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The vibrational density of states of a series of homologous triphenylene-based discotic liquid crystals HATn (n = 5, 6, 8, 10, 12) depending on the length of the aliphatic side chain is investigated by means of inelastic neutron scattering. All studied materials have a plastic crystalline phase at low temperatures, followed by a hexagonally ordered liquid crystalline phase at higher temperatures and a quasi isotropic phase at the highest temperatures. The X-ray scattering pattern for the plastic crystalline phase of all materials shows a sharp Bragg reflection corresponding to the intercolumnar distance in the lower q-range and a peak at circa 17 nm-1 related to intracolumnar distances between the cores perpendicular to the columns as well as a broad amorphous halo related to the disordered structure of the methylene groups in the side chains in the higher q-range. The intercolumnar distance increases linearly with increasing chain length for the hexagonal columnar ordered liquid crystalline phase. A similar behaviour is assumed for the plastic crystalline phase. Besides n = 8 all materials under study exhibit a Boson peak. With increasing chain length, the frequency of the Boson peak decreases and its intensity increases. This can be explained by a self-organized confinement model. The peaks for n = 10, 12 are much narrower than for n = 5, 6 which might imply the transformation from a rigid system to a softer one with increasing chain length. Moreover the results can also be discussed in the framework of a transition from an uncorrelated to a correlated disorder with increasing n where n = 8 might be speculatively considered as a transitional state.
Everything SAXS
(2019)
Experimenting on MAUS
(2019)
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
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.
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.
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.
The unambiguous correlation of possible health and sustainability risks to nanoparticle size must be enabled by reliable measurement of nanoparticle size, to ensure comparability and compatibility between results measured under different methods. The NPSIZE project funded by European Metrology Program (EMPIR) develop methods, reference materials and modelling to improve the traceability chain, comparability and compatibility of nanoparticle size measurements. In this work, we present how spherical silica nanoparticles are synthetized with controlled monomodal or bimodal dispersion to be use as reference materials and international round-robin. Improving the fabrication requires a fine understanding of synthesis (1), coupled with an expertise of in-situ or ex-situ analysis methods. This is a new challenge for the analysis : determining not only average characteristics (size, chemical composition and shape ...) but also the concentration and the distribution over the population studied (2). Small-Angle X-ray Scattering (3) allows very precise measurements of the nanoparticles size and concentration that can be directly link to the metric system (4) (metrological traceability) . We developed a SAXS laboratory instrument dedicated to the in-situ characterization of nanoparticles, which enable fast measurements, and the monitoring of the synthesis parameters. Measurement protocols and software processing chain (5) (i.e. size distribution) are also combined & optimized.