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1 0 A D V A N C E D M A T E R I A L S & P R O C E S S E S | J U L Y / A U G U S T 2 0 1 9 University, Japan, are investigating polymers that change their color or flu- orescence characteristics when placed under mechanical load. The prevailing approach to achiev- ing this function is based on specially designed sensor molecules that contain weak chemical bonds that break when the applied mechanical force exceeds a certain threshold. This effect can cause a color change or other predefined response. A fundamental limitation of this approach, however, is that the weak bonds can also break upon expo- sure to light or heat. This lack of speci- ficity reduces the practical usefulness of stress-indicating polymers. Typically, it also makes the effect irreversible. The researchers devised a new type of sensor molecule that can only be activated by mechanical force. Unlike previous force-transducing mol- ecules, no chemical bond breaking takes place. Instead, the new sensor molecules consist of two parts that mechanically interlock. This inter- connection prevents the separation TESTING | CHARACTERIZATION MACHINE LEARNING FOR POROUS MATERIALS Researchers at the University of Cambridge, U.K., are using machine learning techniques to accurately pre- dict the mechanical properties of met- al-organic frameworks (MOFs), which could be used to extract water from desert air, store dangerous gases, or power hydrogen-based cars. The team used their machine learning algorithm to predict the properties of more than 3000 existing MOFs, as well as MOFs that are yet to be synthesized in the laboratory. The results could accelerate the way materials are characterized and designed at the molecular scale. Unlike plastics, MOFs have orderly crystal- line structures that grow in all direc- tions instead of just one. This structure means that MOFs can be made like building blocks—individual atoms or molecules can be switched in or out of the structure, a level of precision nearly impossible to achieve with plastics. MOFs are synthe- sized in powder form, but in order to be of practical use, the powder must be formed into larger pellets using pressure. Due to their porosity, many MOFs are crushed in this process, wasting resources. To address this prob- lem, the Cambridge re- searchers and their collaborators from Belgium and the U.S. developed a machine learning algorithm to predict the mechanical properties of thou- sands of MOFs, so that only those with the necessary mechanical stability are manufactured. The team used a multilevel computational approach in order to build an interactive map of the structural and mechanical landscape of MOFs. They also launched an interac- tive website where scientists can design and predict the performance of their own MOFs. They say the tool will help close the gap between experimentalists and computationalists working in this area. www.cam.ac.uk. DAMAGE-INDICATING POLYMERS A team of international research- ers developed a method to tailor the properties of stress-indicating mole- cules that can be integrated into poly- mers and signal damage or excessive mechanical loads with an optical sig- nal. Researchers at the University of Fribourg, Switzerland, and Hokkaido Crystalline metal-organic framework. Courtesy of David Fairen-Jimenez. The acquisition of B&W Tek LLC by Metrohm AG, Switzerland, in July 2018 created one of the largest providers for applied Raman spectroscopy. In May 2019, Metrohm USA and Metrohm Canada began selling B&W Tek mobile spectroscopy equipment. bwtek.com , metrohmag.com . BRIEFS Donaldson Company Inc., Minneapolis, a manufacturer of filtration products and solutions, recently broke ground on a $15 million materials research center at its corporate head- quarters in Bloomington, Minn. donaldson.com . A newly developed polymer switches its fluorescence on and off in response to mechanical stress. Courtesy of Hokkaido University.