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Breaking Barriers in Surface Chemistry: The autoSKZCAM Framework for Ionic Materials

Understanding and predicting chemical reactions on surfaces lies at the heart of modern materials science. From heterogeneous catalysis to energy storage and greenhouse gas sequestration, surface chemistry defines the efficiency and viability of advanced technologies. Yet, computationally modeling these processes with both accuracy and efficiency has been a grand challenge.

A recent study published in Nature Chemistry introduces a breakthrough: the autoSKZCAM framework, an automated and open-source method that applies correlated wavefunction theory (cWFT) to surfaces of ionic materials at costs comparable to density functional theory (DFT). This achievement not only bridges the accuracy gap but also enables routine, large-scale studies of surface processes with chemical accuracy.

Streams of gas might lead to the rapid formation of high-mass stars

The size of our universe and the bodies within it is incomprehensible to us lowly humans. The sun has a mass that is more than 330,000 times that of our Earth, and yet there are stars in the universe that completely dwarf our sun.

Stars with masses more than eight times that of the sun are considered high-mass stars. These form rapidly in a process that gives off and radiation, which could not result in stars of such high mass without somehow overcoming this loss of mass, or feedback. Something is feeding these stars, but how exactly they can accumulate so much mass so quickly has remained a mystery.

Observations have proposed that enormous disk-like structures that form around a star— —might be the chief way of rapidly feeding . However, a team of researchers from several institutions, including Kyoto University and the University of Tokyo, has discovered another possibility.

TAU Breakthrough Offers New Hope to Help People With Paralysis Walk Again

Paralysis from spinal injury has long remained untreatable. Could scientific developments get people affected on their feet again sooner than imagined? In a worldwide first, Tel Aviv University researchers have engineered 3D human spinal cord tissues and implanted them in a lab model with long-term chronic paralysis, demonstrating high rates of success in restoring walking abilities. Now, the researchers are preparing for the next stage of the study, clinical trials in human patients.

Shape-changing soft material for soft robotics, smart textiles and more

Harvard researchers developed liquid crystal elastomers that can switch between multiple shapes — chevrons, flat layers, and coils — in response to heat.

By aligning molecules in different directions, the material can be programmed to morph into domes, saddles, or fin-like motions inspired by stingrays and jellyfish.

The shape-shifting material could advance applications in soft robotics, biomedical devices, and smart textiles.

Liquid crystal elastomers are a class of soft materials that can change shape in response to stimuli such as light or heat — making them promising for applications in soft robotics, wearable and biomedical devices, smart textiles and more. But designing compositionally uniform elastomers that can change into different shapes in response to just one stimulus has been challenging and has limited the application of these potentially powerful materials.

Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a way to program liquid crystal elastomers with the ability to deform in opposite directions just by heating — opening up a range of applications.

The research was published in Science.

(May be a repost from 2024)

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A Wearable Robot That Learns

Having lived with an ALS diagnosis since 2018, Kate Nycz can tell you firsthand what it’s like to slowly lose motor function for basic tasks. “My arm can get to maybe 90 degrees, but then it fatigues and falls,” the 39-year-old said. “To eat or do a repetitive motion with my right hand, which was my dominant hand, is difficult. I’ve mainly become left-handed.”

People like Nycz who live with a neurodegenerative disease like ALS or who have had a stroke often suffer from impaired movement of the shoulder, arm or hands, preventing them from daily tasks like tooth-brushing, hair-combing or eating.

For the last several years, Harvard bioengineers have been developing a soft, wearable robot that not only provides movement assistance for such individuals but could even augment therapies to help them regain mobility.

But no two people move exactly the same way. Physical motions are highly individualized, especially for the mobility-impaired, making it difficult to design a device that works for many different people.

It turns out advances in machine learning can create a more personal touch. Researchers in the John A. Paulson School of Engineering and Applied Sciences (SEAS), together with physician-scientists at Massachusetts General Hospital and Harvard Medical School, have upgraded their wearable robot to be responsive to an individual user’s exact movements, endowing the device with more personalized assistance that could give users better, more controlled support for daily tasks.

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