TY - JOUR A1 - Kohlbrecher, J. A1 - Breßler, Ingo T1 - Updates in SASfit for fitting analytical expressions and numerical models to small-angle scattering patterns N2 - Small-angle scattering is an increasingly common method for characterizing particle ensembles in a wide variety of sample types and for diverse areas of application. SASfit has been one of the most comprehensive and flexible curve-fitting programs for decades, with many specialized tools for various fields. Here, a selection of enhancements and additions to the SASfit program are presented that may be of great benefit to interested and advanced users alike: (a) further development of the technical basis of the program, such as new numerical algorithms currently in use, a continuous integration practice for automated building and packaging of the software, and upgrades on the plug-in system for easier adoption by third-party developers; (b) a selection of new form factors for anisotropic scattering patterns and updates to existing form factors to account for multiple scattering effects; (c) a new type of a very flexible distribution called metalog [Keelin (2016). Decis. Anal. 13, 243–277], and regularization techniques such as the expectation-maximization method [Dempster et al. (1977). J. R. Stat. Soc. Ser. B (Methodological), 39, 1–22; Richardson (1972) J. Opt. Soc. Am. 62, 55; Lucy (1974). Astron. J. 79, 745; Lucy (1994). Astron. Astrophys. 289, 983–994], which is compared with fits of analytical size distributions via the non-linear least-squares method; and (d) new structure factors, especially for ordered nano- and meso-scaled material systems, as well as the Ornstein–Zernike solver for numerical determination of particle interactions and the resulting structure factor when no analytical solution is available, with the aim of incorporating its effects into the small-angle scattering intensity model used for fitting with SASfit. KW - Small-angle scattering KW - Numerical models KW - Structure factors KW - Regularization KW - SAXS KW - SANS PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-565069 DO - https://doi.org/10.1107/S1600576722009037 SN - 0021-8898 SN - 1600-5767 VL - 55 IS - 6 SP - 1677 EP - 1688 PB - Wiley-Blackwell CY - Oxford AN - OPUS4-56506 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pauw, Brian Richard T1 - Basics and applications of good SAXS: Quantifying the fine structure of lots of materials N2 - In contrast to the crisp, clear images you can get from electron microscopy, small-angle X-ray scattering (SAXS) patterns are rather featureless. These patterns, however, contain averaged structural information of all of the finest material structures that were illuminated by the X-ray beam. With careful and precise investigation, and supplementary information from complementary techniques, this bulk material structure can be quantified to reveal structural information spanning four or even five decades in size. Additionally, while the data correction and analysis is complex, sample preparation is very straightforward, also allowing for in-situ and operando measurements to be performed without breaking a sweat. In the right hands, then, this technique can be the most powerful tool in your analytical arsenal. T2 - OpTecBB webinar within the scope of the focus area Optical Analytics CY - Online meeting DA - 27.05.2020 KW - Small-angle scattering KW - Introduction KW - Application KW - Saxs KW - Nanomaterials KW - Nanostructure PY - 2020 UR - https://www.youtube.com/watch?v=mXkYL3dSsTY UR - https://optecbb.de/veranstaltungen/veranstaltung/webinar-basics-and-applications-of-good-saxs-1238/ AN - OPUS4-50879 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kohlbrecher, J. A1 - Breßler, Ingo T1 - SASfit 0.94.12 N2 - Small-angle scattering is an increasingly common method for characterizing particle ensembles in a wide variety of sample types and for diverse areas of application. SASfit has been one of the most comprehensive and flexible curve-fitting programs for decades, with many specialized tools for various fields. KW - Small-angle scattering KW - SAXS KW - SANS KW - Scattering pattern analysis PY - 2023 UR - https://doi.org/10.5281/zenodo.7530357 DO - https://doi.org/10.5281/zenodo.7530356 PB - Zenodo CY - Geneva AN - OPUS4-57913 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hayward, E. C. A1 - Smales, Glen Jacob A1 - Pauw, Brian Richard A1 - Takeguchi, M. A1 - Kulak, A. A1 - Hunter, R. D. A1 - Schnepp, Z. T1 - The effect of catalyst precursors on the mechanism of iron-catalysed graphitization of cellulose N2 - Iron-catalysed graphitization of biomass is a simple and sustainable route to carbons with high graphitic content. It uses abundant precursors and moderate processing temperatures and generates carbons with high porosity. Recently, it has been demonstrated that the choice of biomass precursor can have a significant impact on the textural and compositional properties of the resulting carbon. In this paper, we demonstrate that the choice of catalyst is also critical to the carbon structure. Aqueous iron(III) nitrate and iron(III) chloride convert cellulose to carbons with very different textural properties. This is due to the choice of iron catalyst changing the mechanism of cellulose decomposition and also the nature of the active graphitization catalyst. KW - Catalyst KW - Carbon materials KW - Cellulose KW - Small-angle scattering KW - Green energy KW - Graphitic carbon KW - Green synthesis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-614800 DO - https://doi.org/10.1039/D4SU00365A SP - 1 EP - 10 PB - Royal Society of Chemistry (RSC) AN - OPUS4-61480 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob A1 - Rosalie, Julian T1 - Small-angle scattering data analysis round robin - Anonymized results, figures and Jupyter notebook N2 - The intent of this round robin was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset. This zip file contains the anonymized results and the jupyter notebook used to do the data processing, analysis and visualisation. Additionally, TEM images of the samples are included. KW - Round robin KW - Small-angle scattering KW - Data analysis PY - 2023 DO - https://doi.org/10.5281/zenodo.7509710 PB - Zenodo CY - Geneva AN - OPUS4-56803 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 -