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This dataset is a complete set of raw, processed and analyzed data, complete with Jupiter notebooks, associated with the manuscript mentioned in the title.
In the manuscript, we provide a "systems architecture"-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (Methodology Optimization for Ultrafine Structure Exploration). Through this project, we aim to provide a comprehensive methodology for obtaining the highest quality X-ray scattering information (at small and wide angles) from measurements on materials science samples.
Small-angle scattering data from particle dispersions with narrow size distributions are usually analysed by fitting a model function to the data, which is composed of a monodisperse form factor with a size distribution and, optionally, a structure factor. For common particle shapes like solid spheres, core-shell particles, ellipsoids or rods, the form factor can be computed analytically, and several software packages are readily available which provide a compilation of form factors.1,2 Recently, highly monodisperse nanoparticles with a variety of shapes have been synthesized, e.g. cubes and bipyramids3,4 with lightly capped edges, for which analytic form factors are harder to derive.
We compute an approximation to the scattering curve of arbitrary shapes by filling the shape with a quasi-random distribution of point scatterers and using Debye’s formula to get the overall scattering curve. The highly optimized debyer code is used to perform an efficient evaluation of Debye’s formula,5 which can perform the evaluation of 500,000 point scatterers in two minutes on commodity hardware. Moderate polydispersity is handled by interpolating the computed scattering curve over q from a master curve. In this way, the code is fast enough to perform data fitting of particle ensembles with moderate polydispersity for arbitrary shapes to experimental data.
This work was partly funded by the 17NRM04 nPSize project of the EMPIR programme co-financed by the EMPIR participating states and by the European Union’s Horizon 2020 research and innovation programme.
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.
Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet.
After a colleague sent me a more useful measurement from a laboratory instrument than what I could get from the beamline, I knew it was time to reassess my life's choices. Over the course of several subsequent post-doc and permanent positions around lab instruments, I managed to refine a flexible, comprehensive methodology for data collection, correction and analysis which can be applied to many X-ray scattering investigations at the lab and at the synchrotron. With the help of friends at round places, this methodology was implemented and put into production, and has been delivering high-quality data since then. Now, we have almost all possible data corrections (for X-ray scattering) implemented, and are improving the hardware to deliver higher-quality metadata to enable the corrections to be performed to a higher accuracy.
Simultaneously, we have set up a mini-large facility at BAM with the MAUS, the Multi-scale Analyzer for Ultrafine Structures. The MAUS combines the freedom of a laboratory instrument, with the spectrum of users of a beamline: besides measuring our own samples, and performing our own machine and methodology developments, we have opened this instrument for collaboration with fellow scientists from within BAM and from external institutes and universities. Here, we provide a comprehensive support for these collaborations, guiding the user from concept to sample selection, to interpretation and analysis. In 2019, we have supported over 30 different projects this way, leading to seven co-authored publications involving the MAUS in that year alone. As the MAUS uses the latest iteration of our comprehensive measurement methodology, the data quality is unmatched by any other lab instrument, and fully traceable to boot.
The freedom of the laboratory allows for more proof-of-principle experimentation than what is possible at the synchrotron. Therefore, the MAUS provides a good first (and sometimes final) step towards many experimental materials science investigations, nicely complementing the capabilities of the synchrotron. If and when more flux is needed, the step to the synchrotron is now smaller than ever, in particular with the same method