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Eingeladener Vortrag
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X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG). This data was collected using the MOUSE project (instrument and methodology).
X-ray scattering datasets for samples described in the 2022 publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism". This dataset includes both raw and processed X-ray scattering data for samples APTCN and MPTCN, alongside background measurements files (BKG).
X-ray scattering datasets for samples described in the 2022 publication "Side chain length dependent dynamics and conductivity in self assembled ion channels". This dataset includes both raw and processed X-ray scattering data for samples ILC8, ILC10, ILC12, ILC14 and ILC16 alongside background measurement files (BKG).
OpenSCAD, STL and technical drawings for the capillary flow-through cell designed primarily for use with The MOUSE instruments.
This flow-through cell can be used in conjunction with:
- Modular sample holder (10.5281/zenodo.7499416)
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Laser-cut sample holder (10.5281/zenodo.7499437)
OpenSCAD, STL and technical drawings for the solid sample rack designed primarily for use with The MOUSE instruments.
This solid sample rack can be used in conjunction with:
- Laser-cut sample holder (10.5281/zenodo.7499437)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
PDF file for the laser-cut sample holder designed primarily for use with The MOUSE instruments.
This sample holder can be used in conjunction with:
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
OpenSCAD, STL and technical drawings for the Modular sample holder designed primarily for use with The MOUSE instruments.
This modular sample holder can be used in conjunction with the following:
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Laser-cut sample holder (10.5281/zenodo.7499437)
Small-angle Scattering Data Analysis Round Robin: anonymized results, figures and Jupyter notebook
(2023)
The intent of this round robin was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
This zip file contains the anonymized results and the jupyter notebook used to do the data processing, analysis and visualisation. Additionally, TEM images of the samples are included
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 %, respectively. Due to the added complexity of the structure factor, much 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.
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.