TY - JOUR A1 - Deumer, J. A1 - Pauw, Brian Richard A1 - Marguet, S. A1 - Skroblin, D. A1 - Taché, O. A1 - Krumrey, M. A1 - Gollwitzer, C. T1 - Small-angle X-ray scattering: characterization of cubic Au nanoparticles using Debye’s scattering formula N2 - A versatile software package in the form of a Python extension, named CDEF (computing Debye’s scattering formula for extraordinary form factors), is proposed to calculate approximate scattering profiles of arbitrarily shaped nanoparticles for small-angle X-ray scattering (SAXS). CDEF generates a quasi-randomly distributed point cloud in the desired particle shape and then applies the open-source software DEBYER for efficient evaluation of Debye’s scattering formula to calculate the SAXS pattern (https://github.com/j-from-b/CDEF). If self-correlation of the scattering signal is not omitted, the quasi-random distribution provides faster convergence compared with a true-random distribution of the scatterers, especially at higher momentum transfer. The usage of the software is demonstrated for the evaluation of scattering data of Au nanocubes with rounded edges, which were measured at the four-crystal monochromator beamline of PTB at the synchrotron radiation facility BESSY II in Berlin. The implementation is fast enough to run on a single desktop computer and perform model fits within minutes. The accuracy of the method was analyzed by comparison with analytically known form factors and verified with another implementation, the SPONGE, based on a similar principle with fewer approximations. Additionally, the SPONGE coupled to McSAS3 allows one to retrieve information on the uncertainty of the size distribution using a Monte Carlo uncertainty estimation algorithm. KW - X-ray scattering KW - SAXS KW - Non-spherical nanoparticles KW - Nanoparticles KW - Nanomaterials KW - Debye scattering equation KW - Simulation KW - Data fitting PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-557652 DO - https://doi.org/10.1107/S160057672200499X VL - 55 IS - Pt 4 SP - 993 EP - 1001 PB - International Union of Crystallography CY - Chester, England AN - OPUS4-55765 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - RPRT A1 - Hodoroaba, Vasile-Dan A1 - Fontanges, R. A1 - Delvallée, A. A1 - Deumer, J. A1 - Salzmann, C. A1 - Crouzier, L. A1 - Gollwitzer, C. A1 - Klein, T. A1 - Koops, R. A1 - Sebaihi, N. A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob A1 - Artous, S. A1 - Taché, O. A1 - Feltin, N. T1 - Report on full algorithm sequences for nanoparticle detection and size measurement as developed on both a physical basis and by machine learning N2 - he main objective of the nPSize project is to improve the measurement capabilities for nanoparticle size based on both measurement methods traceable to SI units and new reference materials. Two basic approaches have been used in order to develop measurement procedures resulting in traceable results of the nanoparticle size distribution: physical modelling for the methods used in the project (TSEM, SEM, AFM and SAXS) and machine learning. Physical modelling: In this part, the physical models associated with different shape measurements for the techniques TSEM, SEM, AFM and SAXS have been collected and further developed with the aim to simulate the resulting signal as measured by the individual methods. Uncertainties and traceability associated with each model were investigated and evaluated. In the following, the progress on these physical models is reported for each individual method. Machine Learning modelling: The aim of this part is to use machine learning to enable automatic measurement of nanoparticle shape from expert a-priori information only. No physical model will be used as a-priori information in this task. The accuracy and traceability of the size results obtained by each technique will be analyzed and compared with the physical modelling. A machine learning database will then be used to create automatic detection algorithms. KW - Nanoparticles KW - Particle size distribution KW - SEM KW - TSEM KW - TEM KW - SAXS KW - AFM PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546531 DO - https://doi.org/10.5281/zenodo.5807864 SP - 1 EP - 20 PB - Zenodo CY - Geneva AN - OPUS4-54653 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob A1 - Anker, A. S. A1 - Annadurai, V. A1 - Balazs, D. M. 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 - Del Giudice, A. A1 - Deumer, J. A1 - Disch, S. A1 - Dutt, S. A1 - Frank, K. A1 - Fratini, E. A1 - Garcia, P. R. A. F. A1 - Gilbert, E. P. A1 - Hahn, Marc Benjamin A1 - Hallett, J. A1 - Hohenschutz, M. A1 - Hollamby, M. 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 - Rochels, L. A1 - Rosalie, Julian A1 - Saloga, Patrick E. J. A1 - Seibt, S. A1 - Smith, A. J. A1 - Smith, G. N. A1 - Spiering, G. A. A1 - Stawski, Tomasz M. 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 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%. Due to the added complexity of the structure factor, far 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 - Data analysis KW - Small-angle scattering KW - Nanomaterials KW - Interlaboratory comparability KW - Nanostructure quantification KW - Methodology KW - MOUSE PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-587091 DO - https://doi.org/10.1107/S1600576723008324 SN - 1600-5767 VL - 56 IS - 6 SP - 1618 EP - 1629 PB - Wiley-Blackwell CY - Oxford AN - OPUS4-58709 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR 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 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 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 - Ithaca, NY AN - OPUS4-57134 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -