Ingenieurwissenschaften und zugeordnete Tätigkeiten
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- 2022 (4) (entfernen)
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- Sample preparation (4) (entfernen)
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XPS of GR2M
(2022)
Quantitative Microstructural Analysis - VAMAS TWA 37 & Liaison with ISO/TC 202 Microbeam Analysis
(2022)
The progress in activities on Microbeam Analysis under VAMAS/TWA 37 is reviewed. Particularly the liaison with the new projects within the ISO technical committee TC 202 is presented and discussed with respect to the possibility of identifying and launching related VAMAS projects. The recently started project FIB sample processing for TEM is highlighted. Need of more promotion for the engagement of more participants from industry and academia at national, European and international level is highlighted. Also, the competition with the other technical working areas (on 'nano' or materials-related) is critically discussed. Further, a short overview of the VAMAS areas of activities is given where Germany is involved. Planed regional VAMAS Workshops in Germany in 2023 are announced.
Experience gained at BAM within the European project ISGScope on the accurate measureemnt of graphene oxide flakes by electron microscopy is presented. Particularly, factors such as the type of solvent, substrate temperature, but also proper, gentle measurement parameters and image analysis conditions towards automation are highlighted. The measurement procedures in develeopment are being prepared to launch an inter-laboratory comparison under VAMAS (TWA41) as preparatory guidance for future standardisation at ISO.
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by au-tomation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been de-veloped, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.