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Categories: Chemistry: Inorganic Chemistry, Physics: General

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Carbon nanostructures could become easier to design and synthesize thanks to a machine learning method that predicts how they grow on metal surfaces. The new approach will make it easier to exploit the unique chemical versatility of carbon nanotechnology.

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At low enough temperatures, certain metals lose their electrical resistance and they conduct electricity without loss. This effect of superconductivity is known for more than hundred years and is well understood for so-called conventional superconductors. More recent, however, are unconventional superconductors, for which it is unclear yet how they work.

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Researchers have developed a pioneering method to precisely manipulate ultrafast spin waves in antiferromagnetic materials using tailored light pulses.

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Researchers have outlined ambitious targets to help deliver a sustainable and net zero plastic economy. The authors argue for a rethinking of the technical, economic, and policy paradigms that have entrenched the status-quo, one of rising carbon emissions and uncontrolled pollution.

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An international team has developed a new method to make and manipulate a widely studied class of high-temperature superconductors. This technique should pave the way for the creation of unusual forms of superconductivity in previously unattainable materials.

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Every fluid -- from Earth's atmosphere to blood pumping through the human body -- has viscosity, a quantifiable characteristic describing how the fluid will deform when it encounters some other matter. If the viscosity is higher, the fluid flows calmly, a state known as laminar. If the viscosity decreases, the fluid undergoes the transition from laminar to turbulent flow. The degree of laminar or turbulent flow is referred to as the Reynolds number, which is inversely proportional to the viscosity. However, this Reynolds similitude does not apply to quantum superfluids. A researcher has theorized a way to examine the Reynolds similitude in superfluids, which could demonstrate the existence of quantum viscosity in superfluids.

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Nanoclusters (NCs) of transition metals like cobalt or nickel have widespread applications in drug delivery and water purification, with smaller NCs exhibiting improved functionalities. Downsizing NCs is, however, usually challenging. Now, scientists have demonstrated functional NC formation with atomic-scale precision. They successfully grew cobalt NCs on flat copper surfaces using molecular arrays as traps. This breakthrough paves the way for advancements like single-atom catalysis and spintronics miniaturization.

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Not all plastics are created the same, and some last longer in the ocean than others. Scientists have been working for years to quantify the environmental lifetimes of a wide range of plastic goods to see which have the shortest and longest lifespans in the ocean. To determine what plastics persist in the ocean, the team tests different products in large tanks that recreate the natural ocean environment.

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Scientists have discovered a first-of-its-kind material, a 3D crystalline metal in which quantum correlations and the geometry of the crystal structure combine to frustrate the movement of electrons and lock them in place.

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Global warming continues to pose a threat to human society and the ecological systems, and carbon dioxide accounts for the largest proportion of the greenhouse gases that dominate climate warming. To combat climate change and move towards the goal of carbon neutrality, researchers have developed a durable, highly selective and energy-efficient carbon dioxide (CO2) electroreduction system that can convert CO2 into ethylene for industrial purposes to provide an effective solution for reducing CO2 emissions.

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Artificial intelligence using neural networks performs calculations digitally with the help of microelectronic chips. Physicists have now created a type of neural network that works not with electricity but with so-called active colloidal particles.The researchers describe how these microparticles can be used as a physical system for artificial intelligence and the prediction of time series.

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Scientists have long strived to develop artificial molecular motors that can convert energy into directed motion. Researchers have now presented a solution to a challenging problem: how motion can be transferred in a controlled manner from one place to another through a 'molecular gear'. Molecular motors have the potential for use in, for example, energy storage applications and medicine.

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Lithium-ion batteries face safety concerns as a result of internal separator issues which often lead to short circuits. Scientists have now developed a method to improve the stability and properties of separators with a layer of silicon dioxide and other functional molecules. Batteries employing these separators demonstrated improved performance and reduced growth of disruptive root-like structures, paving the way for high-safety batteries that can aid the adoption of electric vehicles and advanced energy storage systems.

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Perchlorate compounds are known for their explosive nature. To understand what makes these compounds so explosive, a team of researchers developed a novel deep learning-based method that analyses their crystal structure and molecular interactions to elucidate their physical properties. This novel technique avoids dangerous laboratory-based experiments and uses data to study the nature of compounds. Overall, the study marks a significant step towards data-driven and artificial intelligence-based methods for chemical research.

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Researchers were able to control the growth of thin, branching networks that support cellular structure and help cells function. The networks, called microtubules, can exert force and precisely transport chemicals at a subcellular level.

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Chemists have developed an autonomous chemical synthesis robot with an integrated AI-driven machine learning unit. Dubbed 'RoboChem', the benchtop device can outperform a human chemist in terms of speed and accuracy while also displaying a high level of ingenuity. As the first of its kind, it could significantly accelerate chemical discovery of molecules for pharmaceutical and many other applications.

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A newly evolved enzyme could one day make silicone compounds biodegradable.

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The branch of mathematics known as topology has become a cornerstone of modern physics thanks to the remarkable -- and above all reliable -- properties it can impart to a material or system. Unfortunately, identifying topological systems, or even designing new ones, is generally a tedious process that requires exactly matching the physical system to a mathematical model. Researchers have demonstrated a model-free method for identifying topology, enabling the discovery of new topological materials using a purely experimental approach.

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Emerging research suggests it may be easier to use fusion as a power source if liquid lithium is applied to the internal walls of the device housing the plasma. Past experiments studied solid lithium coatings and found they could enhance a plasma. The researchers were pleased they could yield similar results with liquid lithium, as it's better suited for use in a large-scale tokamak.

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Certain materials have desirable properties that are hidden and scientists can use light to uncover these properties. Researchers have used an advanced optical technique, based on terahertz time-domain spectroscopy, to learn more about a quantum material called Ta2NiSe5 (TNS).