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Reliable measurement of the size of polydisperse, complex-shaped commercial nanopowders is a difficult but necessary task, e.g., for regulatory requirements and toxicity risk assessment. Suitable methods exist for the accurate characterization of the size of non-aggregated, stabilized, spherical and monodisperse nanoparticles. In contrast, industrial nanoscale powders usually require dedicated sample preparation procedures developed for the analysis method of choice. These nano-powders tend to agglomerate and/or aggregate, a behavior which in combination with an innate broad particle size distribution and irregular shape often significantly alters the achievable accuracy of the measured size parameters. The present study systematically tests two commercially available nanoscale powders using different sample preparation methods for correlative analysis by scanning electron microscopy, dynamic light scattering, Brunauer–Emmet–Teller method and differential mobility analysis. One focus was set on the sample preparation by embedding nanoparticles in carbon-based hot-mounting resin. Literature on this topic is scarce and the accuracy of the data extracted from cross sections of these particles is unclearly stated. In this paper systematic simulations on the deviation of the size parameters of well-defined series of nanoparticles with different shapes from the nominal value were carried out and the contributing factors are discussed.
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.
Sample preparation for analytical scanning electron microscopy using initial notch sectioning
(2021)
A novel method for broad ion beam based sample sectioning using the concept of initial notches is presented. An adapted sample geometry is utilized in order to create terraces with a well-define d step in erosion depth from the surface. The method consists of milling a notch into the surface, followed by glancing-angle ion beam erosion, which leads to preferential erosion at the notch due to increased local surface elevation. The process of terrace formation can be utilized in sample preparation for analytical scanning electron microscopy in order to get efficient access to the depth-dependent microstructure of a material. It is demonstrated that the method can be applied to both conducting and non-conducting specimens. Furthermore, experimental parameters influencing the preparation success are determined. Finally, as a proof-of-concept, an electron backscatter diffraction study on a surface crystallized diopside glass ceramic is performed, where the method is used to analyze orientation dependent crystal growth phenomena occurring during growth of surface crystals into the bulk.
As part of the development of a library of accurate and efficient methods for measurement of nanoparticle properties, we develop and optimize a method for the efficient analysis of nanoparticle size distribution from suspensions via microprinting and digital analysis of electron microscopy (SEM and TEM) images, with the ultimate aim of automated quantitative concentration analysis (calculated from drop volume). A series of different nanoparticle suspensions (gold, latex, and SiO2 in varying sizes and concentrations) were printed onto TEM grids in a 4 x 4 array in the concentration range 7x10^8 to 1x10^11 nanoparticles/mL and imaged with SEM. Concentrations and printing conditions (temperature, relative humidity) were varied in order to minimize the coffee-ring effect.
Modern EBSD systems perform diffraction pattern “quality” (PQ) calculations, essentially measurements of the contrast of the brighter bands in the pattern above background, for every pattern analyzed. Since the calculations are independent of EBSD indexing, data are generated from all points on the analyzed sample surface, regardless of indexability or the state of the material beneath. EBSD maps generated from PQ data are like microstructurally sensitive SEM images, collected at EBSD speeds and grid resolutions, with contrast arising from phase density, crystal structure, crystallographic orientation, grain boundary location, near-surface plastic strain, coating density/thickness, and to some extent topography. Grain boundaries and other aspects of sample microstructure are readily revealed, so the PQ map is a very useful characterization tool in its own right, and serves as a reference for EBSD maps generated from indexing-derived data, such as orientation, grain boundary character, phase distribution and strain maps.