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This is a set of use examples for the HDF5Translator framework. This framework lets you translate measurement files into a different (e.g. NeXus-compatible) structure, with some optional checks and conversions on the way. For an in-depth look at what it does, there is a blog post here.
The use examples provided herein are each accompanied by the measurement data necessary to test and replicate the conversion. The README.md files in each example show the steps necessary to do the conversion for each.
We encourage those who have used or adapted one or more of these exampes to create their own conversion, to get in touch with us so we may add your example to the set.
Trinamic TMCL IOC is a Python package designed for controlling stepper motors connected to a Trinamic board using the TMCL language (all boards supported by PyTrinamic should now work, has been tested on the TMCM 6110 and the TMCM 6214). Since it is implementing the TMCL protocol, it should be easy to adapt to other Trinamic motor controller boards. This package assumes the motor controller is connected over a machine network via a network-to-serial converter, but the underlying PyTrinamic package allows for other connections too.
This allows the control of attached motors via the EPICS Channel-Access virtual communications bus. If EPICS is not desired, plain Pythonic control via motion_control should also be possible. An example for this will be provided in the example.ipynb Jupyter notebook.
This package leverages Caproto for EPICS IOCs and a modified PyTrinamic library for the motor board control, and interfaces between the two via an internal set of dataclasses. Configuration for the motors and boards are loaded from YAML files (see tests/testdata/example_config.yaml).
The modifications to PyTrinamic involved extending their library with a socket interface. This was a minor modification that should eventually find its way into the official package (a pull request has been submitted).
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.
Hierarchically porous, high‐surface‐area silica materials are excellent candidates for multiple applications like catalysis and environmental remediation. Shaping these materials with additive manufacturing (AM) techniques, like robocasting, could enable their use with the benefit of on‐demand, customized shaping and maximizing performance. Herein, ordered mesoporous silica COK‐12 slurries were robocasted into monoliths, containing different ratios of uncalcined COK‐12 and sodium bentonite (0–25 wt.%). The rheology of the mixed slurries is characterized by lower flow indexes (0.69 vs. 0.32) and higher yield stresses (96 vs. 259 Pa) compared to pure COK‐12 ones. Monoliths were printed in woodpile structures and calcined at 600°C. Micro‐CT measurements showed a linear shrinkage of 25% after calcination. Mechanical characterization showed increased uniaxial strength (0.20 ± 0.07 to 1.0 ± 0.3 MPa) with increasing binder/solids ratio from 13 to 25%. The amorphous, mesoporous structure of COK‐12 was retained. The structures exhibited open porosities of 52 ± 4% and showed higher specific mesopore volumes, and increased average mesopore size (6 vs. 8 nm) compared to COK‐12. Small‐angle x‐ray scattering analysis revealed an increased lattice parameter (10.3 vs. 11.0 nm) and reduced wall thickness (3.1 nm vs. 4.1 nm) of the COK‐12 in the monoliths. These properties indicate suitability for their application as porous supports and adsorbents.
Test artifact for fs-LDW
(2023)
With femtosecond-laser direct writing (fs-LDW) maturing in all aspects as a manufacturing technology, a toolset for quality assurance must be developed. In this work we introduce a first of its kind test artifact. Test artifacts are standardized 3D models with specific geometric features to evaluate the performance of writing parameters. Test artifacts are already common in other 3D additive manufacturing technologies e.g. selective laser melting. The test artifact introduced in this work was developed in particular to accommodate the high geometrical resolution of fs-LDW structures and the limited possibilities to examine the resulting structure. Geometric accuracy, surface adhesion as well as confocal Raman spectroscopy results were considered when evaluating the design of the test artifact. We will explain the individual features and design considerations of our fs-LDW test artifact. The difference between two slicers, Cura and 3DPoli, and the implications on measured feature sizes and the general shape is quantified. The measured geometries are used to derive a general design guide for a specific combination of photoresists, laser power and scanning speed and to analyze the geometric accuracy of a structure produced using these guidelines. The shown test artifact is publicly available as STL file on GitHub (https://github.com/BAMresearch/2PP-TestArtifact) and in the supplement.
The complex nature of liquid water saturation of polymer electrolyte fuel cell (PEFC) catalyst layers (CLs) greatly affects the device performance. To investigate this problem, we present a method to quantify the presence of liquid water in a PEFC CL using small-angle X-ray scattering (SAXS). This method leverages the differences in electron densities between the solid catalyst matrix and the liquid water filled pores of the CL under both dry and wet conditions. This approach is validated using ex situ wetting experiments, which aid the study of the transient saturation of a CL in a flow cell configuration in situ. The azimuthally integrated scattering data are fitted using 3D morphology models of the CL under dry conditions. Different wetting scenarios are realized in silico, and the corresponding SAXS data are numerically simulated by a direct 3D Fourier transformation. The simulated SAXS profiles of the different wetting scenarios are used to interpret the measured SAXS data which allows the derivation of the most probable wetting mechanism within a flow cell electrode.
This paper reports a systematic study into the effect of nitrogen on iron-catalyzed graphitization of biomass. Chitin, chitosan, N-acetylglucosamine, gelatin and glycine were selected to represent nitrogen-rich saccharides and amino-acid/polypeptide biomass precursors. The materials were pyrolyzed with an iron catalyst to produce carbons with a wide range of chemical and structural features such as mesoporosity and nitrogen-doping. Many authors have reported the synthesis of nitrogen-doped carbons by pyrolysis and these have diverse applications. However, this is the first systematic study of how nitrogen affects pyrolysis of biomass and importantly the catalytic graphitization step. Our data demonstrates that nitrogen inhibits graphitization but that some nitrogen survives the catalytic graphitization process to become incorporated into various chemical environments in the carbon product.
This talk highlights a proof-of-concept that demonstrates the ability to calculate high-resolution Fourier transforms. These can be combined with multi-scale modeling to simulate scattering over a wide range, from small-angle scattering to XRD and PDF.
The preprint documenting this is available on the ArXiv here:
https://doi.org/10.48550/arXiv.2303.13435
The Jupyter notebook, VASP calculation details and MOUSE measured scattering patterns are available from this Zenodo repository: https://dx.doi.org/10.5281/zenodo.7764045