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McSAS3: improved Monte Carlo small-angle scattering analysis software for dilute and dense scatterers

Brian Richard Pauw, Ingo Breßler

TL;DR

McSAS3 addresses the bottleneck of time-consuming, bias-prone SAS analysis by providing a refactored, Python-based Monte Carlo framework with a GUI for automated data pipelines. The core supports CLI/Jupyter usage, YAML-configured read-in, a large library of SasModels, multi-threaded optimization, and post-hoc histogram rebinning, while the GUI aids template creation and batch processing. The paper demonstrates the approach with three use cases—bimodal gold nanoparticles, bimodal silica powders, and custom simulated form factors—highlighting improved flexibility, speed, and applicability to dilute and dense scatterers. This work enables near-real-time, form-free morphology extraction in automated laboratory and synchrotron workflows, reducing user bias and facilitating operando analyses.

Abstract

McSAS3 is the refactored successor to the original McSAS Monte Carlo small-angle scattering analysis software. It is intended to be integrated in automated data processing pipelines, but can also be used to process individual (batches of) scattering data. McSAS3 comes with a graphical user interface (McSAS3GUI), complete with guides, examples and videos. McSAS3GUI will help to generate and test the three configuration files that McSAS3 needs for data read-in, Monte Carlo optimization and histogramming. The user interface can also be used to process individual files or batches, and can be augmented with machine-specific use templates. The Monte Carlo (MC) approach is able to fit most practical scattering patterns extremely well, resulting in form-free model parameter distributions. Theoretically, these can be distributions on any model parameter, but in practice the MC-optimized parameter is usually a (volume-weighted) size distribution, in absolute volume fraction for absolute-scaled data.

McSAS3: improved Monte Carlo small-angle scattering analysis software for dilute and dense scatterers

TL;DR

McSAS3 addresses the bottleneck of time-consuming, bias-prone SAS analysis by providing a refactored, Python-based Monte Carlo framework with a GUI for automated data pipelines. The core supports CLI/Jupyter usage, YAML-configured read-in, a large library of SasModels, multi-threaded optimization, and post-hoc histogram rebinning, while the GUI aids template creation and batch processing. The paper demonstrates the approach with three use cases—bimodal gold nanoparticles, bimodal silica powders, and custom simulated form factors—highlighting improved flexibility, speed, and applicability to dilute and dense scatterers. This work enables near-real-time, form-free morphology extraction in automated laboratory and synchrotron workflows, reducing user bias and facilitating operando analyses.

Abstract

McSAS3 is the refactored successor to the original McSAS Monte Carlo small-angle scattering analysis software. It is intended to be integrated in automated data processing pipelines, but can also be used to process individual (batches of) scattering data. McSAS3 comes with a graphical user interface (McSAS3GUI), complete with guides, examples and videos. McSAS3GUI will help to generate and test the three configuration files that McSAS3 needs for data read-in, Monte Carlo optimization and histogramming. The user interface can also be used to process individual files or batches, and can be augmented with machine-specific use templates. The Monte Carlo (MC) approach is able to fit most practical scattering patterns extremely well, resulting in form-free model parameter distributions. Theoretically, these can be distributions on any model parameter, but in practice the MC-optimized parameter is usually a (volume-weighted) size distribution, in absolute volume fraction for absolute-scaled data.
Paper Structure (10 sections, 4 figures)

This paper contains 10 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Advantages of McSAS3
  3. Adminstrative details
  4. Use examples
  5. Use example 1: Data Analysis Round Robin bimodal gold nanoparticle suspension
  6. Use example 2: Data Analysis Round Robin bimodal silica powder
  7. Use example 3: Using a custom simulated form factor on a dispersion of faceted cubes
  8. Adapting McSAS3 for lab and synchrotron data pipelines
  9. Current limitations
  10. Conclusions

Figures (4)

  • Figure 1: The panels of the McSAS3GUI user interface showing an optimization on a ZIF-8 dataset, with the main UI shown in the top left, the loaded, clipped, rebinned data and fit shown in top right, the output PDF in the bottom left with the data and size distribution, and the evolution of the optimization parameter in the bottom right.
  • Figure 2: The analysis result of scattering from a bimodal gold nanoparticle suspension. Top left: data and fit, Top right: overall size histogram. Bottom left: first population, Bottom right: second population. Note that the lower graphs use linear binning edges, and therefore show a slightly different histogram than when using logarithmically-spaced histogram bin edges (as used in the top right figure).
  • Figure 3: The analysis result of scattering from a dense, bimodal silica powder. Top left: data and fit. Top right: overall size histogram. Lower left: first population. Lower right: second population.
  • Figure 4: The analysis result of scattering from suspension of faceted cubes. Left: data and fit, right: isolated population showing the size distribution of faceted cubes.