TY - CONF A1 - Rühle, Bastian A1 - Hodoroaba, Vasile-Dan T1 - Automatic Image Segmentation and Analysis using 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 electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. 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. 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, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors. T2 - Machine Learning Workshop CY - Online Meeting DA - 18.03.2021 KW - Electron Microscopy KW - Neural Networks KW - Artificial Intelligence KW - Image Segmentation KW - Automated Image Analysis PY - 2021 AN - OPUS4-52304 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Miropoltsev, M. A1 - Wegner, Karl David A1 - Häusler, I. A1 - Hodoroaba, Vasile-Dan A1 - Resch-Genger, Ute T1 - Influence of Hydrophilic Thiol Ligands of Varying Denticity on the Luminescence Properties and Colloidal Stability of Quaternary Semiconductor Nanocrystals N2 - Binary photoluminescent semiconductor nanocrystals (quantum dots, QDs) are one of the best studied fluorescent nanomaterials, and their unique optoelectronic properties paved the road to many applications in (bio)nanophotonics, optoelectronics, and photovoltaics. However, concerns related to their toxic constituents like cadmium or lead and the emerging interest in greener chemistry synthesis approaches hamper their future applicability. Interesting alternatives for some applications like biosensing or bioimaging are heavy-metal-free ternary QDs like AgInS2 (AIS), CuInS2 (CIS), and quaternary QDs such as AIS-ZnS (ZAIS). In this context, we explored the effect of ligand denticity on the organic-to-aqueous phase transfer of oleylamine-stabilized ZAIS QDs with the hydrophilic ligands mercaptopropionic acid (MPA), dihydrolipoic acid (DHLA), and 3-mercapto-2,2-bis(mercaptomethyl)propanoic acid (3MPA), bearing mono-, bi-, and trialkyl thiol groups. Spectroscopic studies of the resulting water-dispersible ZAIS QDs revealed a considerable influence of ligand denticity and ligand-to-QD ratio on the spectral position and width (FWHM; full width at half-maximum) of the photoluminescence (PL) bands, the PL quantum yields (PL QY), and the PL decay kinetics. Thiol capping and phase transfer resulted in a loss in PL by at least a factor of 2. The ligand-induced PL quenching observed particularly for ligands bearing two or three thiol groups was attributed to the facilitated formation of surface-bound disulfides. The best colloidal stability under high dilution conditions was observed for 3MPA. KW - Quantum dots KW - Ligand exchange KW - Lifetime analysis KW - Thiols PY - 2022 DO - https://doi.org/10.1021/acs.jpcc.2c05342 SN - 1932-7447 VL - 126 IS - 47 SP - 20101 EP - 20113 PB - ACS Publications AN - OPUS4-56707 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 - CONF A1 - Radnik, Jörg T1 - Correlating HR-TEM and XPS to elucidate the core-shell structure of ultrabright CdSE/CdS semiconductor quantum dots N2 - Controlling the thickness and tightness of surface passivation shells is crucial for many applications of core-shell nanoparticles (NP). Usually, to determine shell thickness, core and core/shell particle are measured individually requiring the availability of both nanoobjects. This is often not fulfilled for functional nanomaterials such as many photoluminescent semiconductor quantum dots (QD) used for bioimaging, solid state lighting, and display technologies as the core does not show the application-relevant functionality like a high photoluminescence (PL) quantum yield. This calls for a whole nanoobject approach. Moreover, the thickness of the organic coating remains often unclear. By combining high-resolution transmission electron microscopy (HR-TEM) and X-ray photoelectron spectroscopy (XPS), a novel whole nanoobject approach is developed representatively for an ultrabright oleic acid-stabilized, thick shell CdSe/CdS QD with a PL quantum yield close to unity. The size of this spectroscopically assessed QD, is in the range of the information depth of usual laboratory XPS. Information on particle size and monodispersity were validated with dynamic light scattering (DLS) and small angle X-ray scattering (SAXS) and compared to data derived from optical measurements. The results of the different methods match very well within the different measurement uncertainties. Additionally, results obtained with energy-resolved XPS using excitation energies between 200 eV and 800 eV are discussed with respect to a potential core/shell intermixing. Moreover, the future application potential of this approach correlating different sizing and structural methods is discussed considering the method-inherent uncertainties and other core/multi-shell nanostructures. T2 - E-MRS Fall Meeting CY - Online meeting DA - 20.09.2021 KW - Core-shell nanoparticles KW - Quantum dots KW - High-resolution transmission electron microscopy KW - X-ray Photoelectron Spectroscopy PY - 2021 AN - OPUS4-53365 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Relling, Alexander T1 - Copper doped AgInS2-ZnS QDs from a single-source-precursor N2 - Quaternary semiconductor nanocrystals like AgInS2-ZnS solid solution quantum dots (QDs) are a highly promising material for material science and biomedical applications due to their tunable photoluminescence (PL), their high quantum yields (QY), and their low cytotoxicity1. A red shift of the PL into the NIR and SWIR region could further increase their application potential. Copper doping has been proven to be a suitable approach for bathochromically shifting the PL of QDs2. The synthesis of copper doped AgInS2-ZnS QDs from a single-source-precursor should enable an easily scalable synthesis with high reproducibility. T2 - Summer School "Exciting nanostructures: characterizing advanced confined systems" CY - Bad Honnef, Germany DA - 18.07.2021 KW - Nanocrystals KW - Quantum dots KW - Doping KW - AgInS2 KW - Single-source-precursor PY - 2021 AN - OPUS4-53129 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - López de Ipina, J.-M. A1 - Arevalillo, A. A1 - Martín, A. A1 - Caillard, B. A1 - Marcoulaki, E. A1 - Aguerre- Charol, O. A1 - van Duuren-Stuurman, B. A1 - Hodoroaba, Vasile-Dan A1 - Viitanen, A.-K. A1 - Witters, H. A1 - Vercauteren, S. A1 - Persson, K. A1 - Bard, D. A1 - Evans, G. A1 - Jensen, K.A. A1 - Himly, M. A1 - Scalbi, S. A1 - Papin, A. A1 - Le Bihan, O. A1 - Kanerva, T. A1 - Tirez, K. A1 - Frijns, E. A1 - Niga, P. A1 - Eleftheriadis, K. A1 - Travlos, A. A1 - Geppert, M. A1 - Himly, M. A1 - Radnik, Jörg A1 - Kuchenbecker, Petra A1 - Resch-Genger, Ute A1 - Fraboulet, I. A1 - Bressot, C. A1 - Rissler, J. A1 - Gaucher, R. A1 - Binotto, G. A1 - Krietsch, Arne A1 - Braun, A. A1 - Abenet, S. A1 - Catalan, J. A1 - Verstraelen, S. A1 - Manier, N. A1 - Manzo, S. A1 - Fransman, S. A1 - Queron, J. A1 - Charpentier, D. A1 - Taxell, D. A1 - Säämänen, A. A1 - Brignon, J.-M. A1 - Jovanovic, A. A1 - Bisson, M A1 - Neofytou, P. T1 - EC4Safenano - Catalogue of Services N2 - The publicly available document encapsulates the first version of the Catalogue of Services of the future EC4Safenano Centre (CoS 2019). The CoS 2019 is structured in 12 Service Categories and 27 Service Topics, for each of the 12 categories considered. This architecture configures a 12 x 27 matrix that allows ordering the potential EC4Safenano offer in 324 types of services/groups of services. Each type of service/group of services is described, in a simple and friendly way, by means of a specific service sheet: the EC4Safenano - Service Data Sheet (EC4-SDS). These EC4-SDSs allow structuring and summarizing the information of each service, providing the customer with a concise view of characteristics of the service and also the contact details with the service provider. The CoS 2019 deploys a map of services consisting of a set of 100 EC4-SDSs, covering 7 of the 12 Service Categories and 17 of the 27 Service Topics. The harmonization of services is visualized as a future necessary step in EC4Safenano, in order to strengthen the offer and provide added value to customers with a growing offer of harmonized services in future versions of the CoS. The information contained in this document is structured in 3 main sections, as follows: • Catalogue structure. This section describes in short the main characteristics of the CoS 2019. • Catalogue content. This section represents the core part of the document and encapsulates the set of 100 SDSs displaying the offer proposed by the CoS 2019. • Online Catalogue. This section describes the resources implemented by EC4Safenano to facilitate the on-line consultation of the CoS 2019 by customers and other interested parties. KW - Nano-safety KW - Analytical services KW - Nanomaterials KW - Catalogue of services KW - EC4SafeNano KW - European Centre PY - 2021 UR - https://ec4safenano.eu-vri.eu/Public/Guidance SP - 1 EP - 72 PB - EU-VRi – European Virtual Institute for Integrated Risk Management CY - Stuttgart, Germany AN - OPUS4-52943 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 - CONF 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. T2 - Microscopy and Microanalysis 2020 CY - Online meeting DA - 03.08.2020 KW - Nanoparticles KW - Automatisation KW - Image segmentation KW - Convolutional neural networks KW - Electron microscopy PY - 2020 AN - OPUS4-51114 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Resch-Genger, Ute T1 - Perspective of BAM on Advanced Materials N2 - A brief perspective of BAM on advanced materials is presented including examples for nanomaterials and other systems presenting advanced materials with special emphasis on characterization methods used in different division of BAM for the determination of functional or safety parameters of such materials. In this respect, also ongoing activities of the capacity building project nanoplatform of BAM aiming at the development of nanometer-sized reference materials are briefly summarized. T2 - BfR Workshop zu Advanced Materials CY - Online meeting DA - 04.11.2020 KW - Quality assurance KW - Risk assessment KW - Safety KW - Core/shell particle KW - Characterization KW - Measurement uncertainty KW - Method KW - Nano particle PY - 2020 AN - OPUS4-51620 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -