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IMAGE: SEM micrograph of vertically standing, flat micromirror array with an inset of magnified area. Credit: Hillmer et al. view more
Credit: Hillmer et al.
Buildings are responsible for 40 percent of primary energy consumption and 36 percent of total CO2 emissions. And, as we know, CO2 emissions trigger global warming, sea level rise, and profound changes in ocean ecosystems. Substituting the inefficient glazing areas of buildings with energy efficient smart glazing windows has great potential to decrease energy consumption for lighting and temperature control.
Harmut Hillmer et al. of the University of Kassel in Germany demonstrate that potential in MOEMS micromirror arrays in smart windows for daylight steering, a paper published recently in the inaugural issue of the
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Scientists at the U.S. Department of Energy s Ames Laboratory and their partners from Clemson University have discovered a green, low-energy process to break down polystyrene, a type of plastic that is widely used in foam packaging materials, disposable food containers, cutlery, and many other applications.
Polystyrene is part of a much larger global plastic waste problem. Hundreds of millions metric tons of polymers are produced each year, a large majority of which is discarded after use. Due to the chemical stability and durability of industrial polymers, plastic waste does not easily degrade in landfills and is often burned, which produces carbon dioxide and other hazardous gases. In order to stop the growing flood of polymer waste and reduce carbon dioxide emissions, plastics have to be recycled or converted into new value-added products.
Credit: POSTECH
Alchemy, which attempted to turn cheap metals such as lead and copper into gold, has not yet succeeded. However, with the development of alloys in which two or three auxiliary elements are mixed with the best elements of the times, modern alchemy can produce high-tech metal materials with high strength, such as high entropy alloys. Now, together with artificial intelligence, the era of predicting the crystal structure of high-tech materials has arrived without requiring repetitive experiments.
A joint research team of Professor Ji Hoon Shim and Dr. Taewon Jin (first author, currently at KAIST) of POSTECH s Department of Chemistry, and Professor Jaesik Park of POSTECH Graduate School of Artificial Intelligence have together developed a system that predicts the crystal structures of multi-element alloys with expandable features without needing massive training data. These research findings were recently published in
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IMAGE: The software can detect defects in the printed parts and determine where in the printer the defects occur. view more
Credit: University of Illinois Urbana-Champaign
Researchers at University of Illinois Urbana-Champaign developed software to improve the accuracy of 3D-printed parts, seeking to reduce costs and waste for companies using additive manufacturing to mass produce parts in factories. Additive manufacturing is incredibly exciting and offers tremendous benefits, but consistency and accuracy on mass-produced 3D-printed parts can be an issue. As with any production technology, parts built should be as close to identical as possible, whether it is 10 parts or 10 million, said Professor Bill King, Andersen Chair in the Department of Mechanical Science and Engineering and leader of the project.