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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.
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.
The main outcomes of the EMPIR project nPSize are presented and the suitability of the new capabilities (e.g. reference materials, measurement procedures, inter-laboratory comparisons) as pertinent contributions to normative projects within CEN/TC 352 Nanotechnologies are discussed. E.g. the first technical report of nPSize on full algorithm sequences for nanoparticle detection and size measurement as developed on both a physical basis (SEM, TSEM, AFM and SAXS) and by machine learning is put at disposal.
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
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.
Nanomaterials and nanoparticles are finding applications across a wide range of technology sectors, from medicine and food to transportation and construction. In order to assess these new materials for potential risks to health and the environment, they need to be well-characterised. The measurement of constituent nanoparticle size, shape, and size distribution are important factors for the risk evaluation process.
EMPIR project Improved traceability chain of nanoparticle size measurements (17NRM04, nPSize) is working to assess a range of traceable nanoparticle measurement approaches, including Scanning Electron Microscopy (also in Transmission Mode), Atomic Force Microscopy and Small Angle X-ray Scattering, and deliver improved calibration methods to users. For the techniques under investigation, physical models of their response to a range of nanoparticle types are developed. Validated reference materials will also be used for an inter-comparison of measurement systems, with an evaluation of the associated measurement uncertainty. With project contributions to standards development work, manufacturers will be better placed to assess the human and environmental risks posed by nanomaterials across a whole range of products.
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 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.
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.
Gold nanocubes with a monodispersed size distribution (edge = 55 nm) deposited on a silicon wafer. These tiny uniform cubes were produced as reference nanoparticles within the EMPIR project nPSize - Improved traceability chain of nanoparticle size measurements. SEM image acquired at 10 kV with an in-lens secondary electron detector. Image colored in ImageJ using LUT “Orange hot.” Published in Microscopy and Microanalysis 25(S2) (2019) 2328.
Gold nanocubes with a monodispersed size distribution (edge = 55 nm) deposited on a silicon wafer. These tiny uniform cubes were produced as reference nanoparticles in the frame of the European project nPSize - Improved traceability chain of nanoparticle size measurements. SEM Image acquired at 10 kV with an in-lens secondary electron detector. Image colored in ImageJ using LUT “Orange hot.” Published in Microscopy and Microanalysis 25(S2) (2019) 2328.
Understanding the correlation between the morphological and functional properties of particulate materials is crucial across all fields of physical and natural sciences. This manuscript reports on the investigation of the effect of polyethylene glycol (PEG) employed as a capping Agent in the synthesis of SrTiO3 crystals. The crucial influence of PEG on both the shape and size of the strontium titanate particles is revealed, highlighting the effect on the photocurrents measured under
UV–Vis irradiation.
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.
The present study addresses the capability of measurement of relative particle number concentration by scanning electron microscopy for model bimodal silica and gold samples prepared in the frame of the European research project “nPSize - Improved traceability chain of nanoparticle size measurements” as candidate reference nanoparticles.
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.
Challenges in Traceable Size Measurement of Non-Spherical, Non-Monodisperse Nanoparticles - nPSize
(2019)
Size measurement of nanoparticles (NP) becomes a challenging analytical problem when non-spherical shapes must be traceably measured. However, most industrial NPs have irregular shapes and broad size distribution making it often more complex to follow European regulatory to identify a material as a nanomaterial according to which accurate measurement of the smallest dimension and its size Distribution is necessary.
The European research project nPSize - Improved traceability chain of nanoparticle size measurements aims to fill this gap by developing potential non-spherical reference nanoparticles, measurement procedures and physical modelling to improve the traceability chain, comparability and compatibility for NP size measurements between different methods. Therefore, new model NP with well-controlled shape has been synthesized and are supposed to be systematically characterized using the traceable methods scanning/transmission electron microscopy, atomic force microscopy and small angle X-ray scattering.
Following NP candidates are under investigation with respect to their homogeneity and stability: (i) titania nanoplatelets (10-15 nm thickness x 50-100 nm lateral), (ii) titania bipyramides (~60 nm length x 40 nm width), (iii) titania acicular particles (100 nm length x 15-20 nm width; aspect ratio 5.5/6), (iv) gold nanorods (~10 nm width x 30 nm length), and (v) gold nanocubes (~55 nm x 55 nm x 55 nm). In addition, sample preparation procedures as well as measurement analysis procedures with evaluation of appropriate measurands and descriptors for each material class and method are being developed to support standardization. To underpin the traceability of the size measurement of nonspherical NP, physical modelling of the signals in e.g. electron microscopy techniques will be used and in combination, the implementation of machine learning is aimed to facilitate measurement Analysis procedures, especially regarding the accurate thresholding/segmentation of the NPs.zeige mehr
Extraction of true, 3D shape (and size) of non-spherical nanoparticles (NPs) is associated with Errors by conventional 2D electron microscopy using projection images. Significant efforts within the ISO technical committee TC 229 ‘Nanotechnologies’ are aimed at establishing accurate TEM and SEM measurement of NP size and shape as robust, standard procedures. Study groups have been organizing inter-laboratory comparisons on well-selected NP systems according to the market needs, such as aggregated titania nano-powder for which size and shape distribution of primary crystallites of irregular shape must be measured accurately. To be noticed is e. g. the fact that the measurement procedure allows only manual selection of the particles clearly distinguishable for analysis as well as manual definition of the contour of the imaged NPs.
An inter-laboratory exercise on titania NPs (pure anatase, grown by hydrothermal synthesis) of well-defined non-spherical shape, i.e. bipyramidal, has been recently started within ISO/TC 229 under similar conditions as for the irregular shaped titania. In the TEM micrograph the particles tracked manually according to the measurement protocol. Overlapped particles were allowed to be considered, as long as they are clearly distinguishable. One decisive NP selection criterion was to analyze only those NPs with a roundness value below 0.7, i.e. the NPs laying on the support foil and, hence, with projection areas clearly deviating from perfect circles (R=1). The overall evaluation (for 15 labs) of the size descriptors (area, Feret, minFeret, perimeter) and shape descriptors (aspect ratio, roundness, compactness, extent) by analysis of variance is just to be finished and included in ISO/WD 21363 Nanotechnologies -- Protocol for particle size distribution by transmission electron microscopy.
New reference material candidates for traceable size measurement of nonspherical nanoparticles
(2019)
Size measurement of nanoparticles (NP) becomes a challenging analytical problem when non-spherical shapes must be traceably measured. However, most industrial NPs have irregular shapes and broad size distribution making it often more complex to follow European regulatory to identify a material as a nanomaterial according to which accurate measurement of the smallest dimension and its size Distribution is necessary.
The European research project nPSize - Improved traceability chain of nanoparticle size measurements aims to fill this gap by developing potential non-spherical reference nanoparticles, measurement procedures and physical modelling to improve the traceability chain, comparability and compatibility for NP size measurements between different methods. Therefore, new model NP with well-controlled shape has been synthesized and are supposed to be systematically characterized using the traceable methods scanning/transmission electron microscopy, atomic force microscopy and small angle X-ray scattering.
Following NP candidates are under investigation with respect to their homogeneity and stability: (i) titania nanoplatelets (10-15 nm thickness x 50-100 nm lateral), (ii) titania bipyramides (~60 nm length x 40 nm width), (iii) titania acicular particles (100 nm length x 15-20 nm width; aspect ratio 5.5/6), (iv) gold nanorods (~10 nm width x 30 nm length), and (v) gold nanocubes (~55 nm x 55 nm x 55 nm). In addition, sample preparation procedures as well as measurement analysis procedures with evaluation of appropriate measurands and descriptors for each material class and method are being developed to support standardization. To underpin the traceability of the size measurement of nonspherical NP, physical modelling of the signals in e.g. electron microscopy techniques will be used and in combination, the implementation of machine learning is aimed to facilitate measurement Analysis procedures, especially regarding the accurate thresholding/segmentation of the NPs.