6 Materialchemie
Filtern
Erscheinungsjahr
- 2020 (134) (entfernen)
Dokumenttyp
Referierte Publikation
- nein (134) (entfernen)
Schlagworte
- Nanoparticles (27)
- Electron microscopy (12)
- Particle size distribution (10)
- BAMline (9)
- Small angle scattering (8)
- Synchrotron (8)
- Applications (7)
- Geant4 (6)
- Laser-induced periodic surface structures (LIPSS) (6)
- MCS (6)
Organisationseinheit der BAM
- 6 Materialchemie (134)
- 6.1 Oberflächen- und Dünnschichtanalyse (40)
- 6.3 Strukturanalytik (27)
- 6.6 Physik und chemische Analytik der Polymere (27)
- 6.2 Material- und Oberflächentechnologien (20)
- 6.5 Synthese und Streuverfahren nanostrukturierter Materialien (17)
- 1 Analytische Chemie; Referenzmaterialien (8)
- 6.7 Materialsynthese und Design (7)
- 5 Werkstofftechnik (6)
- 9 Komponentensicherheit (6)
Eingeladener Vortrag
- nein (73)
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.
Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet.
This dataset is a complete set of raw, processed and analyzed data, complete with Jupiter notebooks, associated with the manuscript mentioned in the title.
In the manuscript, we provide a "systems architecture"-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (Methodology Optimization for Ultrafine Structure Exploration). Through this project, we aim to provide a comprehensive methodology for obtaining the highest quality X-ray scattering information (at small and wide angles) from measurements on materials science samples.
The Dark Side of Science
(2020)
Development and characterization of starch film and the incorporation of silver nanoparticles
(2020)
Starch is one of the biopolymers being used for bioplastic synthesis. For production, starch can be combined with different plasticizers, starches from different plant sources and even with nanomaterials to improve or to add film properties. The challenge of adding these, e.g. in the form of silver nanoparticles (AgNp) is to determine the concentration so as to avoid impairing the properties of the film, agglomeration or altering the visual characteristics of the film. In this study, a starch film synthesis route and the incorporation of silver nanoparticles has been proposed in order not to alter the properties of the film while maintaining the transparency and a clear colour of the starch film. The results showed that the proposed synthesis route is promising, efficient, reproducible, fast and the film has good mechanical properties.
The image designed by Natalia Cano Murillo and colleagues shows the cross section of a ternary composite (boehmite/polycarbonate/epoxy, 80μm x 80μm). The surface was measured by AFM kelvin probe microscopy, yielding the surface potential which is shown as 3D‐surface and contour lines. The sample was further subjected to AFM force spectroscopy with a lateral resolution of 1μm², yielding the local Young's modulus, projected in false colors on the 3D surface. The ternary system, containing boehmite nanoparticles, shows a broad distribution of modulus, desirable for optimized macroscopic mechanical properties, such as high stiffness as well as toughness.
Im Vortrag wird ein Messfiltertiegel als Zusatztool für die TED-GC/MS vorgestellt, welcher für die Probenaufbereitung von kleinen Partikelfrachten oder kleinen Partikelgrößen genutzt werden kann. Er ist Filtereinheit und Messtiegel in einem, wodurch Verluste bei der Überführung der Probe in das Messgefäß sowie Kontaminationen vermindert werden. Das beispiel ist hier die Filtration von Flaschenwasser mit anschließender Detektion mit der TED-GC/MS.
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.
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.