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SASfit 0.94.12
(2023)
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.
These are four datasets that were made available to the participants of the Small-angle Scattering data analysis round robin. The intent was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
In this repository, there are:
1) a PDF document with more details for the study,
2) the datasets for people to try and fit
3) an Excel spreadsheet to document the results.
Datasets 1 and 2 were modified from: Deumer, Jerome, & Gollwitzer, Christian. (2022). npSize_SAXS_data_PTB (Version 5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5886834
Datasets 3 and 4 were collected in-house on the MOUSE instrument.
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.
A tool for merging and/or rebinning single or multiple datasets to achieve a lower point density with best possible statistics. highly scriptable, CLI, no GUI
Version 0.1: works but could do with a cleanup. Weighting by uncertainty currently always on, but should be optional for use as an azimuthal or radial averager
The formation and alignment of gel fibres in alkanes (solvents) was investigated. The gel fibres consist of small amphiphilic molecules containing a C60 molecule and a small ligand chain. Under the right conditions, these self-assemble in core-shell micelles, which themselves pack into micron-sized fibre-like structures. These can be aligned using a strong magnetic field, as investigated using SANS with a 17T superconducting magnet at a range of field strengths.
An evolutionary optimization technique is used to reconstruct digitized material models of 3003 nm3 size for mesoporous two-phase systems. The models are adapted to the two-point probability (TPP) and to a volume-based pore-size distribution (PSD) which were derived from SANS and adsorption experiments and which carry statistical information about morphology and topology of the pore system. To avoid extreme update-costs, the bulk of mutations are assessed by means of a suitable approximation of the PSD; it is demonstrated that a sporadic insertion of the PSD suffices to drive the algorithm towards satisfactory models in acceptable time. Our approach is knowledge-based in the sense that (i) the mutations are restricted to expedient exchanges of phase-voxels by a heuristic rule, and (ii) the sporadic calculation of the PSD from the current state of the model, in essence, provides an efficient self-control for the evolutionary process. We applied the method to reconstruct periodic models of the xerogel Gelsil 200. Such reconstructs of real mesoporous solids could be utilized, for instance, to verify theories of adsorption and capillary condensation.