Scientific Events

The NanoSIMS is emerging as a powerful tool to study complex problems in materials science. The NanoSIMS is a high-resolution secondary ion mass spectrometry instrument capable of chemical mapping at 100 nm spatial resolution, detection limits in the ppm range and is able to detect almost all elements in the periodic table as well as isotopes. In this seminar I will show how we have been using the NanoSIMS to image localised deuterium in electrochemically charged steel and nickel alloys as well as in zirconium alloys oxidised in an autoclave to simulate nuclear reactor conditions. I will explain how isotopic tracers, such as 18O and deuterium, can be used to avoid imaging artefacts and provide temporal information. Some of the complexities associated with detecting hydrogen and deuterium in the NanoSIMS will be discussed. [more]

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Electrochemical Energy Storage, in particular Li-Ion Batteries, have become one of the most important technological cornerstones for the current energy transition. The further development and progress in existing technology will depend on both, the introduction of new active electrode materials and the better understanding and mitigation of existing materials challenges. After an introduction in battery technology and the used materials, I will focus on a few examples where advanced characterization is able to bring new insights and better understanding of performance and degradation mechanisms. [more]

Bulk-type (inorganic) solid-state batteries are a promising next-generation energy-storage technology with the prospect of improving safety and enabling higher energy densities than conventional lithium-ion batteries [1]. Especially high-capacity, layered oxide cathode materials (NCM or related) and lithium thiophosphate superionic solid electrolytes are currently being considered for solid-state battery applications (at the positive electrode side). However, interfacial side reactions and chemo-mechanical degradation during cycling operation are major obstacles toward commercialization of “practical” cells. In this presentation, I will demonstrate the importance of tailoring Ni-rich NCM materials in terms of size and composition, among others, for improving the cycling performance of pelletized and slurry-cast cathodes [2-4]. In addition, I will show recent findings on the effects that coating chemistry and morphology have on the side reactions, including gas evolution, in high-loading cells [4-6]. [1] J. Janek, W.G. Zeier, Nat. Energy, 1 (2016) 16141. [2] F. Strauss, T. Bartsch, L. de Biasi, A.-Y. Kim, J. Janek, P. Hartmann, T. Brezesinski, ACS Energy Lett., 3 (2018) 992. [3] F. Strauss, L. de Biasi, A.-Y. Kim, J. Hertle, S. Schweidler, J. Janek, P. Hartmann, T. Brezesinski, ACS Mater. Lett., 2 (2020) 84. [4] J.H. Teo, F. Strauss, D. Tripković, S. Schweidler, Y. Ma, M. Bianchini, J. Janek, T. Brezesinski, Cell Rep. Phys. Sci., 2 (2021) 100465. [5] T. Bartsch, F. Strauss, T. Hatsukade, A. Schiele, A.-Y. Kim, P. Hartmann, J. Janek, T. Brezesinski, ACS Energy Lett., 3 (2018) 2539. [6] F. Strauss, J.H. Teo, J. Maibach, A.-Y. Kim, A. Mazilkin, J. Janek, T. Brezesinski, ACS Appl. Mater. Interfaces, 12 (2020) 57146.

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in areas ranging from materials synthesis to imaging. The implementation of this vision requires the synergy of three intertwined components, including the engineering controls of the physical tools, the algorithmic developments that allow to define non-trivial exploratory patterns, and physics-driven interpretation. In this presentation, I will discuss recent progress in automated experiment in electron and scanning probe microscopy, ranging from feature finding to physics discovery via active learning. The applications of classical deep learning methods in streaming image analysis are strongly affected by the out of distribution drift effects, and the approaches to minimize though are discussed. We further present invariant variational autoencoders as a method to disentangle affine distortions and rotational degrees of freedom from other latent variables in imaging and spectral data. The analysis of the latent space of autoencoders further allows establishing physically relevant transformation mechanisms. Extension of encoder approach towards establishing structure-property relationships will be illustrated on the example of plasmonic structures. Finally, I illustrate transition from post-experiment data analysis to active learning process. Here, the strategies based on simple Gaussian Processes often tend to produce sub-optimal results due to the lack of prior knowledge and very simplified (via learned kernel function) representation of spatial complexity of the system. Comparatively, deep kernel learning (DKL) methods allow to realize both the exploration of complex systems towards the discovery of structure-property relationship, and enable automated experiment targeting physics (rather than simple spatial feature) discovery. The latter is illustrated via experimental discovery of the edge plasmons in STEM/EELS and ferroelectric domain dynamics in PFM. This research is supported by the by the U.S. Department of Energy, Basic Energy Sciences, Materials Sciences and Engineering Division and the Center for Nanophase Materials Sciences, which is sponsored at Oak Ridge National Laboratory by the Scientific User Facilities Division, BES DOE. [more]