Lifeboat Foundation

Australian researchers have developed and tested a way to electrolyze hydrogen straight out of the air, anywhere on Earth, without requiring any other fresh water source. The Direct Air Electrolyzer (DAE) absorbs and converts atmospheric moisture – even down to a “bone-dry” 4% humidity.

Such a machine could be particularly relevant to a country like Australia, which has ambitions as a clean energy exporter, along with enormous solar energy potential – but also widespread drought conditions and limited access to clean water. Decoupling hydrogen production from the need for a water supply could allow green hydrogen to be produced more or less anywhere you can ship it out from – and since water scarcity and solar potential often go hand in hand, this could prove a boon for much of Africa, Asia, India and the Middle East, too.

Chemical engineers at Melbourne University came up with what they describe as a simple design: an electrolyzer with two flat plates acting as anode and cathode. Sandwiched between the two plates is a porous material – melamine sponge, for example, or sintered glass foam. This medium is soaked in a hygroscopic ionic solution – a chemical that can absorb moisture from the air spontaneously.

The equations of quantum mechanics provide a roadmap to predicting the properties of chemicals starting from basic scientific theories. However, these equations quickly become too expensive in terms of computer time and power when used to predict behavior in large systems. Machine learning offers a promising approach to accelerating such large-scale simulations.

Researchers have shown that machine learning models can mimic the basic structure of the fundamental laws of nature. These laws can be very difficult to simulate directly. The machine learning approach enables predictions that are easy to compute and are accurate in a wide range of chemical systems.

The improved machine learning model can quickly and accurately predict a wide range of properties of molecules (Proceedings of the National Academy of Sciences, “Deep Learning of Dynamically Responsive Chemical Hamiltonians with Semi-Empirical Quantum Mechanics”). These approaches score very well on important benchmarks in computational chemistry and show how deep learning methods can continue to improve by incorporating more data from experiments. The model can also succeed at challenging tasks such as predicting excited state dynamics—how systems behave with elevated energy levels.

As the poet Dylan Thomas once explained, it is “the force that through the green fuse drives the flower.”

Organic photochemistry brings life to Earth, allowing plants to “eat” sunlight. Using this power of light to make new molecules in the lab instead of the leaf, from fuel to pharmaceuticals, is one of the grand challenges of photochemical research.

What is old is new again. Sometimes gaining new insight requires a return to old tools, with a modern twist. Now, a collaborative team from the National Renewable Energy Laboratory (NREL) and Princeton University has resurrected a century-old microwave technique to reveal a surprising feature of well-established light-driven chemistry.

THE artificial intelligence revolution has only just begun, but there have already been numerous unsettling developments.

AI programs can be used to act on humans’ worst instincts or achieve humans’ more wicked goals, like creating weapons or terrifying its creators with a lack of morality.

Artificial intelligence is a catch-all phrase for a computer program designed to simulate, mimic or copy human thinking processes.

A team of researchers from the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and Stony Brook University have devised a new quantum algorithm to compute the lowest energies of molecules at specific configurations during chemical reactions, including when their chemical bonds are broken. As described in Physical Review Research, compared to similar existing algorithms, including the team’s previous method, the new algorithm will significantly improve scientists’ ability to accurately and reliably calculate the potential energy surface in reacting molecules.

For this work, Deyu Lu, a Center for Functional Nanomaterials (CFN) physicist at Brookhaven Lab, worked with Tzu-Chieh Wei, an associate professor specializing in quantum information science at the C.N. Yang Institute for Theoretical Physics at Stony Brook University, Qin Wu, a theorist at CFN, and Hongye Yu, a Ph.D. student at Stony Brook.

“Understanding the quantum mechanics of a molecule, how it behaves at an atomic level, can provide key insight into its chemical properties, like its stability and reactivity,” said Lu.

The global dairy industry is changing. Among the disruptions is competition from food alternatives not produced using animals – including potential challenges posed by synthetic milk.

Synthetic milk does not require cows or other animals. It can have the same biochemical make up as animal milk, but is grown using an emerging biotechnology technique know as “precision fermentation” that produces biomass cultured from cells.

More than 80 percent of the world’s population regularly consume dairy products. There have been increasing calls to move beyond animal-based food systems to more sustainable forms of food production.

The technology at the heart of this research takes aim at one of the key metabolic functions of cells in all living things called ATP, or adenosine triphosphate. This molecule is the primary energy carrier in cells, capturing chemical energy from the breakdown of food molecules and distributing it to power other cellular processes.

Among those cellular processes is the proliferation of cancerous cells, and because of this we have seen ATP implicated in previous anti-cancer breakthroughs. The authors of the new study sought to cut off the supply of ATP, which is generated as mitochondria soak up oxygen and convert it into the molecule.

Circa 2005 Bacteria that is resistant to radiation could lead to better radiation resistance in humans.

Relatively little is known about the biochemical basis of the capacity of Deinococcus radiodurans to endure the genetic insult that results from exposure to ionizing radiation and can include hundreds of DNA double-strand breaks. However, recent reports indicate that this species compensates for extensive DNA damage through adaptations that allow cells to avoid the potentially detrimental effects of DNA strand breaks. It seems that D. radiodurans uses mechanisms that limit DNA degradation and that restrict the diffusion of DNA fragments that are produced following irradiation, to preserve genetic integrity. These mechanisms also increase the efficiency of the DNA-repair proteins.

“Neuromorphic computing could offer a compelling alternative to traditional AI accelerators by significantly improving power and data efficiency for more complex AI use cases, spanning data centers to extreme edge applications.”

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Can computer systems develop to the point where they can think creatively, identify people or items they have never seen before, and adjust accordingly — all while working more efficiently, with less power? Intel Labs is betting on it, with a new hardware and software approach using neuromorphic computing, which, according to a recent blog post, “uses new algorithmic approaches that emulate how the human brain interacts with the world to deliver capabilities closer to human cognition.”

Continue reading “Is Intel Labs’ brain-inspired AI approach the future of robot learning?” »