ICG Highlights

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  • (2020) Research Endeavors, Cross-Cultural Experiences, and Educational Activities

    The Wisconsin MRSEC Research Experience for Teachers (RET) program is a cross-cultural collaboration with the University of Puerto Rico-Mayagüez (UPRM). The RET program provides science teachers from Wisconsin and Puerto Rico an authentic research and cultural literacy professional development experience. The program culminates with an in-person capstone week where the RETs from both sites share the research-inspired classroom activities they have developed and learn from one another about teaching in a different cultural and geographic environment. The teachers learn about the area and culture that is unique to the site hosting the capstone.

  • (2020) Solid-phase Epitaxy Produces Magnetic Oxides with Novel Magnetic Properties

    The Wisconsin MRSEC has created thin films of a fascinating magnetic material, Pr2Ir2O7, in which the magnetic moments are frustrated: No matter how they are arranged, some of the moments are always fighting to change their direction, like two bar magnets with their north poles shoved together. Frustration creates a rich landscape for discovery and manipulation of new magnetic effects and of electronic phenomena linked to magnetism.

  • (2020) Order From Disorder: Molecular Packing in Glasses

    Using physical vapor deposition, researchers produced glassy films that are smooth and uniform, but which also have the molecules aligned with one another and organized in layers. This added structure could make the glass more efficient for conductors and expand the range of materials that can be used in future organic electronics.  The colorful images in the figure show measurements using synchrotron x-rays that contrast the disordered starting material and the ordered glass.

  • (2020) Why Sound Waves Travel So Far Unimpeded in Glasses at Low Temperatures

    IRG 1 showed how the atoms around the defects can restrict their ability to jump between configurations and how defects can talk to each other via sound waves. Both phenomena keep the defects from interfering with sound waves allowing the waves to travel long distances.

  • (2020) Wisconsin MRSEC Excellence in Open Science Prize

    The Wisconsin MRSEC is committed to being a leader in Open Science, which shares data in digital forms following FAIR1 principles. As part of these efforts the Center has developed a new web site, a best practices guide, and held informational events. This year the Center launched the first Wisconsin MRSEC Excellence in Open Science Prize. The winner was graduate student Bradley Dallin2 for his work on molecules interacting with water, with potential applications from understanding human blood to protein folding diseases like Alzheimer’s. Bradley shared his results in papers, but also shared all his simulations and tools in an open accessible format for the community, increasing the impact of his work.

  • (2020) In Situ Synchrotron Radiation Instrumentation for Challenging Problems in Oxide Crystallization

    Researchers at the Wisconsin MRSEC have developed a new instrument using very bright synchrotron x-ray beams to watch nanoscale crystals as they grow. The system has a unique design that allows the crystals to grow in vacuum while keeping the x-ray lenses and the x-ray beam in air but bringing them very close to the crystal. Wisconsin MRSEC researchers are using this new instrument to learn about solid phase epitaxy, a process based on the growth of ordered crystals from a disordered amorphous solid, which is capable of creating new materials for applications in electronics, optics, sensors, and quantum information.

  • (2020) Machine Learning Algorithms

    The Wisconsin MRSEC has developed machine learning techniques that enable the design of new toxin sensors using liquid crystal droplets that respond to the presence of different bacterial toxins and at extremely low concentrations by changing shape and appearance. Machine learning enables computers to automatically analyze the droplet responses to measure toxin concentration and type automatically at high accuracy. More generally, these results demonstrate that the machine learning approach can quickly extract valuable information from complex datasets.

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