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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)%.
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.
A pilot study for the thickness measurement of HfO2 films was performed by the Surface Analysis Working Group (SAWG) of the Consultative Committee for Amount of Substance (CCQM). The aim of this pilot study was to ensure the equivalency in the measurement capability of national metrology institutes for the thickness measurement of HfO2 films. In this pilot study, the thicknesses of six HfO2 films with nominal thickness from 1 nm to 4 nm were measured by X-ray Photoelectron Spectroscopy (XPS), X-ray Reflectometry(XRR), X-ray Fluorescence Analysis (XRF), Transmission Electron Spectroscopy (TEM), Spectroscopic Ellipsometry (SE) and Rutherford Backscattering Spectrometry (RBS). The reference thicknesses were determined by mutual calibration of a zero-offset method (Medium Energy Ion Scattering Spectroscopy (MEIS) of KRISS) and a method traceable to the length unit (the average thicknesses of three XRR data except the thinnest film). These reference thicknesses are traceable to the length unit because they are based on the traceability of XRR. For the thickness measurement by XPS, the effective attenuation length of Hf 4f electrons was determined. In the cases of XRR and TEM, the offset values were determined from a linear fitting between the reference thicknesses and the individual data by XRR and TEM. The amount of substance of HfO2, expressed as thickness of HfO2 films (in both linear and areal density units), was found to be a good subject for a CCQM key comparison.
To reach the main text of this paper, click on Final Report.
The final report has been peer-reviewed and approved for publication by the CCQM.
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.
The prediction of the properties of the materials used in the operation of components is of enormous importance, in order to accelerate the development process of new components. To evaluate new materials in terms of their performance (in a component), the development of new innovative methods is necessary. These methods can also be summarized under the term lab-to-field or materials – upscaling, meaning materials being characterised in a laboratory and their properties being upscaled to the component performance by means of e.g. simulation. i-TRIBOMAT is a EU funded project (H2020, GA Nr. 814494) aiming at building an Open Innovation Test Bed for tribological material characterization and offering corresponding services from tribological characterization of new materials to simulation models for predicting the performance of industrial components. By bundling the infrastructure, know-how for characterization and building a digital platform, i-TRIBOMAT becomes the world’s largest open innovation test bed for tribological material characterization.
Microstructural and Mechanical Performance Assessment of Diffusion Bonded Bimetallic Model Discs
(2003)
Incomplete tomographic data sets such as limited view (missing wedge) data represent a well-known challenge
for reconstruction algorithms, since they unavoidably lead to substantial image artefacts. Such data sets may
occur in industrial computed tomography of limited access (e.g. extended components, fixed objects), directional
opacity, limited sample life time or laminographic set-up. We present strategies to effectively suppress the
typical elongation artefacts (e.g. lemon-like deformed pores) by our iterative algorithm DIRECTT which offers
the opportunity to vary the versatile reconstruction parameters within each cycle. Those strategies are applied to
experimental data obtained from metallic foams as well as model simulations. Comparison is drawn to state-ofthe-
art techniques (filtered backprojection and algebraic techniques). Further reference is made to reconstructions
of complete data sets serving as gold standards. For quantitative assessment of the reconstruction
quality adapted techniques based on spatial statistics are introduced.
Coarsening of grain-refined semi-solid Al-Ge32 alloy: X-ray microtomography and in situ radiography
(2007)
The characteristics of TiO2 coatings can greatly influence their final performance in large‐scale applications. In the present study, self‐assembly of TiO2 nanoparticles (NPs) in multiple layers was selected as a deposition procedure on various substrates. For this, the main prerequisite constitutes the surface modification of both NPs and substrate with, for example, silane coupling agents. A set of functionalized TiO2 NPs has been produced by reaction with either (3‐aminopropyl)triethoxysilane (APTES) or (3‐aminopropyl)phosphonic acid (APPA) to functionalize the NP surface with free amino‐groups. Then, the complementary functionalized NP set can be obtained from an aliquot of the first one, through the conversion of free surface amino groups to aldehydes by reaction with glutaraldehyde (GA). Several types of TiO2 NPs differing in size, shape, and specific surface area have been functionalized. Fourier‐transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), SEM/ energy‐dispersive X‐ray spectroscopy (EDS), XPS, Auger electron spectroscopy (AES), and Time‐of‐Flight (ToF)‐SIMS analyses have been carried out to evaluate the degree of functionalization, all the analytical methods employed demonstrating successful functionalization of TiO2 NP surface with APTES or APPA and GA.
Parameters of TiO2 coatings can greatly influence their final performance in largescale applications such as photocatalytic measurements, orthopedic and/or dental prostheses, cell cultures, and dye-sensitized solar cells. From different film deposition procedures, self-assembly of TiO2 NPs in multiple layers was selected for systematic characterization. EDX, AES and ToF-SIMS analysis have been carried out in order to evaluate the functionalization of several types of TiO2 NPs differing in size, shape and surface area.
Silver nanoparticles (AgNPs) nowadays appear in close to 24% of consumer products that contain engineered nanomaterials. Thus, they are expected to be released into the environment, where their fate and effect are still undetermined. Considering the evidenced efficacy of the single particle Inductively Coupled Plasma – Mass Spectrometry (sp ICP-MS) technique in the study of nanomaterials, this work reports on the use of sp ICP-MS along with an online dilution sample introduction system for the direct analysis of untreated and spiked seawater samples, as part of a larger scale experiment studying the fate of Ag (ionic and nanoparticles) in seawater mesocosm systems. Silver nanoparticles coated with branched polyethyleneimine (BPEI@AgNPs) or ionic silver (Ag+) were introduced gradually into the seawater mesocosm tanks at very low, environmentally relevant concentrations (50 ng Ag L− 1 per day, for 10 consecutive days, up to a total of 500 ng Ag L− 1 ), and samples were collected and analyzed daily, within a consistent time window. Using very low detector dwell time (75 μs) and specialized data treatment, information was obtained on the nanoparticles’ size distribution and particle number concentration, as well as the ionic silver content, of both the AgNPs and the Ag+ treated seawater mesocosm tanks. The results for the AgNP treated samples indicated the rapid degradation of the added silver particles, and the subsequent increase of ionic silver, with recoveries close to 100% for the first days of the experiment. On the other hand, particle formation was observed in the Ag+ treated seawater tanks, and even though the number concentration of silver-containing nanoparticles increased throughout the experiment, the amount of silver per particle remained relatively constant from the early days of the experiment. In addition, the online dilution sample introduction system for the ICP-MS proved capable of handling the untreated seawater matrix without significant contamination issues and downtime, while the low dwell time and data treatment procedure developed were shown to be suitable for the analysis of nanomaterials at the low nm-scale, despite the complex and heavy matrix introduced into the ICP-MS.
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.