Physical participation will be subject to existing COVID-19 guidelines at the time of the lecture.

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Espen Drath Bøjesen, Interdisciplinary Nanoscience Center, Aarhus University

Bridging the Gap Between Order and Disorder: On the road towards statistical (Scanning) Transmission Electron Microscopy

Established methods for the determination of crystal structures have been an unprecedented success in advancing knowledge in the field of materials science. Yet, crucial structure-function relationships cannot be elucidated for many important materials of technological and scientific interest that “occupy the complex middle ground between liquid-like randomness and crystalline periodic order”.¹ Isolated, neither the average structure obtained by X-ray diffraction nor atomic-level detailed information from conventional (scanning) transmission electron microscopy ((S)TEM) is sufficient to unlock the structural complexity of such materials.

In this talk I will present selected recent work which demonstrates the benefits of combining the merits of broad beam diffraction methods, e.g. X-ray total scattering, with a range of different “classical” electron microscopy-based techniques to unearth new details about the structural intricacies of metal oxides and metal nanoparticles.²

Furthermore, I will introduce and demonstrate some of the strengths of two of the major driving forces behind recent breakthroughs within materials (S)TEM, i.e. 4D-STEM³ and the use of tools from big data science for data analysis. In a 4D-STEM experiment the entire electron diffraction pattern is collected at each probe position – this is in stark contrast to conventional STEM where a single integrated count is recorded for each probe position. Thus, the amount of information in every single 4D-STEM experiment is staggering and this calls for new ways of handling, storing and analysing data. I will give examples of the versatility of 4D-STEM for studying materials with varying degree of order, i.e. metallic glasses, activated carbon and relaxor ferroelectrics, and discuss/illustrate how this approach, when combined with clever data analysis methods, may push STEM towards a more “statistical nature”.^4,5

1: Keen et al., Nature, 2015, 521, 303–309.
2: Christiansen, Bøjesen, et al., ACS Nano, 2019, 13, 8725–8735.  & Mathiesen, Bøjesen et al., in preparation.
3: Ophus, Microscopy and Microanalysis, 2019, 25, 1–20.
4: Martin, Bøjesen et al., Small, 2020, 2000828.
5: Bojesen et al., Journal of Physics: Materials, 2020, 3, 044002.