Lifeboat Foundation

Astronomers at the University of Toronto (U of T) have discovered the first pairs of white dwarf and main sequence stars—” dead” remnants and “living” stars—in young star clusters. Described in a new study published in The Astrophysical Journal, this breakthrough offers new insights into an extreme phase of stellar evolution, and one of the biggest mysteries in astrophysics.

Scientists can now begin to bridge the gap between the earliest and final stages of binary star systems—two stars that orbit a shared center of gravity—to further our understanding of how stars form, how galaxies evolve, and how most elements on the periodic table were created. This discovery could also help explain cosmic events like supernova explosions and gravitational waves, since binaries containing one or more of these compact dead stars are thought to be the origin of such phenomena.

Most stars exist in binary systems. In fact, nearly half of all stars similar to our sun have at least one companion star. These paired stars usually differ in size, with one star often being more massive than the other. Though one might be tempted to assume that these stars evolve at the same rate, more massive stars tend to live shorter lives and go through the stages of stellar evolution much faster than their lower mass companions.

Surfaces play a key role in numerous chemical reactions, including catalysis and corrosion. Understanding the atomic structure of the surface of a functional material is essential for both engineers and chemists. Researchers at Nagoya University in Japan used atomic-resolution secondary electron (SE) imaging to capture the atomic structure of the very top layer of materials to better understand the differences from its lower layers. The researchers published their findings in the journal Microscopy.

Some materials exhibit “surface reconstruction,” where the surface atoms are organized differently from the interior atoms. To observe this, especially at the atomic level, surface-sensitive techniques are needed.

Traditionally, scanning electron microscopy (SEM) has been an effective tool to examine nanoscale structures. SEM works by scanning a sample with a focused electron beam and capturing the SEs emitted from the surface. SEs are typically emitted from a shallow depth below the surface, making it difficult to observe phenomena like surface reconstruction, especially if only a single atomic layer is involved.

Fermium studies indicate nuclear shell effects diminish as nuclear mass increases, emphasizing macroscopic influences in superheavy elements.

Where does the periodic table of chemical elements end and which processes lead to the existence of heavy elements? An international research team has conducted experiments at the GSI/FAIR accelerator facility and at Johannes Gutenberg University Mainz to investigate these questions.

Their research, published in the journal Nature, provides new insights into the structure of atomic nuclei of fermium (element 100) with different numbers of neutrons. Using forefront laser spectroscopy techniques, the team traced the evolution of the nuclear charge radius and found a steady increase as neutrons were added to the nuclei. This indicates that localized nuclear shell effects have a reduced influence on the nuclear charge radius in these heavy nuclei.

As energy from the sun reaches Earth, some solar radiation is absorbed by the atmosphere, leading to chemical reactions like the formation of ozone and the breakup of gas molecules. A new approach for modeling these reactions, developed by a team led by scientists at Penn State, may improve our understanding of the atmosphere on early Earth and help in the search for habitable conditions on planets beyond our solar system.

The researchers have reported in the journal JGR Atmospheres that using a statistical method called correlated-k can improve existing photochemical models used to understand conditions on early Earth.

The approach can help scientists better understand the atmospheric composition of early Earth and will play an important role as new observatories come online in the coming decades that can provide new data on exoplanet atmospheres, the scientists said.

Can weight loss leave a lasting imprint on our fat cells?

Losing weight is often touted as a cornerstone of better health, particularly for people dealing with obesity and its associated health risks.

Anyone who has ever tried to get rid of a few extra kilos knows the frustration: the weight drops initially, only to be back within a matter of weeks—the yo-yo effect has struck. Researchers at ETH Zurich have now been able to show that this is all down to epigenetics.

Continue reading “Fat cells have epigenetics-based memory: Researchers discover mechanism behind weight loss yo-yo effect” »

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have used machine learning and supercomputer simulations to investigate how tiny gold nanoparticles bind to blood proteins. The studies discovered that favorable nanoparticle-protein interactions can be predicted from machine learning models that are trained from atom-scale molecular dynamics simulations. The new methodology opens ways to simulate the efficacy of gold nanoparticles as targeted drug delivery systems in precision nanomedicine.

Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano–bio interface.

Modeling the properties of the nano-bio interface is demanding since the important processes such as electronic charge transfer, chemical reactions or restructuring of the biomolecule surface can take place in a wide range of length and time scales, and the atomistic simulations need to be run in the appropriate aqueous environment.

Researchers from the University of Bonn have trained an AI process to predict potential active ingredients with special properties. Therefore, they derived a chemical language model — a kind of ChatGPT for molecules. Following a training phase, the AI was able to exactly reproduce the chemical structures of compounds with known dual-target activity that may be particularly effective medications. The study has now been published in Cell Reports Physical Science.

Anyone who wants to delight their granny with a poem on her 90th birthday doesn’t need to be a poet nowadays: A short prompt in ChatGPT is all it takes, and within a few seconds the AI spits out a long list of words that rhyme with the birthday girl’s name. It can even produce a sonnet to go with it if you like.

Researchers at the University of Bonn have implemented a similar model in their study — known as a chemical language model. This does not, however, produce rhymes. Instead, the AI displays the structural formulas of chemical compounds that may have a particularly desirable property: They are able to bind to two different target proteins. In the organism, this means, for example, they can inhibit two enzymes at once.

Water, a molecule essential for life, exhibits unusual properties—referred to as anomalies—that define its behavior. Despite extensive study, many mysteries remain about the molecular mechanisms underlying these anomalies that make water unique. Deciphering and replicating this distinctive behavior across various temperature ranges remains a significant challenge for the scientific community.

Now, a study presents a new theoretical model capable of overcoming the limitations of previous methodologies to understand how water behaves in extreme conditions. The paper, featured on the cover of The Journal of Chemical Physics, is led by Giancarlo Franzese and Luis Enrique Coronas, from the Faculty of Physics and the Institute of Nanoscience and Nanotechnology of the University of Barcelona (IN2UB).

The study not only broadens our understanding of the physics of water, but also has implications for technology, biology and biomedicine, in particular for addressing the treatment of neurodegenerative diseases and the development of advanced biotechnologies.

A research team led by Dr. Hyung-Suk Oh and Dr. Woong Hee Lee at the Clean Energy Research Center at Korea Institute of Science and Technology (KIST) has developed a silver-silica composite catalyst capable of reversible local pH control through a silica-hydroxide cycle, inspired by Earth’s natural cycles.