Lifeboat Foundation

Circa 2021 force field this can also shield the earth or cities.

The computational design of functional materials relies heavily on large-scale atomistic simulations. Such simulations are often problematic for conventional classical force fields, which require tedious and time-consuming parameterization of interaction parameters. The problem can be solved using a quantum mechanically derived force field (QMDFF)—a system-specific force field derived directly from the first-principles calculations. We present a computational approach for atomistic simulations of complex molecular systems, which include the treatment of chemical reactions with the empirical valence bond approach. The accuracy of the QMDFF is verified by comparison with the experimental properties of liquid solvents.

In a new paper in PNAS, “Triplet-Pair Spin Signatures From Macroscopically Aligned Heteroacenes in an Oriented Single Crystal,” National Renewable Energy Laboratory (NREL) researchers Brandon Rugg, Brian Fluegel, Christopher Chang, and Justin Johnson tackle one of the fundamental problems in quantum information science: how to produce pure elements of quantum information—that is, those that start and remain in a well-defined “spin state”—at practical temperatures.

Quantum information science has the potential to revolutionize computation, sensing, and communications. But many of these applications are still beyond reach because of the challenges of producing units of quantum information, or qubits, without relying on extremely low temperatures to maintain their purity. Current approaches to identifying suitable quantum materials tend to rely on trial and error.

“The field of developing new molecules and materials [for quantum information science] sometimes progresses through ad hoc methods and serendipity. ‘This material just so happens to work better than the other one’—we saw a lot of that happening, and decided ultimately that it was not going to suffice for a project where the goal was to limit the set of possible options,” said Justin Johnson, a researcher in NREL’s Chemistry and Nanoscience Center. “We wanted to have the theory provide us with firm guidelines about what should happen.”

Australian scientists say they’ve made a “eureka moment” breakthrough in gas separation and storage that could radically reduce energy use in the petrochemical industry, while making hydrogen much easier and safer to store and transport in a powder.

Nanotechnology researchers, based at Deakin University’s Institute for Frontier Materials, claim to have found a super-efficient way to mechanochemically trap and hold gases in powders, with potentially enormous and wide-ranging industrial implications.

Mechanochemistry is a relatively recently coined term, referring to chemical reactions that are triggered by mechanical forces as opposed to heat, light, or electric potential differences. In this case, the mechanical force is supplied by ball milling – a low-energy grinding process in which a cylinder containing steel balls is rotated such that the balls roll up the side, then drop back down again, crushing and rolling over the material inside.

A recent study from the Singapore Centre for Environmental Life Sciences Engineering (SCELSE) at Nanyang Technological University (NTU) and published in Wa | Chemistry And Physics.

This study is intriguing since one of the results of climate change is increasing water temperatures, so removing phosphorus from such waters will prove invaluable in the future, with this study appropriately being referred to as a “future-proof” method.

Continue reading “New ‘future-proof’ method could remove phosphorus from wastewater using bacteria” »

Scientists have tested a natural chemical that could heal wounds twice as fast.

So precise.

If chemists built cars, they’d fill a factory with car parts, set it on fire, and sift from the ashes pieces that now looked vaguely car-like.

When you’re dealing with car-parts the size of atoms, this is a perfectly reasonable process. Yet chemists yearn for ways to reduce the waste and make reactions far more precise.

Continue reading “Chemists Just Rearranged Atomic Bonds in a Single Molecule For The First Time” »

Early detection and identification of pathogenic bacteria in food and water samples are essential to public health. Bacterial infections cause millions of deaths worldwide and bring a heavy economic burden, costing more than 4 billion dollars annually in the United States alone. Among pathogenic bacteria, Escherichia coli (E. coli) and other coliform bacteria are among the most common ones, and they indicate fecal contamination in food and water samples. The most conventional and frequently used method for detecting these bacteria involves culturing of the samples, which usually takes 24 hours for the final read-out and needs expert visual examination. Although some methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, they cannot differentiate live and dead bacteria and present low sensitivity at low concentrations of bacteria. That is why the U.S. Environmental Protection Agency (EPA) approves no nucleic acid-based bacteria sensing method for screening water samples.

In an article recently published in ACS Photonics, a journal of the American Chemical Society (ACS), a team of scientists, led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), and co-workers have developed an AI-powered smart bacterial colony detection system using a thin-film transistor (TFT) array, which is a widely used technology in mobile phones and other displays.

The ultra-large imaging area of the TFT array (27 mm × 26 mm) manufactured by researchers at Japan Display Inc. enabled the system to rapidly capture the growth patterns of bacterial colonies without the need for scanning, which significantly simplified both the hardware and software design. This system achieved ~12-hour time savings compared to gold-standard culture-based methods approved by EPA. By analyzing the microscopic images captured by the TFT array as a function of time, the AI-based system could rapidly and automatically detect colony growth with a deep neural network. Following the detection of each colony, a second neural network is used to classify the bacteria species.

The question of how the chemical composition of a protein—the amino acid sequence—determines its 3D structure has been one of the biggest challenges in biophysics for more than half a century. This knowledge about the so-called “folding” of proteins is in great demand, as it contributes significantly to the understanding of various diseases and their treatment, among other things. For these reasons, Google’s DeepMind research team has developed AlphaFold, an artificial intelligence that predicts 3D structures.

A team consisting of researchers from Johannes Gutenberg University Mainz (JGU) and the University of California, Los Angeles, has now taken a closer look at these structures and examined them with respect to knots. We know knots primarily from shoelaces and cables, but they also occur on the nanoscale in our cells. Knotted proteins can not only be used to assess the quality of structure predictions but also raise important questions about folding mechanisms and the evolution of proteins.