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Machine Learning is increasingly being exploited on campus in various scientific fields, projects, groups and colleagues. Applications encompass for example autonomous accelerators, segmentation of bio-degradable bone implants. particle physics simulations and track detection, automated processing of electron microscopy images. These pages give a brief overview of AI related activities at DESY, and offer opportunities for groups and projects to present themselves and their research fields.

Note: This is currently a test space without much real content.

Recent Science Highlights

Sample for blogs. See below for the latest three highlights. Add your own scientific highlight!

Gram-negative pathogens evolved a syringe-like nanomachine, termed type 3 secretion system, to deliver protein effectors into the cytoplasm of host cells. An essential component of this system is a long helical needle filament that protrudes from the bacterial surface and connects the cytoplasms of the bacterium and the eukaryotic cell. Previous structural research was predominantly focused on reconstituted type 3 needle filaments, which lacked the biological context. In this work we introduce a facile procedure to obtain high-resolution cryo-EM structure of needle filaments attached to the basal body of type 3 secretion systems. We validate our approach by solving the structure of Salmonella PrgI filament and demonstrate its utility by obtaining the first high-resolution cryo-EM reconstruction of Shigella MxiH filament. Our work paves the way to systematic structural characterization of attached type 3 needle filaments in the context of mutagenesis studies, protein structural evolution and drug development.

Biochemistry and Biophysics Reports 27, 2021, 101039

Vadim Kotov, Michele Lunelli, Jiri Wald, Michael Kolbe, Thomas C. Marlovits,

Ziegler-type catalysts are the grand old workhorse of the polyolefin industry, yet their hierarchically complex nature complicates polymerization activity–catalyst structure relationships. In this work, the degree of catalyst framework fragmentation of a high-density polyethylene (HDPE) Ziegler-type catalyst was studied using ptychography X-ray-computed nanotomography (PXCT) in the early stages of ethylene polymerization under mild reaction conditions. An ensemble consisting of 434 fully reconstructed ethylene prepolymerized Ziegler catalyst particles prepared at a polymer yield of 3.4 g HDPE/g catalyst was imaged. This enabled a statistical route to study the heterogeneity in the degree of particle fragmentation and therefore local polymerization activity at an achieved 3-D spatial resolution of 74 nm without requiring invasive imaging tools. To study the degree of catalyst fragmentation within the ensemble, a fragmentation parameter was constructed based on a k-means clustering algorithm that relates the quantity of polyethylene formed to the average size of the spatially resolved catalyst fragments. With this classification method, we have identified particles that exhibit weak, moderate, and strong degrees of catalyst fragmentation, showing that there is a strong heterogeneity in the overall catalyst particle fragmentation and thus polymerization activity within the entire ensemble. This hints toward local mass transfer limitations or other deactivation phenomena. The methodology used here can be applied to all polyolefin catalysts, including metallocene and the Phillips catalysts to gain statistically relevant fundamental insights in the fragmentation behavior of an ensemble of catalyst particles.

JACS Au 2021 1 (6), 852-864

Koen W. Bossers, Roozbeh Valadian, Jan Garrevoet, Stijn van Malderen, Robert Chan, Nic Friederichs, John Severn, Arnold Wilbers, Silvia Zanoni, Maarten K. Jongkind, Bert M. Weckhuysen, and Florian Meirer

Catalyst deactivation involves a complex interplay of processes taking place at different length and time scales. Understanding this phenomenon is one of the grand challenges in solid catalyst characterization. A process contributing to deactivation is carbon deposition (i. e., coking), which reduces catalyst activity by limiting diffusion and blocking active sites. However, characterizing coke formation and its effects remains challenging as it involves both the organic and inorganic phase of the catalytic process and length scales from the atomic scale to the scale of the catalyst body. Here we present a combination of hard X-ray imaging techniques able to visualize in 3-D the distribution, effect and nature of carbon deposits in the macro-pore space of an entire industrially used catalyst particle. Our findings provide direct evidence for coke promoting effects of metal poisons, pore clogging by coke, and a correlation between carbon nature and its location. These results provide a better understanding of the coking process, its relation to catalyst deactivation and new insights into the efficiency of the industrial scale process of fluid catalytic cracking.

ChemCatChem 2021, 13, 2494.

M. Veselý, R. Valadian, L. Merten Lohse, M. Toepperwien, K. Spiers, J. Garrevoet, E. T. C. Vogt, T. Salditt, B. M. Weckhuysen, F. Meirer

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