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In this chapter sample preparation, image acquisition, and nanoparticle size and shape characterization methods using the scanning electron microscope (SEM) in reflective and transmitted working modes are described. These help in obtaining reliable, highly repeatable results. The best solutions vary case-by-case and depend on the raw (powdered or suspension) nanoparticle material, the required measurement uncertainty and on the performance of the SEM.
In this work, we present various promising sample preparation procedures aiming to avoid drying artifacts and guarantee homogeneously distributed NP samples for imaging techniques like TEM, SEM or AFM. This involves retarded drying in an environment of high relative humidity as well as spin coating or freeze drying of the sample on the substrate.
Depending on the type of the NPs, different techniques are successful in obtaining homogenous sample deposition. Simple drying at ambient conditions often results in heavily agglomerated NPs densely packed in coffee ring traces. Retardation of the drying speed in an environment of high relative humidity, however, yields homogeneous NP deposition in the case of bimodal SiO2 and bimodal gold NP’s. In contrast, a sample of bipyramidal TiO2, heavily agglomerated when commonly dried, is successfully dispersed by spin coating the suspension on the substrate. The analysis of the particle size distribution of the bimodal silica and gold samples yielded number ratios resembling the nominal number ratios. The reliable measurement of the NP concentration of polydisperse samples by electron microscopy, even if relative, is new and of special relevance because up to now only the size of the particles, i.e. the abscissa of the PSD, can be measured reliably.
Various promising sample preparation procedures aiming to avoid drying artifacts and guarantee homogeneously distributed NP samples for imaging techniques like TEM, SEM or AFM are presented. This involves retarded drying in an environment of high relative humidity as well as spin coating or freeze drying of the sample on the substrate.
Depending on the type of the NPs, different techniques are successful in obtaining homogenous sample deposition. Simple drying at ambient conditions often results in heavily agglomerated NPs densely packed in coffee ring traces. Retardation of the drying speed in an environment of high relative humidity, however, yields homogeneous NP deposition in the case of bimodal SiO2 and bimodal gold NP’s. In contrast, a sample of bipyramidal TiO2, heavily agglomerated when commonly dried, is successfully dispersed by spin coating the suspension on the substrate.
The analysis of the particle size distribution of the bimodal silica and gold samples yielded number ratios resembling the nominal number ratios. The reliable measurement of the NP concentration of polydisperse samples by electron microscopy, even if relative, is new and of special relevance because up to now only the size of the particles, i.e. the abscissa of the PSD, can be measured reliably.
In this paper one refined approach is applied to determine the exact orientation of bipyramidal TiO2 nanoparticles prepared with good dispersion as almost isolated particles on a carbon TEM grid. The advantages of the recently developed high-throughput Transmission Kikuchi Diffraction (TKD) are used to identify quickly and reliably the geometrical orientation of the crystalline TiO2 nanoparticle bipyramids (anatase) on a statistically relevant number of particles.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
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.
In this paper, the accurate determination of the size and size distribution of bipyramidal anatase nanoparticles (NPs) after deposition as single particles on a silicon substrate by correlative Scanning Electron Microscopy (SEM) with Atomic Force Microscopy (AFM) analysis is described as a new measurement procedure for metrological purposes. The knowledge of the exact orientation of the NPs is a crucial step in extracting the real 3D dimensions of the particles. Two approaches are proposed to determine the geometrical orientation of individual nano‐bipyramides: (i) AFM profiling along the long bipyramid axis and (ii) stage tilting followed by SEM imaging. Furthermore, a recently developed method, Transmission Kikuchi Diffraction (TKD), which needs preparation of the crystalline NPs on electron‐transparent substrates such as TEM grids, has been tested with respect to its capability of identifying the geometrical orientation of the individual NPs. With the NPs prepared homogeneously on a TEM grid, the transmission mode in a SEM, i.e., STEM‐in‐SEM (or T‐SEM), can be also applied to extract accurate projection dimensions of the nanoparticles from the same sample area as that analysed by SEM, TKD and possibly AFM. Finally, Small Angle X‐ray Scattering (SAXS) can be used as an ensemble technique able to measure the NPs in liquid suspension and, with ab‐initio knowledge of the NP shape from the descriptive imaging techniques, to provide traceable NP size distribution and particle concentration.
Ellipsometry-based approach for the characterization of mesoporous thin films for H2 technologies
(2021)
Porous thin layer materials are gaining importance in different fields of technology and pose a challenge to the accurate determination of materials properties important for their function. In this work, we demonstrate a hybrid measurement technique using ellipsometry together with other independent methods for validation. Ellipsometry provides information about the porosity of different mesoporous films (PtRuNP/OMC = 45%; IrOx = 46%) as well as about the pore size (pore radius of ca. 5 nm for PtRuNP/OMC). In addition, the electronic structure of a material, such as intraband transitions of a mesoporous IrOx film, can be identified, which can be used to better understand the mechanisms of chemical processes. In addition, we show that ellipsometry can be used as a scalable imaging and visualization method for quality assurance in production. These require accurate and traceable measurements, with reference materials playing an important role that include porosity and other related properties. We show that our novel analytical methods are useful for improving analytical work in this entire field.
A case study on the TEM analysis of the size and shape distribution of TiO2 bipyramidal nanoparticles prepared on TEM grids was included in the recently published ISO standard ISO 21363. It was agreed to organize at a later stage a second inter-lab comparison with the nanoparticles distributed to the participants as a liquid suspension. Protocols for uniform nanoparticle deposition on suited supports developed and optimized within the EMPIR nPSize project are also prepared to be distributed. For this, we have chosen the VAMAS platform (www.vamas.org) which offers an excellent international infrastructure of laboratories with high competence in nanoparticle measurement. The VAMAS technical working area dedicated to nanoparticle measurement is TWA 34 ‘Nanoparticle populations’. For this type of nanoparticles, the size and shape distributions are the primary parameters to be reported. Due to the good deposition protocols developed, an automated image analysis is enabled (in contrast to the manual analysis of irregular TiO2 nanoparticles.
In parallel with the TiO2 nanoparticle exercise, two spherical SiO2 nanoparticle samples with bi-modal size distributions (nominal relative number concentrations of 1:1 and 10:1) are prepared for a second VAMAS inter-lab comparison. Here, the nanoparticle concentration is the primary parameter to be measured. For the imaging methods it is targeted to measure the relative nanoparticle concentrations of the two modes. For the absolute nanoparticle concentration to be measured by imaging methods it is necessary to control the volume of the liquid suspension deposited on the substrate and to obtain such a homogeneous nanoparticle deposition on the substrate which allows to count (or extrapolate) all the deposited particles.
A particle roughness analysis tool, based on electron microscopy (EM) images. The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
Welcome to the poster Towards Determination of Surface Roughness from a SEM Image, a contribution from BAM in Berlin, Germany. This work is part of the MamaLoCA project, which aims to develop a biosensor for the detection of mycotoxins in cereals. Biosensors come in a great variety, one of which makes use of microscopic beads produced by homogenous coating of polystyrene microspheres. The beads are functionalized by decorating them with bioreceptors – in our case antibodies – which then specifically react with the analyte – in our case mycotoxins – and emit an electrical or optical signal. The functionalization of the beads depends on the surface roughness because this determines the amount and orientation of binders. In other words, the surface roughness affects the accessibility to the binding sites and influences device sensitivity, hence its quantitative determination is an important step in evaluating the quality of the biosensor in general.
The presented solution to the problem of the estimation of surface roughness relies in the repetitive characteristics on the surface of the beads. A SEM image of the bead shows a raspberry like microparticle with a variation of grayscale values, which arise from the secondary electron yield. The principle of the measurement measures the variation of grayscale values along a circumference of a circle centred in the centre of the particle and with an arbitrary radius. The grayscale value variation along the given circumference gives the so-called z-modulation or the lateral profile. By performing Fast Fourier Transform on this profile we obtain the power spectrum as a function of the spatial frequencies through which the grayscales vary. The maximal value for spatial frequency then reveals the most common feature along one given circumference. Surface roughness then is the feature frequency in the spatial domain. This calculation is repeated for several concentric circles with different radii over the particle.
The results for the same particle but recorded at two different accelerating voltages show that the applied method has a potential to reveal the roughness. Interpretation of results from an SE InLens SEM image obtained using 3 kV shows that surface roughness is about 21 nm, which is in a good agreement with an alternative method given in a different presentation. The results from the 10 kV are underestimated due to the loss of surface sensitivity on the SE InLens detection at high voltages.
In conclusion, this method shows promise in determining quantitatively the surface roughness from a single SEM image and its validation is being sought using 3D SEM images and AFM methods.
A particle roughness analysis tool, based on electron microscopy images (SEM and TEM). The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
A case study on the TEM analysis of the size and shape distribution of TiO2 bipyramidal nanoparticles prepared on TEM grids was included in the recently published ISO standard ISO 21363. It was agreed to organize at a later stage a second inter-lab comparison with the nanoparticles distributed to the participants as a liquid suspension. Protocols for uniform nanoparticle deposition on suited supports developed and optimized within the EMPIR nPSize project are also prepared to be distributed. For this, we have chosen the VAMAS platform (www.vamas.org) which offers an excellent international infrastructure of laboratories with high competence in nanoparticle measurement. The VAMAS technical working area dedicated to nanoparticle measurement is TWA 34 ‘Nanoparticle populations’. For this type of nanoparticles, the size and shape distributions are the primary parameters to be reported. Due to the good deposition protocols developed, an automated image analysis is enabled (in contrast to the manual analysis of irregular TiO2 nanoparticles.
In parallel with the TiO2 nanoparticle exercise, two spherical SiO2 nanoparticle samples with bi-modal size distributions (nominal relative number concentrations of 1:1 and 10:1) are prepared for a second VAMAS inter-lab comparison. Here, the nanoparticle concentration is the primary parameter to be measured. For the imaging methods it is targeted to measure the relative nanoparticle concentrations of the two modes. For the absolute nanoparticle concentration to be measured by imaging methods it is necessary to control the volume of the liquid suspension deposited on the substrate and to obtain such a homogeneous nanoparticle deposition on the substrate which allows to count (or extrapolate) all the deposited particles.
The minimum information requirements needed to guarantee high-quality surface Analysis data of nanomaterials are described with the aim to provide reliable and traceable Information about size, shape, elemental composition and surface chemistry for risk assessment approaches.
The widespread surface analysis methods electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) were considered. The complete analysis sequence from sample preparation, over measurements, to data analysis and data format for reporting and archiving is outlined. All selected methods are used in surface analysis since many years so that many aspects of the analysis (including (meta)data formats) are already standardized. As a practical analysis use case, two coated TiO2 reference nanoparticulate samples, which are available on the Joint Research Centre (JRC) repository, were selected. The added value of the complementary analysis is highlighted based on the minimum information requirements, which are well-defined for the analysis methods selected. The present paper is supposed to serve primarily as a source of understanding of the high standardization level already available for the high-quality data in surface analysis of nanomaterials as reliable input for the nanosafety community.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation
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.
It sounds like being a simple analytical task, it is definitely not. The way toward accurate measurement of the size distribution of nanoparticles (NPs) with complex shape, having a broad size polydispersity, with inhomogeneous chemistry, and with a high degree of agglomeration/aggregation is very challenging for all available analytical methods. Particularly for the NPs with complex shape, the access to the smallest dimension (as e.g. required for regulatory purposes) can be enabled only by using imaging techniques with spatial resolution at the nanoscale. Moreover, the full 3D-chacterisation of the NP shape can be provided either by advanced characterization techniques like 3D-TEM tomography or by correlative analysis, i. e. synergetic/complementary measurement of the same field-of-view of the sample with different probes. Examples of the latter type of analysis are: i) electron microscopy for the lateral dimensions and AFM for the height of the NPs, ii) SEM with STEM-in-SEM (also called T-SEM), iii) Electron Microscopy with TKD (Transmission Kikuchi Diffraction) for determination of the geometrical orientation of crystalline NPs, iv) Raman and SEM for e.g. thickness of graphen flakes, or v) Electron Microscopy for descriptive NP shape and SAXS for the NP concentration, the latter as a NP property able to be measured with higher and higher accuracy. For all these types of measurement, reference NPs are necessary for the validation of the measured size. Particularly non-spherical reference NPs are still missing. Examples of such new reference NPs as characterized by the correlative analyses enumerated above will be presented in detail in the contribution.