News & Scientific Highlights

Jisoo Kim was awarded the 2022 Werner Meyer-Ilse Memorial Award. The WMI Award is given to young scientists for exceptional contributions to the advancement of X-ray microscopy through either outstanding technical developments or applications, as evidenced by their presentation at the International Conference on X-ray Microscopy and supporting publications.  Jisoo was awarded for his development of the method "Time-resolved x-ray scattering tomography for rheological studies", and is co-recipient of the award with Yanqi Luo from the Advanced Photons Source for her work on applications. The award was presented during the 15th International Conference on X-ray Microscopy XRM2022 hosted by the National Synchrotron Radiation Research Center (NSRRC) in Hsinchu, Taiwan on 19 - 24 June, 2022.

The orientation mismatch between the cone beam of an X-ray tube and the grating lines in a flat substrate remains a big challenge for laboratory grating-based X-ray interferometry, since it severely limits the imaging field of view. To solve this problem, we fabricated fan-shaped G0 source gratings by modulating the electric field during the deep reactive ion etching of silicon. With local electric field modulation in plasma we can etch high aspect ratio fan-shaped gratings that match the X-ray cone beam emission of a tube source. This new technology replaces the grating bending and allows a more compact design with larger field of view. Our work have recently been published in Applied Surface Science.

In the framework of the HERCULES European School about Neutrons & Synchrotron Radiation for Science which is coordinated by the Université de Grenoble Alpes and took place on February 28 – April 1, 2022, we were pleased to virtually host 4 students for a hands-on session. During this practical on “Absorption and phase contrast X-ray tomographic microscopy”, the students had the chance to go, with the help of a jupyter notebook and the guidance of our team members Margaux Schmeltz and Christian Schlepütz, through different examples of tomographic reconstructions. They learnt about basic decisions and tradeoffs that have to be taken into account when planning to acquire tomographic imaging data, were introduced to some segmentation tools and even got to play with dynamic tomographic data!

Time-resolved X-ray tomographic microscopy provides new opportunities in the volumetric investigation of dynamic processes. Full exploitation of these new capabilities is currently still hindered by the lack of efficient post-processing approaches capable of handling TBs of noisy datasets. A deep learning based reconstruction and classification algorithm designed to reconstruct and segment dynamic processes within a static matrix with high efficiency is a solution to this issue. In a paper published recently in Scientific Reports, we demonstrate the advantages of the proposed approach on dynamic, time-resolved fuel cell data, for which the current data post-processing pipeline heavily relies on manual labor, typically limiting the experimental plans to just a small range of the full parameter space.  

X-ray scattering tensor tomography facilitates the investigation of the microstructural organization in statistically large sample volumes. Established acquisition protocols based on scanning small-angle X-ray scattering and X-ray grating interferometry inherently require long scan times even with high brilliance X-ray sources. Recent developments in X-ray circular diffractive optics enable fast single-shot acquisition of the sample scattering properties with 2D omnidirectional sensitivity. Researchers from the TOMCAT beamline at Paul Scherrer Institut have proposed simple yet inherently rapid acquisition protocols for X-ray scattering tensor tomography leveraging these new optical elements. Results from both simulation and experimental data, supported by a null space analysis, suggest that the proposed acquisition protocols are rapid and corroborate, providing sufficient information for the accurate volumetric reconstruction of the scattering properties. The proposed acquisition protocols will be the cornerstones for rapid inspection or time-resolved tensor tomography of the microstructural organization over an extended field of view. The work is published in Scientific Reports on 29 November 2021.

The paper "High aspect ratio grating microfabrication by Pt assisted chemical etching and Au electroplating” by Dr. Lucia Romano & coauthors has been highlighted in the "Women in Engineering Materials" Virtual Issue published on Advanced Engineering Materials. This Virtual Issue draws attention to outstanding works within Materials Science created under the lead of women as principal investigators.

Congratulations to Lucia and the x-ray optics design and fabrication team!

In December 2020 the Swiss parliament approved the Swiss Dispatch on Promotion of Education, Research and Innovation (ERI) for 2021 to 2024 which includes funding for the planned SLS 2.0 upgrade. The new machine will lead to significantly increased brightness, thus providing a firm basis for keeping the SLS and its beamlines state-of-the-art for the decades to come. The TOMCAT crew is very excited that the TOMCAT 2.0 plans (deployment of the S- and I-TOMCAT branches, see SLS 2.0 CDR, p. 353ff) have been included in the Phase-I beamline upgrade portfolio. These beamlines will receive first light right after the commissioning of the SLS 2.0 machine around mid 2025. A first milestone towards this goal has just been achieved, with the successful installation of the S-TOMCAT optics hutch during W1 of 2021. The TOMCAT scientific and technical staff would like to thank Mr. Nolte and his Innospec crew for delivering perfectly on schedule. 

TOMCAT welcomes Gianluca Iori, beamline scientist from BEATS - the new beamline for tomography at the SESAME synchrotron in Jordan, to a 3-month training on beamline operations. Gianluca’s visit is part of the Staff Training (BEATS Work Package 2) organized for BEATS scientific staff and SESAME control engineers. BEATS is a European project, funded under the EU’s Horizon 2020 research and innovation programme and coordinated by the ESRF.

A fully automatized iterative reconstruction pipeline designed to reconstruct and segment dynamic processes within a static matrix has been developed at TOMCAT. The algorithm performance is demonstrated on dynamic fuel cell data where it enabled automatic extraction of liquid water dynamics from sub-second tomographic microscopy data. The work is published in Scientific Reports on 2 October 2020.