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Materials Science Research Lecture

Wednesday, November 2, 2022
4:00pm to 5:00pm
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Noyes 147 (J. Holmes Sturdivant Lecture Hall)
Impact of Local Disorder on Materials Properties studied by Single Crystal Diffuse Scattering
Stephan Rosenkranz, Sr. Group Leader, Sr. Physicist, Argonne National Laboratory,

NOTE! Every student or postdoc (any option!) will receive a $5 SmartCash "coffee credit" for each Materials Research lecture attended, in person or online. The credits will be tallied and issued after the last speaker of the term. *If you attend in person be sure to put your name on the sign-in sheet so you are counted.

Link to join Webinar:

Webinar ID 850 1041 3991


Many phenomena of scientific and technological interest derive from the presence of local disorder and nanoscale correlations embedded within a long-range ordered crystalline structure. Such complex disorder often leads to strongly enhanced responses to external stimuli, with properties desirable for future applications. Examples include relaxor behavior, thermoelectricity, ionic conduction, colossal magnetoresistance, unconventional superconductivity, negative thermal expansion, and more. Detailed insight into the existence and morphology of local distortions and short-range correlations is provided by single crystal diffuse scattering. In particular, recent developments in instrumentation and novel analysis methods now provide the ability to efficiently measure diffuse scattering intensities from single crystals over large volumes of reciprocal space and enables new ways of analyzing the data in real space through three-dimensional pair-distribution functions. I will illustrate these methods on a variety of recent research examples where they provided model-free imaging of intercalant ion correlations in NaxV2O5, revealed frustrated structural transition across the metal-insulator transition in MoxVO2, and enabled the reconstruction of magnetic correlations in frustrated magnets. I will further present ongoing developments of the technique and recent advances in applying Machine Learning methods to volumes of scattering data measured as a function of temperature, which can provide rapid insight on information hidden in this large data sets.

More about the Speaker:

Stephan Rosenkranz is a Senior Physicist and leader of the Neutron and X-ray Scattering group in the Materials Science Division at Argonne National Laboratory. Both his undergraduate education and his PhD in Experimental Physics was at ETH Zurich. He then performed postdoctoral research at Argonne and at the University of Illinois in Chicago before joining Argonne as Staff Scientist in the neutron and x-ray scattering group. His research focuses on utilizing neutron and X-ray scattering techniques to study properties of materials that emerge from the presence of complex disorder and short-range correlations, induced as a response to the subtle balance among competing interactions involving spin, charge, orbital, and strain degrees of freedom.

For more information, please contact Jennifer Blankenship by email at [email protected].