TY - GEN A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob A1 - Anker, A. S. A1 - Balazs, D. M. A1 - Beyer, F. L. A1 - Bienert, Ralf A1 - Bouwman, W. G. A1 - Breßler, Ingo A1 - Breternitz, J. A1 - Brok, E. S. A1 - Bryant, G. A1 - Clulow, A. J. A1 - Crater, E. R. A1 - De Geuser, F. A1 - Giudice, A. D. A1 - Deumer, J. A1 - Disch, S. A1 - Dutt, S. A1 - Frank, K. A1 - Fratini, E. A1 - Gilbert, E. P. A1 - Hahn, Marc Benjamin A1 - Hallett, J. A1 - Hohenschutz, Max A1 - Hollamby, M. J. A1 - Huband, S. A1 - Ilavsky, J. A1 - Jochum, J. K. A1 - Juelsholt, M. A1 - Mansel, B. W. A1 - Penttilä, P. A1 - Pittkowski, R. K. A1 - Portale, G. A1 - Pozzo, L. D. A1 - Ricardo de Abreu Furtado Garcia, P. A1 - Rochels, L. A1 - Rosalie, Julian M. A1 - Saloga, P. E. J. A1 - Seibt, S. A1 - Smith, A. J. A1 - Smith, G. N. A1 - Annadurai, V. A1 - Spiering, G. A. A1 - Stawski, Tomasz A1 - Taché, O. A1 - Thünemann, Andreas A1 - Toth, K. A1 - Whitten, A. E. A1 - Wuttke, J. T1 - The human factor: results of a small-angle scattering data analysis Round Robin T2 - arXiv.org N2 - 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. KW - Round Robin KW - Sall-angle scattering KW - Nanostructure quantification KW - Nanostructure KW - SAXS KW - MOUSE KW - X-ray scattering KW - Size distribution KW - Nanoparticles PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-571342 DO - https://doi.org/10.48550/arXiv.2303.03772 SP - 1 EP - 23 PB - Cornell University CY - New York AN - OPUS4-57134 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Laskina, Sofya T1 - Computing the forward and inverse problem of X-ray scattering N2 - 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. KW - Machine Learning KW - SAXS KW - DFT PY - 2023 SP - 1 EP - 76 PB - Freie Universität Berlin CY - Berlin AN - OPUS4-56798 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Hodoroaba, Vasile-Dan A1 - Hörenz, Christoph T1 - Improving nanoparticle size measurement accuracy for safety assessment T2 - EURAMET News N2 - Nanomaterials and nanoparticles are finding applications across a wide range of technology sectors, from medicine and food to transportation and construction. In order to assess these new materials for potential risks to health and the environment, they need to be well-characterised. The measurement of constituent nanoparticle size, shape, and size distribution are important factors for the risk evaluation process. EMPIR project Improved traceability chain of nanoparticle size measurements (17NRM04, nPSize) is working to assess a range of traceable nanoparticle measurement approaches, including Scanning Electron Microscopy (also in Transmission Mode), Atomic Force Microscopy and Small Angle X-ray Scattering, and deliver improved calibration methods to users. For the techniques under investigation, physical models of their response to a range of nanoparticle types are developed. Validated reference materials will also be used for an inter-comparison of measurement systems, with an evaluation of the associated measurement uncertainty. With project contributions to standards development work, manufacturers will be better placed to assess the human and environmental risks posed by nanomaterials across a whole range of products. KW - Nanoparticles KW - Particle size distribution KW - Traceability KW - Electron microscopy KW - AFM KW - SAXS PY - 2021 UR - https://www.euramet.org/?L=0&news=40%3A1159 SP - 1 PB - EURAMET e.V. CY - Braunschweig AN - OPUS4-52129 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Swimm, K. A1 - Scherdel, C. A1 - Reichenauer, G. A1 - Klobes, Peter T1 - Characterization of organic aerogels as model materials for thermal conductivity measurements T2 - HASYLAB annual report 2008 KW - Aerogels KW - SEM KW - SAXS KW - N2 sorption KW - Hg porosimetry KW - Thermal conductivity KW - Porosity PY - 2008 UR - http://hasylab.desy.de/annual_report/files/2008/200838.pdf SP - 1 EP - 2 PB - DESY CY - Hamburg AN - OPUS4-21828 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -