TY - JOUR A1 - Pellegrino, Francesco A1 - Maurino, V. A1 - Hodoroaba, Vasile-Dan T1 - Morphological Characterization and Chemical Identification of TiO2 Nanoparticles Doped with Ultrafine Metal Particles for Enhanced Photocatalytical Activity N2 - The conversion of solar energy into electricity and solar fuels is of crucial importance for a green and sustainable future. Water splitting using semiconductor photo-catalysts is considered a sustainable method to produce clean hydrogen (H2) fuel. Nevertheless, H2 photo-production efficiency remains still low, although extensive research works to understand better the mechanisms of the Hydrogen Evolution Reaction (HER) and the Oxygen Evolution Reaction (OER) are being carried out. In this respect, TiO2 is a key photoactive material, usually employed with a co-catalyst deposited onto the surface to enhance charge carriers’ separation and catalyze surface charge transfer reactions. The deposition of a co-catalyst on the TiO2 nanoparticle surface represents one successful way to enhance the activity of the photocatalyst through a modification of its surface and redox properties. In this context, high-resolution scanning electron microscopy coupled with elemental analysis by energy-dispersive X-ray spectroscopy (EDS) is fundamental for studying and understanding the effect of the nanoparticle morphology on the functional properties of shape-controlled TiO2 crystals (bipyramides, platelets, and elongated particles). Different types of metal-semiconductor combinations, TiO2 shapes and dopant metals (Ag, Pt, etc) and metal concentrations will be discussed. KW - Nanoparticles KW - Photocatalysis KW - Electron microscopy KW - EDS KW - Metal-semiconductor PY - 2022 DO - https://doi.org/10.1017/S1431927622010078 VL - 28 IS - Suppl. 1 SP - 2658 EP - 2660 PB - Cambridge University Press AN - OPUS4-55436 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Porta-Velilla, L. A1 - Turan, N. A1 - Cubero, Á. A1 - Shao, W. A1 - Li, H. A1 - de la Fuente, G.F. A1 - Martínez, E. A1 - Larrea, Á. A1 - Castro, M. A1 - Koralay, H. A1 - Çavdar, Ş. A1 - Bonse, Jörn A1 - Angurel, L.A. T1 - Highly Regular Hexagonally-Arranged Nanostructures on Ni-W Alloy Tapes upon Irradiation with Ultrashort UV Laser Pulses N2 - Nickel tungsten alloy tapes (Ni—5 at% W, 10 mm wide, 80 µm thick, biaxially textured) used in second-generation high temperature superconductor (2G-HTS) technology were laser-processed in air with ultraviolet ps-laser pulses (355 nm wavelength, 300 ps pulse duration, 250–800 kHz pulse repetition frequency). By employing optimized surface scan-processing strategies, various laser-generated periodic surface structures were generated on the tapes. Particularly, distinct surface microstructures and nanostructures were formed. These included sub-wavelength-sized highly-regular hexagonally-arranged nano-protrusions, wavelength-sized line-grating-like laser-induced periodic surface structures (LIPSS, ripples), and larger irregular pyramidal microstructures. The induced surface morphology was characterized in depth by electron-based techniques, including scanning electron microscopy (SEM), electron back scatter diffraction (EBSD), cross-sectional transmission electron microscopy (STEM/TEM) and energy dispersive X-ray spectrometry (EDS). The in-depth EBSD crystallographic analyses indicated a significant impact of the material initial grain orientation on the type of surface nanostructure and microstructure formed upon laser irradiation. Special emphasis was laid on high-resolution material analysis of the hexagonally-arranged nano-protrusions. Their formation mechanism is discussed on the basis of the interplay between electromagnetic scattering effects followed by hydrodynamic matter re-organization after the laser exposure. The temperature stability of the hexagonally-arranged nano-protrusion was explored in post-irradiation thermal annealing experiments, in order to qualify their suitability in 2G-HTS fabrication technology with initial steps deposition temperatures in the range of 773–873 K. KW - Laser-induced periodic surface structures (LIPSS) KW - Ultrashort laser processing KW - Hexagonally-arranged nano-protrusions KW - Second-generation high temperature superconductor technology KW - Electron microscopy PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-552551 DO - https://doi.org/10.3390/nano12142380 SN - 2079-4991 VL - 12 IS - 14 SP - 1 EP - 23 PB - MDPI CY - Basel, Switzerland AN - OPUS4-55255 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Radnik, Jörg A1 - Kersting, R. A1 - Hagenhoff, B. A1 - Bennet, Francesca A1 - Ciornii, Dmitri A1 - Nymark, P. A1 - Grafström, R. A1 - Hodoroaba, Vasile-Dan T1 - Reliable Surface Analysis Data of Nanomaterials in Support of Risk Assessment Based on Minimum Information Requirements N2 - 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. KW - Electron microscopy KW - X-ray photoelectron spectroscopy KW - Secondary ion mass spectrometry KW - Energy dispersive X-ray spectroscopy KW - Standardization PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-522683 DO - https://doi.org/10.3390/nano11030639 VL - 11 IS - 3 SP - 639 PB - MDPI AN - OPUS4-52268 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rühle, Bastian A1 - Hodoroaba, Vasile-Dan T1 - Towards Automated Electron Microscopy Image Segmentation for Nanoparticles of Complex Shape by Convolutional Neural Networks N2 - 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. KW - Nanoparticles KW - Convolutional neural networks KW - Image segmentation KW - Electron microscopy KW - Automatisation PY - 2020 DO - https://doi.org/10.1017/S1431927620017262 VL - 26 IS - S2 SP - 1188 EP - 1189 PB - Cambridge University Press CY - Cambridge, UK AN - OPUS4-51773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rühle, Bastian A1 - Krumrey, Julian Frederic A1 - Hodoroaba, Vasile-Dan T1 - Workflow towards automated segmentation of agglomerated, non‑spherical particles from electron microscopy images using artificial neural networks N2 - 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. KW - Electron microscopy KW - Neural networks KW - Artificial intelligence KW - Image segmentation KW - Automated image analysis PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-522454 DO - https://doi.org/10.1038/s41598-021-84287-6 VL - 11 IS - 1 SP - 4942 PB - Springer Nature AN - OPUS4-52245 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Rühle, Bastian A1 - Krumrey, Julian Frederic A1 - Hodoroaba, Vasile-Dan T1 - Dataset accompanying the publication "Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks" N2 - 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 KW - Electron microscopy KW - Neural networks KW - Image segmentation KW - Automated image analysis PY - 2021 DO - https://doi.org/10.5281/zenodo.4563942 PB - Zenodo CY - Geneva AN - OPUS4-52246 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sachse, René A1 - Moor, Maëlle A1 - Kraehnert, Ralph A1 - Hodoroaba, Vasile-Dan A1 - Hertwig, Andreas T1 - Ellipsometry-based approach for the characterization of mesoporous thin films for H2 technologies N2 - 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. KW - Porous materials KW - Electrolysis KW - Spectroscopic ellipsometry KW - Hybrid metrology measurement KW - Electron microscopy PY - 2021 DO - https://doi.org/10.1002/adem.202101320 SP - 1 EP - 17 PB - Wiley-VCH AN - OPUS4-53960 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Vladár, A. E. A1 - Hodoroaba, Vasile-Dan ED - Hodoroaba, Vasile-Dan ED - Unger, Wolfgang ED - Shard, A. G. T1 - Characterization of nanoparticles by scanning electron microscopy N2 - 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. KW - Nanoparticles KW - Sample preparation KW - Electron microscopy KW - SEM KW - Size measurement KW - Shape KW - Threshold PY - 2020 SN - 978-0-12-814182-3 DO - https://doi.org/10.1016/B978-0-12-814182-3.00002-X SP - 7 EP - 27 PB - Elsevier CY - Amsterdam AN - OPUS4-50120 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -