Lifeboat Foundation

A novel metallic compound of hydrogen, carbon, and sulfur exhibited superconductivity at a balmy 59 degrees Fahrenheit—when pressurized between a pair of diamond anvils.

Via Quanta Magazine9 months ago.

Physicists finally achieved the long-sought goal, but there’s a catch: Their compound requires crushing pressures to keep from falling apart.

Continue reading “Scientists Discover the First Room-Temperature Superconductor” »

O,.o.

A study led by scientists have discovered that vanadium dioxide breaks the laws of physics, transferring electricity but not heat.

A new tool that enables thousands of tiny experiments to run simultaneously on a single polymer chip will let scientists study enzymes faster and more comprehensively than ever before.

For much of human history, animals and plants were perceived to follow a different set of rules than rest of the universe. In the 18th and 19th centuries, this culminated in a belief that living organisms were infused by a non-physical energy or “life force” that allowed them to perform remarkable transformations that couldn’t be explained by conventional chemistry or physics alone.

Scientists now understand that these transformations are powered by enzymes – protein molecules comprised of chains of amino acids that act to speed up, or catalyze, the conversion of one kind of molecule (substrates) into another (products). In so doing, they enable reactions such as digestion and fermentation – and all of the chemical events that happen in every one of our cells – that, left alone, would happen extraordinarily slowly.

Scientists hunting for elusive gravitational waves across the universe may be able to supercharge their discoveries with a new tool: artificial intelligence.

Gravitational waves are ripples in spacetime, created when a massive object is accelerated or disturbed, such as when a black hole and a neutron star collide. Theorized by Albert Einstein, their existence was confirmed in 2015 with the first gravitational wave discovery by researchers using LIGO (the advanced Laser Interferometer Gravitational-Wave Observatory). Now, just six years later, there have been at least 50 gravitational wave events detected.

The prediction of protein structures from amino acid sequence information alone, known as the “protein folding problem,” has been an important open research question for more than 50 years. In the fall of 2020, DeepMind’s neural network model AlphaFold took a huge leap forward in solving this problem, outperforming some 100 other teams in the Critical Assessment of Structure Prediction (CASP) challenge, regarded as the gold-standard accuracy assessment for protein structure prediction. The success of the novel approach is considered a milestone in protein structure prediction.

This week, the DeepMind paper Highly Accurate Protein Structure Prediction with AlphaFold was published in the prestigious scientific journal Nature. The paper introduces AlphaFold2, a completely redesigned and open-sourced model that can predict protein structures with atomic-level accuracy.

Although machine learning researchers have long sought to develop computational methods for predicting 3D protein structures from protein sequences, there had been limited progress along this path, chiefly due to the computational intractability of molecular simulation, the context-dependence of protein stability, and the difficulty of producing sufficiently accurate models for protein physics.

On the first passenger test flight for his space company Blue Origin, Jeff Bezos blasted into space from West Texas along with his brother Mark Bezos, 82-year-old aviator Wally Funk and 18-year-old physics student Oliver Daemen aboard the rocket New Shepard. NBC’s Tom Costello reports for TODAY from Corn Ranch, Texas.

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The oil industry, pharmaceutical companies and bioreactor manufacturers all face one common enemy: bubbles. Bubbles can form during the manufacturing or transport of various liquids, and their formation and rupture can cause significant issues in product quality.

Inspired by these issues and the puzzling physics behind bubbles, an international scientific collaboration was born. Stanford University chemical engineer Gerald Fuller along with his Ph.D. students Aadithya Kannan and Vinny Chandran Suja, as well as visiting Ph.D. student Daniele Tammaro from the University of Naples, teamed up to study how different kinds of bubbles pop.

The researchers were particularly interested in bubbles with proteins embedded on their surfaces, which is a common occurrence in the pharmaceutical industry and in bioreactors used for cell culture. In an unanticipated result, the researchers discovered that the protein bubbles they were studying opened up like flowers when popped with a needle. Their findings are detailed in a study published in the journal of the Proceedings of the National Academy of Sciences on July 19.

Salvatore Vitale describes how gravitational-wave signals suggest black holes completely devoured their companion neutron stars.

Recently, an international team of scientists, including researchers at MIT, announced the detection of a new kind of astrophysical system: a collision between a black hole and a neutron star — two of the densest, most exotic objects in the universe.

Scientists have detected signals of colliding black holes, and colliding neutron stars, but had not confirmed a merging of a black hole with a neutron star until now. In a study appearing today in The Astrophysical Journal Letters, the scientists report observing not just one, but two such rare events, each of which gave off gravitational waves that reverberated across a large swath of the universe before reaching Earth in January 2020, just 10 days apart.

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM).