A “sex peptide” transferred from male to female fruit flies during mating interferes with the female’s biological clock, reducing her chances of mating again.
Studying the large-scale structure of our galaxy isn’t easy. We don’t have a clear view of the Milky Way’s shape and features like we do of other galaxies, largely because we live within it. But we do have some advantages. From within, we’re able to carry out close-up surveys of the Milky Way’s stellar population and its chemical compositions. That gives researchers the tools they need to compare our own galaxy to the many millions of others in the Universe.
This week, an international team of researchers from the USA, UK, and Chile released a paper that does just that. They dug through a catalogue of ten thousand galaxies produced by the Sloan Digital Sky Survey, searching for galaxies with similar attributes to our own.
They discovered that the Milky Way has twins – many of them – but just as many that are only superficially similar, with fundamental differences buried in the data. What they discovered has implications for the future evolution of our own galaxy.
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A research group led by Prof. Wu Kaifeng from the Dalian Institute of Chemical Physics (DICP), Chinese Academy of Sciences recently reported the successful initialization, coherent quantum-state control, and readout of spins at room temperature using solution-grown quantum dots, which represents an important advance in quantum information science.
The study was published in Nature Nanotechnology on Dec 19th.
Continue reading “Coherent manipulation of spin qubits at room temperature” »
A smattering of stars scattered throughout the center of the Milky Way is the remnants of the ancient galactic core, when our galaxy was still new.
Using measurements from the most accurate three-dimensional map of the galaxy ever compiled, as well as a neural network to probe the chemical compositions of over 2 million stars, a team of astronomers have identified 18,000 stars from our galaxy’s infancy, when it was just a compact collection of proto-galaxies coming together to dream of bigger things.
Hints of this stellar population have been identified in previous studies.
The world’s best artists can take a handful of differently colored paints and create a museum-worthy canvas that looks like nothing else. They do so by drawing upon inspiration, knowledge of what’s been done in the past and design rules they learned after years in the studio.
Chemists work in a similar way when inventing new compounds. Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory, Northwestern University and The University of Chicago have developed a new method for discovering and making new crystalline materials with two or more elements.
“We expect that our work will prove extremely valuable to the chemistry, materials and condensed matter communities for synthesizing new and currently unpredictable materials with exotic properties,” said Mercouri Kanatzidis, a chemistry professor at Northwestern with a joint appointment at Argonne.
In physics, weak microwave signals can be amplified with minimal added noise. For instance, artificial quantum systems based on superconducting circuits can amplify and detect single microwave patterns, although at millikelvin temperatures. Researchers can use natural quantum systems for low-noise microwave amplification via stimulated emission effects; however, they generate a higher noise at functionalities greater than 1 Kelvin.
In this new work, published in the journal Science Advances, Alexander Sherman and a team of scientists in chemistry at the Technical-Israel Institute of Technology, Haifa, used electron spins in diamond as a quantum microwave amplifier to function with quantum-limited internal noise above liquid nitrogen temperatures. The team reported details of the amplifier’s design, gain, bandwidth, saturation power and noise to facilitate hitherto unavailable applications in quantum science, engineering and physics.
Water has puzzled scientists for decades. For the last 30 years or so, they have theorized that when cooled down to a very low temperature like-100C, water might be able to separate into two liquid phases of different densities. Like oil and water, these phases don’t mix and may help explain some of water’s other strange behavior, like how it becomes less dense as it cools.
It’s almost impossible to study this phenomenon in a lab, though, because water crystallizes into ice so quickly at such low temperatures. Now, new research from the Georgia Institute of Technology uses machine learning models to better understand water’s phase changes, opening more avenues for a better theoretical understanding of various substances. With this technique, the researchers found strong computational evidence in support of water’s liquid-liquid transition that can be applied to real-world systems that use water to operate.
“We are doing this with very detailed quantum chemistry calculations that are trying to be as close as possible to the real physics and physical chemistry of water,” said Thomas Gartner, an assistant professor in the School of Chemical and Biomolecular Engineering at Georgia Tech. “This is the first time anyone has been able to study this transition with this level of accuracy.”
The Donnan electric potential arises from an imbalance of charges at the interface of a charged membrane and a liquid, and for more than a century it has stubbornly eluded direct measurement. Many researchers have even written off such a measurement as impossible.
But that era, at last, has ended. With a tool that’s conventionally used to probe the chemical composition of materials, scientists at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) recently led the first direct measurement of the Donnan potential.
“We were naïve enough to believe we could do the impossible,” said Ethan Crumlin, a staff scientist at Berkeley Lab’s Advanced Light Source (ALS), which generated the bright X-rays used in the experiment. Crumlin and his collaborators recently reported the measurement in Nature Communications.
A new machine learning model will help scientists identify small molecules, with applications in medicine, drug discovery and environmental chemistry. Developed by researchers at Aalto University and the University of Luxembourg, the model was trained with data from dozens of laboratories to become one of the most accurate tools for identifying small molecules.
Thousands of different small molecules, known as metabolites, transport energy and transmit cellular information throughout the human body. Because they are so small, metabolites are difficult to distinguish from each other in a blood sample analysis—but identifying these molecules is important to understand how exercise, nutrition, alcohol use and metabolic disorders affect well-being.
Metabolites are normally identified by analyzing their mass and retention time with a separation technique called liquid chromatography followed by mass spectrometry. This technique first separates metabolites by running the sample through a column, which results in different flow rates—or retention times—through the measurement device.
The cloud condensation nuclei activation of sea spray aerosol (SSA) is tightly linked to the hygroscopic properties of these particles and is defined by their physical and chemical properties. While hygroscopic sea salt in SSA strongly influences particle water uptake, the marine-derived components that make up the organic fraction of SSA constitute a complex mixture, and their effect on hygroscopic growth is unknown. To constrain the effect of organic compounds and specifically surface-active compounds that adsorb on particle interfaces, particle hygroscopic growth studies were performed on laboratory-generated model sea salt/sugar particles. For sea salt/glucose particles, ionic surfactants facilitated water uptake at low relative humidity (RH), increasing the particle growth factor (GF) by up to 7.61%, and caused a reduction in the deliquescence relative humidity (DRH), while nonionic surfactants had a minimal effect. Replacing glucose with polysaccharide laminarin in sea salt/sugar/surfactant particles caused a reduction in GF at low RHs and minimized the effect of ionic surfactants on the DRH. At RHs above the DRH, the addition of anionic or nonionic surfactants caused a decrease in GF for both sea salt/glucose and sea salt/laminarin particles. The addition of cationic surfactants, however, did not have a dampening effect on water uptake of sea salt/sugar particles and even showed a GF increase of up to 3.7% at 90% RH. An increase in the complexity of the sugar dampens the water uptake for particles containing nonionic surfactants but increases the water uptake for cationic surfactants. The cloud activation potential for 100 nm particles analyzed in this study is higher for ionic surfactants and decreases with an increase in surfactant molecular size when particle interfacial tension is considered. The surfactant effect on the hygroscopic growth and cloud activation potential of the particles containing sea salt/sugar is dependent on the surfactant ionicity and molecular size, the particle size and interfacial tension, and the interactions between inorganic salt and organic species under different RH conditions.
