Lifeboat Foundation

Solid-state nuclear magnetic resonance (NMR) spectroscopy—a technique that measures the frequencies emitted by the nuclei of some atoms exposed to radio waves in a strong magnetic field—can be used to determine chemical and 3D structures as well as the dynamics of molecules and materials.

A necessary initial step in the analysis is the so-called chemical shift assignment. This involves assigning each peak in the NMR spectrum to a given atom in the molecule or material under investigation. This can be a particularly complicated task. Assigning chemical shifts experimentally can be challenging and generally requires time-consuming multi-dimensional correlation experiments. Assignment by comparison to statistical analysis of experimental chemical shift databases would be an alternative solution, but there is no such database for molecular solids.

A team of researchers including EPFL professors Lyndon Emsley, head of the Laboratory of Magnetic Resonance, Michele Ceriotti, head of the Laboratory of Computational Science and Modeling and Ph.D. student Manuel Cordova decided to tackle this problem by developing a method of assigning NMR spectra of organic crystals probabilistically, directly from their 2D chemical structures.

https://youtube.com/watch?v=C2kKgtCfUAY

“We are proud to be able to showcase the world’s first fully electric and self-propelled container ship,” said Svein Holsether, CEO of Norwegian chemical company Yara International. “It will cut 1,000 tonnes of CO₂ and replace 40,000 trips by diesel-powered trucks a year.”

Yara has collaborated since 2017 with maritime technology company Kongsberg to develop the ship, which sailed from Horten to Oslo, a distance of approximately 35 nautical miles (65 km). Powered by 7 MWh batteries, it uses an automatic identification system (AIS), cameras (including infrared), a lidar, and radar system. It will begin commercial operations in 2022, transporting mineral fertiliser between ports in southern Norway at up to 15 knots (28 km/h).

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It is the hardest known glass with the highest thermal conductivity among all glass materials.

Carnegie’s Yingwei Fei and Lin Wang were part of an international research team that synthesized a new ultrahard form of carbon glass with a wealth of potential practical applications for devices and electronics. It is the hardest known glass with the highest thermal conductivity among all glass materials. Their findings are published in Nature.

Function follows form when it comes to understanding the properties of a material. How its atoms are chemically bonded to each other, and their resulting structural arrangement, determines a material’s physical qualities—both those that are observable by the naked eye and those that are only revealed by scientific probing.

Ammolite is an opal-like organic gemstone found primarily along the eastern slopes of the Rocky Mountains of North America. It is made of the fossilized shells of ammonites, which in turn are composed primarily of aragonite, the same mineral contained in nacre, with a microstructure inherited from the shell. It is one of few biogenic gemstones; others include amber and pearl.

The chemical composition of ammolite is variable, and aside from aragonite may include calcite, silica, pyrite, or other minerals. The shell itself may contain a number of trace elements, including: aluminium; barium; chromium; copper; iron; magnesium; manganese; strontium; titanium; and vanadium. Its crystallography is orthorhombic. Its hardness is 4.5–5.5, and its specific gravity is 2.60–2.85.

An iridescent opal-like play of color is shown in fine specimens, mostly in shades of green and red; all the spectral colors are possible, however. The iridescence is due to the microstructure of the aragonite: unlike most other gems, whose colors come from light absorption, the iridescent color of ammolite comes from interference with the light that rebounds from stacked layers of thin platelets that make up the aragonite.

It could replace cartilage in knees and even help create soft robots 🤯

Is it a bird? Is it a plane? No, it’s ‘super jelly’ — a bizarre new material that can survive being run over by a car even though it’s composed of 80 per cent water.

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Most human diseases can be traced to malfunctioning parts of a cell—a tumor is able to grow because a gene wasn’t accurately translated into a particular protein or a metabolic disease arises because mitochondria aren’t firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts.

By combining microscopy, biochemistry techniques and artificial intelligence, researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells.

The technique, known as Multi-Scale Integrated Cell (MuSIC), is described November 24, 2021 in Nature.

And it could halve the transit time to Mars.

Pulsar Fusion Ltd., a nuclear fusion company based in the United Kingdom, has recently designed and successfully tested its first launch-capable, high-power chemical rocket engine.

Continue reading “A UK Rocket Company Wants to Put Nuclear Fusion Power in Orbit by 2027” »

Rarely does scientific software spark such sensational headlines. “One of biology’s biggest mysteries ‘largely solved’ by AI”, declared the BBC. Forbes called it “the most important achievement in AI — ever”. The buzz over the November 2020 debut of AlphaFold2, Google DeepMind’s (AI) system for predicting the 3D structure of proteins, has only intensified since the tool was made freely available in July.

The excitement relates to the software’s potential to solve one of biology’s thorniest problems — predicting the functional, folded structure of a protein molecule from its linear amino-acid sequence, right down to the position of each atom in 3D space. The underlying physicochemical rules for how proteins form their 3D structures remain too complicated for humans to parse, so this ‘protein-folding problem’ has remained unsolved for decades.

Researchers have worked out the structures of around 160,000 proteins from all kingdoms of life. They have been using experimental techniques, such as X-ray crystallography and cryo-electron microscopy (cryo-EM), and then depositing their 3D information in the Protein Data Bank. Computational biologists have made steady gains in developing software that complements these methods, and have correctly predicted the 3D shapes of some molecules from well-studied protein families.

The most promising application in biomedicine is in computational chemistry, where researchers have long exploited a quantum approach. But the Fraunhofer Society hopes to spark interest among a wider community of life scientists, such as cancer researchers, whose research questions are not intrinsically quantum in nature.

“It’s uncharted territory,” says oncologist Niels Halama of the DKFZ, Germany’s national cancer center in Heidelberg. Working with a team of physicists and computer scientists, Halama is planning to develop and test algorithms that might help stratify cancer patients, and select small subgroups for specific therapies from heterogeneous data sets.

This is important for precision medicine, he says, but classic computing has insufficient power to find very small groups in the large and complex data sets that oncology, for example, generates. The time needed to complete such a task may stretch out over many weeks—too long to be of use in a clinical setting, and also too expensive. Moreover, the steady improvements in the performance of classic computers are slowing, thanks in large part to fundamental limits on chip miniaturization.