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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.
The CCQM-K148.a comparison was coordinated by the BIPM on behalf of the CCQM Organic Analysis Working Group for NMIs and DIs which provide measurement services in organic analysis under the CIPM MRA. It was undertaken as a "Track A" comparison within the OAWG strategic plan. CCQM-K148.a demonstrates capabilities for assigning the mass fraction content of a solid organic compound having moderate molecular complexity, where the compound has a molar mass in the range (75 - 500) g/mol and is non-polar (pKow < −2), when present as the primary organic component in a neat organic solid and where the mass fraction content of the primary component in the material is in excess of 950 mg/g.
Participants were required to report the mass fraction of Bisphenol A present in one supplied unit of the comparison material. Participants using a mass balance method for the assignment were also required to report their assignments of the impurity components present in the material. Methods used by the seventeen participating NMIs or DIs were predominantly based on either stand-alone mass balance (summation of impurities) or qNMR approaches, or the combination of data obtained using both methods. The results obtained using thermal methods based on freezing-point depression methods were also reported by a limited number of participants. There was excellent agreement between assignments obtained using all three approaches to assign the BPA content.
The assignment of the values for the mass fraction content of BPA consistent with the KCRV was achieved by most of the comparison participants with an associated relative standard uncertainty in the assigned value in the range (0.1 - 0.5)%.
The present work shows results on elemental distribution analyses in Cu(In,Ga)Se2 thin films for solar cells performed by use of wavelength-dispersive and energy-dispersive X-ray spectrometry (EDX) in a scanning electron microscope, EDX in a transmission electron microscope, X-ray photoelectron, angle-dependent soft X-ray emission, secondary ion-mass (SIMS), time-of-flight SIMS, sputtered neutral mass, glow-discharge optical emission and glow-discharge mass, Auger electron, and Rutherford backscattering spectrometry, by use of scanning Auger electron microscopy, Raman depth profiling, and Raman mapping, as well as by use of elastic recoil detection analysis, grazing-incidence X-ray and electron backscatter diffraction, and grazing-incidence X-ray fluorescence analysis. The Cu(In,Ga)Se2 thin films used for the present comparison were produced during the same identical deposition run and exhibit thicknesses of about 2 µm. The analysis techniques were compared with respect to their spatial and depth resolutions, measuring speeds, availabilities, and detection limits.
Data science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DTw) able to mirror or predict the performance of its corresponding physical twins. Therefore, the main target of the Topical Collection is exploring how the development of DTw can benefit the development of safe, efficient solutions for the pre-disposal and disposal of radioactive waste. A particular challenge for DTw in radioactive waste management is the combination of concepts from geological modelling and underground construction which will be addressed by linking structural and multi-physics/chemistry process models to building or tunnel information models. As for technical systems, engineered structures a variety of DTw approaches already exist, the development of DTw concepts for geological systems poses a particular challenge when taking the complexities (structures and processes) and uncertainties at extremely varying time and spatial scales of subsurface environments into account.