Filtern
Erscheinungsjahr
- 2021 (34) (entfernen)
Dokumenttyp
- Zeitschriftenartikel (17)
- Vortrag (10)
- Forschungsdatensatz (3)
- Sonstiges (2)
- Newsletter (1)
- Posterpräsentation (1)
Sprache
- Englisch (34)
Schlagworte
- Nanoparticles (15)
- Electron microscopy (13)
- VAMAS (8)
- Inter-laboratory comparison (6)
- Particle size distribution (6)
- Nanomaterials (4)
- TiO2 (4)
- AFM (3)
- SAXS (3)
- SiO2 (3)
- Traceability (3)
- Automated image analysis (2)
- BAM reference data (2)
- Core-shell particles (2)
- Correlative analysis (2)
- EBSD (2)
- European Centre (2)
- ISO/TC 202 (2)
- ISO/TC 229 (2)
- Image processing (2)
- Image segmentation (2)
- Microplastic (2)
- Nano-safety (2)
- Nanoparticle concentration (2)
- Neural networks (2)
- Reference material (2)
- Reference materials (2)
- Roughness (2)
- Standardisation (2)
- Titania nanoparticles (2)
- ACEnano (1)
- Advanced material (1)
- Advanced materials (1)
- Amperometry (1)
- Analytical services (1)
- Artificial Intelligence (1)
- Artificial intelligence (1)
- Automated Image Analysis (1)
- Benchmarking (1)
- Biofilms (1)
- Bipyramid (1)
- CEN/TC 352 Nanotechnologies (1)
- CUSP (1)
- Catalogue of services (1)
- Catalysis (1)
- Characterisation (1)
- Complex-shape (1)
- Composites (1)
- Concentration (1)
- Correlative imaging (1)
- Cyclic voltammetry (1)
- EC4SafeNano (1)
- EDS (1)
- Electrochemistry (1)
- Electrolysis (1)
- Electron Microscopy (1)
- Electrospun nanocomposite fiber (1)
- Energy dispersive X-ray spectroscopy (1)
- Epoxy nanocomposites (1)
- FIB (1)
- Hybrid metrology measurement (1)
- ISO/TC 201 (1)
- Image Segmentation (1)
- Imaging (1)
- Immunoassay (1)
- Inter-lab comparison (1)
- K-rich Birnessite (K0.45MnO2) (1)
- Machine learning (1)
- MamaLoCA (1)
- MamaLoCa (1)
- Mechanical properties (1)
- Microarray printing (1)
- Microbeam Analysis (1)
- Microbially influenced corrosion (MIC) (1)
- Microstructure (1)
- Modelling (1)
- Nano@BAM (1)
- Nanomechanical charecteisation (1)
- Nanoplastic (1)
- Nanosafety (1)
- Nanotechnology (1)
- Neural Networks (1)
- Ochratoxin A (1)
- Oxygen evolution reaction (OER) (1)
- Particle Characterization (1)
- Particle characterisation (1)
- Particle size and shape distribution (1)
- Porous materials (1)
- Quantum dots (1)
- Quantum yield (1)
- SEM (1)
- STEM-in-SEM (1)
- Sample preparation (1)
- Secondary ion mass spectrometry (1)
- Size (1)
- Size measurements (1)
- Solar concentrator (1)
- Solar energy (1)
- Spectroscopic ellipsometry (1)
- Standardization (1)
- Surface morphology and chemistry (1)
- TKD (1)
- Thermoplastics (1)
- Thermosets (1)
- Titanium dioxide (1)
- ToF-SIMS (1)
- Transmission Kikuchi Diffraction (TKD) (1)
- X-Ray Spectroscopy (1)
- X-ray photoelectron spectroscopy (1)
- XANES (1)
- XPS (1)
- analytical service (1)
- electron microscopy (1)
- nPSize (1)
Organisationseinheit der BAM
- 6 Materialchemie (34)
- 6.1 Oberflächen- und Dünnschichtanalyse (34)
- 1 Analytische Chemie; Referenzmaterialien (8)
- 1.2 Biophotonik (5)
- 6.6 Physik und chemische Analytik der Polymere (4)
- 1.9 Chemische und optische Sensorik (2)
- 1.0 Abteilungsleitung und andere (1)
- 1.4 Prozessanalytik (1)
- 1.8 Umweltanalytik (1)
- 2 Prozess- und Anlagensicherheit (1)
Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (10)
Biofilm formation and microbially influenced corrosion of the iron-reducing microorganism Shewanella putrefaciens were investigated on stainless steel surfaces preconditioned in the absence and presence of flavin molecules by means of XANES (X-ray absorption near-edge structure) analysis and electrochemical methods. The results indicate that biofilm formation was promoted on samples preconditioned in electrolytes containing minute amounts of flavins. On the basis of the XANES results, the corrosion processes are controlled by the iron-rich outer layer of the passive film. Biofilm formation resulted in a cathodic shift of the open circuit potential and a protective effect in terms of pitting corrosion. The samples preconditioned in the absence of flavins have shown delayed pitting and the samples preconditioned in the presence of flavins did not show any pitting in a window of −0.3- to +0.0-V overpotential in the bacterial medium. The results indicate that changes in the passive film chemistry induced by the presence of minute amounts of flavins during a mild anodic polarization can change the susceptibility of stainless steel surfaces to microbially influenced corrosion.
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.
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
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.
Electrochemical methods offer great promise in meeting the demand for user-friendly on-site devices for Monitoring important parameters. The food industry often runs own lab procedures, for example, for mycotoxin analysis, but it is a major goal to simplify analysis, linking analytical methods with smart technologies. Enzyme-linked immunosorbent assays, with photometric detection of 3,3’,5,5’-tetramethylbenzidine (TMB),form a good basis for sensitive detection. To provide a straightforward approach for the miniaturization of the detectionstep, we have studied the pitfalls of the electrochemical TMB detection. By cyclic voltammetry it was found that the TMB electrochemistry is strongly dependent on the pH and the electrode material. A stable electrode response to TMB could be achieved at pH 1 on gold electrodes. We created a smartphonebased, electrochemical, immunomagnetic assay for the detection of ochratoxin A in real samples, providing a solid basis forsensing of further analytes.
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.
Low-cost, high-efficient catalysts for water splitting can be potentially fulfilled by developing earthabundant metal oxides. In this work, surface galvanic formation of Co-OH on K0.45MnO2 (KMO) was achieved via the redox reaction of hydrated Co2+ with crystalline Mn4+. The synthesis method takes place at ambient temperature without using any surfactant agent or organic solvent, providing a clean, green route for the design of highly efficient catalysts. The redox reaction resulted in the formation of ultrathin Co-OH nanoflakes with high electrochemical surface area. X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS) analysis confirmed the changes in the oxidation state of the bulk and
surface species on the Co-OH nanoflakes supported on the KMO. The effect of the anions, such as chloride, nitrate and sulfate, on the preparation of the catalyst was evaluated by electrochemical and spectrochemical means. XPS and Time of flight secondary ion mass spectrometry (ToF-SIMS) analysis demonstrated that the layer of CoOxHy deposited on the KMO and its electronic structure strongly depend on the anion of the precursor used during the synthesis of the catalyst. In particular, it was found that Cl- favors the formation of Co-OH, changing the rate-determining step of the reaction, which enhances the catalytic activity towards the OER, producing the most active OER catalyst in alkaline media.
ACEnano is an EU-funded project which aims at developing, optimising and validating methods for the detection and characterisation of nanomaterials (NMs) in increasingly complex matrices to improve confidence in the results and support their use in regulation. Within this project, several interlaboratory comparisons (ILCs) for the determination of particle size and concentration have been organised to benchmark existing analytical methods. In this paper the results of a number of these ILCs for the characterisation of NMs are presented and discussed. The results of the analyses of pristine well-defined particles such as 60 nm Au NMs in a simple aqueous suspension showed that laboratories are well capable of determining the sizes of these particles. The analysis of particles in complex matrices or formulations such as consumer products resulted in larger variations in particle sizes within technologies and clear differences in capability between techniques. Sunscreen lotion sample analysis by laboratories using spICP-MS and TEM/SEM identified and confirmed the TiO2 particles as being nanoscale and compliant with the EU definition of an NM for regulatory purposes. In a toothpaste sample orthogonal results by PTA, spICP-MS and TEM/SEM agreed and stated the TiO2 particles as not fitting the EU definition of an NM. In general, from the results of these ILCs we conclude that laboratories are well capable of determining particle sizes of NM, even in fairly complex formulations.
An overview is given on the synthesis of TiO2 nanoparticles with well-defined nonspherical shapes (platelet like, bipyramidal, and elongated), with the focus on controlled, reproducible synthesis, as a key requirement for the production of reference materials with homogeneous and stable properties. Particularly with regard to the nanoparticle shapes, there is a high need of certified materials,
solely one material of this type being commercially available since a few months (elongated TiO2). Further, measurement approaches with electron microscopy as the golden method to tackle the nanoparticle shape are developed to determine accurately the size and shape distribution for such nonspherical particles. A prerequisite for accurate and easy (i.e., automated) image analysis is the sample preparation, which ideally must ensure a deposition of the nanoparticles from liquid suspension onto a substrate such that the particles do not overlap, are solvent-free, and have a high deposition density. Challenges in the Synthesis of perfectly monodispersed and solvent-free TiO2 nanoparticles of platelet and acicular shapes are highlighted as well as successful measurement approaches on how to extract from 2D projection electron micrographs the most accurate spatial information, that is, true 3D size, for example, of the bipyramidal nanoparticles with different geometrical orientations on a substrate.