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In our current view, reference datasets in the MSE domain represent specific material properties, e.g., structural, mechanical, … characteristics. A reference dataset must fulfill high-quality standards, not only in precision of measurement but also in a comprehensive documentation of material, processing, and testing history (metadata). This Infrastructure Use Case (IUC) aims to develop a framework for generating reference material datasets using creep data of a single crystal Ni-based superalloy as a best practice example. In a community-driven process, we aim to encourage the discussion and establish a framework for the creation and distribution of reference material datasets. In this poster presentation, we highlight our current vision and activities and intend to stimulate the discussion about the topic reference datasets and future collaborations and work.
In our current view, reference datasets in the MSE domain represent specific material properties, e.g., structural, mechanical, … characteristics. A reference dataset must fulfill high-quality standards, not only in precision of measurement but also in a comprehensive documentation of material, processing, and testing history (metadata). This Infrastructure Use Case (IUC) aims to develop a framework for generating reference material datasets using creep data of a single crystal Ni-based superalloy as a best practice example. In a community-driven process, we aim to encourage the discussion and establish a framework for the creation and distribution of reference material datasets. In this poster presentation, we highlight our current vision and activities and intend to stimulate the discussion about the topic reference datasets and future collaborations and work.
Nanoforms with at least one dimension below 100 nm have an important part to play in more and more areas of our daily life. Therefore, risk assessment of these materials is becoming increasingly important. In this context, the European Chemical Agency (ECHA) considered eleven physico-chemical properties as relevant, of which the following six are essential for the registration: chemical composition, crystallinity, particle size, particle shape, surface chemistry and specific surface area. Four of these priority properties can be obtained with electron microscopy and surface analytics like XPS and ToF-SIMS. The reliability of this data must be ensured, especially for their use for grouping and read across approaches. On the other hand, the “reproducibility” crisis has revealed major shortcomings in the reliability of published data.
In a case study, we show how the quality of the data can be ensured by using existing standards and protocols of each step in the workflow of sample characterization. As exemplary samples, two Al-coated TiO2 samples as nanopowders were selected from the JRC repository, capped either with a hydrophilic or a hydrophobic organic ultrathin shell. SEM results provided the size and shape of the nanoparticles, a first overview about the composition was obtained with EDS. XPS and ToF-SIMS supplied the surface chemistry, especially information about the shell and the coating of the particles. Standards and protocols of all steps of the analytical workflow including preparation and data reduction are discussed regarding reliable and reproducible data. Additionally, uncertainties for the different steps are specified.
Only such a detailed description of all these factors allows a comprehensive physico-chemical characterization of the nanoparticles with understanding of their potential risk assessment.
Based on practical examples of analysis with Electron Microscopy and Energy-Dispersive X-ray Spectroscopy, the minimum information requirements for reliable and reproducible nanomaterial characterization data such as particle size and shape distribution and elemental analysis are presented and discussed.