the researchers look, the sample resembles nothing more than a metallic brown puddle," the article points out. But SLAC's powerful electron camera captures atomic and molecular movements down to the femtosecond — a millionth of a billionth of a second — and revealed that at around 1,424 degreesC (2,595 degreesF) there was still gradual melting as the temperature rose beyond the superheating limit, with real-world conditions showing the atoms shifted and retained some order. "It's a straightforward solution," said Mianzhen Mo, a SLAC staff scientist who led the research. "But molecular dynamics simulations had been overlooking it for years. When you have complex simulations attempting to capture every aspect of reality, down to individual atoms, it takes real-world data to show you what's missing from the calculations."

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Certain kinds of clouds are misbehaving – appearing more often and lower in the sky than they used to. To help identify the factors influencing these changes (e.g. shifts in Earth’s long-term weather patterns), scientists have asked people around the world with cameras to submit fresh images of these clouds as a part of the NASA-supported Space Cloud Watch project. Now, one volunteer has developed a new tool to help other Space Cloud Watch volunteers work more efficiently. 

The misbehaving clouds are “noctilucent”  or “night-shining” clouds (NLCs). These clouds scatter light from the Sun long after sunset and long before sunrise, giving them a silvery glow. But despite this glow, it can be hard to differentiate NLCs from lower-altitude look-alikes. That confusion has meant extra work for project leaders.

Volunteer Namai Chandra shared, “I noticed that NLC images were being manually verified by the project leaders. It felt like a task well-suited for a human-in-the-loop machine learning pipeline, one that could handle the repetitive screening automatically, while keeping human judgment central for the images that matter most.” In other words, Namai found a way to help observers verify when they are indeed seeing NLCs and when they’re not. 

Namai reached out to the Space Cloud Watch scientists Drs. Chihoko Cullens and Brentha Thurairajah, who were delighted with his idea. Namai soon developed a machine learning pipeline, training it on a variety of cloud images, including both the NLCs and the lower altitude look-alikes that are often submitted to Space Cloud Watch. The pipeline combines image pre-screening, cloud classification, and confidence-based review routing. After several rounds of development, testing, and refinement, he released his NLC identification tool to the project. This tool is now being used by cloud contributors who are unsure whether they have observed NLCs, as well as project scientists that want to flag images for review. 

Grab a camera and join the Space Cloud Watch project today! If you’ve hesitated to contribute to Space Cloud Watch because you were not certain if what you were seeing was a noctilucent cloud, you now have a way to check before you share – thanks to Namai.