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Categories: Chemistry: Organic Chemistry, Engineering: Robotics Research

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Chemists have extended a powerful molecule-building method -- called C-H activation -- to the broad class of chemicals known as alcohols.

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A recent study has unveiled a significant breakthrough in photoelectrode development.

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A team of scientists developed a new machine learning model for discovering critical-element-free permanent magnet materials based on the predicted Curie temperature of new material combinations.

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Important progress for RNA research: A team has discovered a new ribozyme that can label RNA molecules in living cells.

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Lithium-ion batteries (LIBs) provide our portable devices like tablets and mobiles -- and increasingly also vehicles -- with power. As the share of volatile renewable energy needing electricity storage increases, more and more LIBs are needed, lithium prices rise, resources dwindle, and the amount of depleted batteries that contain toxic substances increases. Researchers introduce a novel approach for the recovery of lithium from used LIBs.

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A 'living material,' made of a natural polymer combined with genetically engineered bacteria, could offer a sustainable and eco-friendly solution to clean pollutants from water. Researchers developed their living material using a seaweed-based polymer and bacteria that have been programmed to produce an enzyme that transforms various organic pollutants into harmless compounds. In tests, heir material decontaminated water solutions tainted with a pollutant from textile manufacturing: indigo carmine, a blue dye that is used to color denim.

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Hydrogen spillover is exactly what it sounds like. Small metal nanoparticles anchored on a thermally stable oxide, like silica, comprise a major class of catalysts, which are substances used to accelerate chemical reactions without being consumed themselves. The catalytic reaction usually occurs on the reactive -- and expensive -- metal, but on some catalysts, hydrogen atom-like equivalents literally spill from the metal to the oxide. These hydrogen-on-oxide species are called 'hydrogen spillover.'

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An artificial intelligence with the ability to look inward and fine tune its own neural network performs better when it chooses diversity over lack of diversity, a new study finds. The resulting diverse neural networks were particularly effective at solving complex tasks.

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The day when robots can cook dinner, clear the kitchen table and empty the dishwasher is still a long way off. First, robots need to be able to recognize the many items of different sizes, shapes and brands in our homes. A team has now made a significant advance toward that technology with a robotic system that uses artificial intelligence to help robots better identify and remember objects.

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By controlling the arrangement of multiple inorganic and organic layers within crystals using a novel technique, researchers have shown they can control the energy levels of electrons and holes (positive charge carriers) within a class of materials called perovskites. This tuning influences the materials' optoelectronic properties and their ability to emit light of specific energies, demonstrated by their ability to function as a source of lasers.

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A lab is developing more accurate and powerful liquid biopsy technologies that take advantage of signals from RNA 'dark matter,' an understudied area of the genome.

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Researchers have been able to substantially improve photoclick chemistry. They were able to boost the reactivity of the photoclick compound in the popular PQ-ERA reaction through strategic molecular substitution. They now report a superb photoreaction quantum yield, high reaction rates and notable oxygen tolerance.

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Research teams have detailed a pioneering breakthrough in medical device technology that could lead to intelligent, long-lasting, tailored treatment for patients, thanks to soft robotics and artificial intelligence.

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In a milestone for artificial intelligence (AI), the AI system 'Swift' has beaten the world champions in drone racing -- a result that seemed unattainable just a few years ago. The AI-piloted drone was trained in a simulated environment. Real-world applications include environmental monitoring or disaster response.

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Machine learning model provides quick method for determining the composition of solid chemical mixtures using only photographs of the sample.

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Even when humans see AI-based assistants purely as tools, they ascribe partial responsibility for decisions to them, as a new study shows.

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In the atmosphere, gaseous sulfuric acid can form particles that influence the physical properties of clouds. Thus, the formation of sulfuric acid in the gas phase directly affects the radiative forcing and Earth's climate. In addition to the known formation from sulfur dioxide, researchers have now been able to demonstrate through experiments that there is another formation pathway that has been speculated about for decades. Sulfuric acid in the atmosphere can also be formed directly by the oxidation of organic sulfur compounds. This new production pathway can be responsible for up to half of the gaseous sulfuric acid formation over the oceans and is thus of high importance for climate projections -- especially over the oceans of the Southern Hemisphere.

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Molecular photoswitches that can both convert and store energy could be used to make solar energy harvesting more efficient. A team of researchers has used a quantum computing method to find a particularly efficient molecular structure for this purpose. Their procedure was based on a dataset of more than 400,000 molecules, which they screened to find the optimum molecular structure for solar energy storage materials.

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Imagine a robot that can wedge itself through the cracks in rubble to search for survivors trapped in the wreckage of a collapsed building. Engineers are working toward to that goal with CLARI, short for Compliant Legged Articulated Robotic Insect.

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A scientist combines attention neural networks with graph neural networks to better understand and design proteins. The approach couples the strengths of geometric deep learning with those of language models to predict existing protein properties and envision new proteins that nature has not yet devised. The model turns numbers, descriptions, tasks, and other elements into symbols for neural networks to use.