TY - CONF A1 - Schmid, Thomas A1 - Hodoroaba, Vasile-Dan T1 - Correlative Analysis by Raman and other Micro & Nanospectroscopic Imaging Techniques N2 - In the present talk the basics of the Raman spectroscopy and particularly of Raman microscopy are explained. Advantages and disadvantages of the method are highlighted through selected case studies. In the second part of the lecture examples of correlative imaging with electron, X-ray, ion and optical microscopies from micro- to the nanoscale are highlighted. T2 - Charisma School on Raman Harmonisation CY - Turin, Italy DA - 19.10.2022 KW - Raman KW - Correlative Imaging KW - Microscopy KW - Hyperspectral imaging PY - 2022 UR - https://amdgroup.inrim.it/events/vamas-sc-meeting-47/program-sc47 AN - OPUS4-56094 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hülagü, Deniz A1 - Tobias, Charlie A1 - Climent Terol, Estela A1 - Gojani, Ardian A1 - Rurack, Knut A1 - Hodoroaba, Vasile-Dan T1 - Generalized Analysis Approach of the Profile Roughness by Electron Microscopy with the Example of Hierarchically Grown Polystyrene–Iron Oxide–Silica Core–Shell–Shell Particles JF - Advanced Engineering Materials N2 - The roughness as a property of core–shell (CS) microparticles plays a key role in their functionality. Quantitative evaluation of the roughness of CS microparticles is, however, a challenging task with approaches using electron microscopy images being scarce and showing pronounced differences in terms of methodology and results. This work presents a generalized method for the reliable roughness determination of nonplanar specimens such as CS particles from electron microscopic images, the method being robust and reproducible with a high accuracy. It involves a self-written software package (Python) that analyzes the recorded images, extracts corresponding data, and calculates the roughness based on the deviation of the identified contour. Images of single particles are taken by a dual mode scanning electron microscopy (SEM) setup which permits imaging of the same field-of-view of the sample with high resolution and surface sensitive in SE InLens mode as well as in transmission mode (TSEM). Herein, a new type of polystyrene core–iron oxide shell–silica shell particles is developed to serve as a set of lower micrometer-sized study objects with different surface roughness; the analysis of their images by the semiautomatic workflow is demonstrating that the particles’ profile roughness can be quantitatively obtained. KW - Core–shell particles KW - Image analysis KW - Nanoparticles KW - Roughness KW - SEM KW - transmission mode PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-542576 DO - https://doi.org/10.1002/adem.202101344 SP - 1 EP - 9 PB - Wiley-VCH AN - OPUS4-54257 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 JF - Scientific reports 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 - CONF A1 - Hodoroaba, Vasile-Dan A1 - Rühle, Bastian 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 - JOUR A1 - Rühle, Bastian A1 - Hodoroaba, Vasile-Dan T1 - Towards Automated Electron Microscopy Image Segmentation for Nanoparticles of Complex Shape by Convolutional Neural Networks JF - Microscopy and Microanalysis 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 - 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 JF - Journal of Physical Chemistry C 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 - JOUR A1 - Dhamo, Lorena A1 - Carulli, F. A1 - Nickl, Philip A1 - Wegner, Karl David A1 - Hodoroaba, Vasile-Dan A1 - Würth, Christian A1 - Brovelli, S. A1 - Resch-Genger, Ute T1 - Efficient luminescent solar concentrators based on environmentally friendly Cd-free ternary AIS/ZnS quantum dots JF - Advanced optical materials N2 - Luminescent solar concentrators (LSC) allow to obtain renewable energy from building integrated photovoltaic systems. As promising efficient and long-term stable LSC fluorophores semiconductor nanocrystals like Quantum dots (QDs) with size and composition tunable optoelectronic properties have recently emerged. The most popular II/VI or IV/VI semiconductor QDs contain, however, potentially hazardous cadmium or lead ions, which is a bottleneck for commercial applications. A simple aqueous based, microwaveassisted synthesis for environmentally friendly and highly emissive AgInS2/ ZnS QDs is developed using 3-mercaptopropionic acid (MPA) and glutathione (GSH) and their incorporation into polylaurylmethacrylate (PLMA) polymer slabs integrable in LSC devices (10.4 × 10.4 × 0.2 cm3, G = 12.98). With this simple approach, optical power efficiencies (OPE) of 3.8% and 3.6% and optical quantum efficiencies (OQE) of 24.1% and 27.4% are obtained, which are among the highest values yet reported. KW - Solar energy KW - Solar concentrator KW - Quantum dots KW - Advanced material KW - Quantum yield PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529735 DO - https://doi.org/10.1002/adom.202100587 SN - 2195-1071 SN - 0935-9648 VL - 9 IS - 17 SP - 1 EP - 8 PB - Wiley-VCH CY - Weinheim AN - OPUS4-52973 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Dhamo, Lorena A1 - Wegner, Karl David A1 - Würth, Christian A1 - Häusler, I. A1 - Hodoroaba, Vasile-Dan A1 - Resch-Genger, Ute T1 - Assessing the influence of microwave-assisted synthesis parameters and stabilizing ligands on the optical properties of AIS/ZnS quantum dots JF - Scientific reports N2 - Luminescent semiconductor quantum dots (QDs) are frequently used in the life and material sciences as reporter for bioimaging studies and as active components in devices such as displays, light-emitting diodes, solar cells, and sensors. Increasing concerns regarding the use of toxic elements like cadmium and lead, and hazardous organic solvents during QD synthesis have meanwhile triggered the search for heavy-metal free QDs using green chemistry syntheses methods. Interesting candidates are ternary AgInS2 (AIS) QDs that exhibit broad photoluminescence (PL) bands, large effective Stokes shifts, high PL quantum yields (PL QYs), and long PL lifetimes, which are particularly beneficial for applications such as bioimaging, white light-emitting diodes, and solar concentrators. In addition, these nanomaterials can be prepared in high quality with a microwave-assisted (MW) synthesis in aqueous solution. The homogeneous heat diffusion and instant temperature rise of the MW synthesis enables a better control of QD nucleation and growth and thus increases the batch-to-batch reproducibility. In this study, we systematically explored the MW synthesis of AIS/ZnS QDs by varying parameters such as the order of reagent addition, precursor concentration, and type of stabilizing thiol ligand, and assessed their influence on the optical properties of the resulting AIS/ZnS QDs. Under optimized synthesis conditions, water-soluble AIS/ZnS QDs with a PL QY of 65% and excellent colloidal and long-term stability could be reproducible prepared. KW - Quantum dots KW - Microwave-assisted synthesis KW - AgInS KW - Aqueous synthesis PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-567091 DO - https://doi.org/10.1038/s41598-022-25498-3 SN - 2045-2322 VL - 12 IS - 1 SP - 1 EP - 11 PB - Nature Publishing Group CY - London AN - OPUS4-56709 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -