Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Liaison activities within ISO/TC 202 'Microbeam Analysis' which are relevant to ISO/TC 229 'Nanotechnologies' are reported acoording to the structure defined by ISO/TC229 Nanotechnologies Liaisons Coordination Group (NLCG): new standards/documents, coordination issues, and further detailed specific information, e.g. publications, events, comments.
We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10\,K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory (DFT) in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI's determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1cm−1.
Die Verwendung hochfester Feinkornbaustähle hat für viele Anwendungen des Stahlbaus ein großes Potenzial für gewichtsoptimierte, effiziente Strukturen mit ho-her mechanischer Beanspruchbarkeit. Weitere Zugewinne an Effizienz sind durch generative Fertigungsschritte sowie bionische Bauweisen erzielbar. Hierzu stehen bereits kommerzielle hochfeste drahtförmige Zusatzwerkstoffe für formgebendes MSG-Schweißen zur Verfügung. Dem Einsatz stehen noch fehlende quantitative Aussagen zu den fertigungsbedingten Beanspruchungen und der Bauteilsicherheit während Herstellung und Betrieb im Wege. Dies betrifft insbesondere prozess- sowie materialbedingte Einflüsse und die konstruktive Schrumpfbehinderung verbunden mit der Ausbildung hoher Zugeigenspannungen und damit zusammenhängenden Kaltrissbildung.
Hierfür wurden im Projekt detaillierte und anwenderbezogene Kenntnisse zu den komplexen Wechselwirkungen zwischen Schweißprozess und Wärmeführung während der Fertigung, der metallurgischen Vorgänge und insbesondere der vorliegenden konstruktiven Einflüsse auf die entstehenden Eigenspannungen erarbeitet, um ein frühzeitiges Bauteilversagen aufgrund eines hohen fertigungsbedingten Beanspruchungsniveaus bis hin zu einer Rissbildung während der Fertigung sicher zu vermeiden. Gleichzeitig wurden die Einflüsse auf die mechanisch-technologischen Gütewerte systematisch analysiert. Zudem wurden die Auswirkungen trennender Verfahren durch Entfernen der Substratplatte sowie durch die spanende Bearbeitung der Vorformlinge zu Endbauteilgeometrien geklärt, da diese unmittelbar den Eigenspannungszustand beeinflussen und deutlichen Verzug der Bauteile auslösen.
Für das generative Schweißen konnten Verarbeitungsempfehlungen sowie Normenvorgaben erarbeitet werden. Dies hilft insbesondere KMU eine wirtschaftliche, beanspruchungsgerechte und risssichere generative Fertigung von Bauteilen aus hochfesten Feinkornbaustählen zu ermöglichen.
A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders.
Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5 % and half of the population width entries within 40 %, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets.
For those that did, half of the entries for the means and widths were within 44 % and 86 % respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
Continuing progress in the field of X-ray scattering methods empowers scientists with new possibilities to capture the most important piece of information about the structure of the sample - its 3D electron density.
Although the first methods appeared almost a century ago, recovering the density structure of a sample is still very problematic. Most avail-able imaging techniques transform a 3D electron density of a realspace structure into the 2D Fourier Transform of the intensity of scattered waves in the reciprocal space. This process causes a loss of information.
Firstly, instead of a 3D sample, a 2D image is created, and secondly, the phase information of the scattered waves is lost. The latter is known as the ”phase problem” and poses a serious obstacle on a way to recover a 3D electron density. In this work, we draw attention to the problem of forward and inverse Small Angle X-Ray Scattering. In the first, forward, part, we rethink the existing pipelines to computationally simulate such scattering experiments. Although there are efficient implementations of fast Fourier transformation, they often have some drawbacks. For instance, to calculate a 3D fast Fourier transform it is required to place its density in the RAM. For high-resolution structures of size > 1024 3 , this becomes very problematic, as the whole density structure requires more than 16 GB of memory.
CUDA solution allows for a very fast and parallelizable implementation of high-resolution data on hundreds of last-generation machines.
Such computations are very pricy and inaccessible for most scientists.
To bypass this limitation, we propose a solution for a split-up 3D fast Fourier transform, which is implemented as a sequence of 2D and 1D operations. We compare our implementation on the simulated 3D shapes and show the result of a proof-of-concept on 4096 3 Metallorganic framework density structure. In the second, inverse problem, we train an invertible neural network, that given scattering data can predict the shape and its parameters. The architecture is built such, that the inverse problem is learned together with the forward process - the Fourier Transformation. We achieved very good results with this architecture, nonetheless, further testing is required, as the current training set only encompasses three simple shapes: sphere, hard sphere and cylinder.
All code to reproduce and analyze the results is available at https: //github.com/sofyalaski/SAXS-simulations.