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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
Development and characterization of starch film and the incorporation of silver nanoparticles
(2020)
Starch is one of the biopolymers being used for bioplastic synthesis. For production, starch can be combined with different plasticizers, starches from different plant sources and even with nanomaterials to improve or to add film properties. The challenge of adding these, e.g. in the form of silver nanoparticles (AgNp) is to determine the concentration so as to avoid impairing the properties of the film, agglomeration or altering the visual characteristics of the film. In this study, a starch film synthesis route and the incorporation of silver nanoparticles has been proposed in order not to alter the properties of the film while maintaining the transparency and a clear colour of the starch film. The results showed that the proposed synthesis route is promising, efficient, reproducible, fast and the film has good mechanical properties.
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.
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.