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Damage induced by electric field (DIEF) that happens in the transmission electron microscope (TEM) when converging the electron beam (e-beam) on microparticles (MPs) can be used to synthesis new nanomaterial and nanomaterial compositions. The research questions are to clarify the limits and possibilities of the method regarding materials that can be produced, systems to which it is applicable and working beam parameters. Synthesis of nano-objects from microparticles using DIEF in TEM could be shown for different materials. Additionally, DIEF using the e-beam in a scanning electron microscope (SEM) can also be used to synthesis nano-objects. A deeper material analysis of this nano-objects was done using TEM and shows that the material of the nanoparticles (NPs) can be gold or/and silicon. Furthermore, the size of the NPs depends on the distance to the center of DIEF whereby the larger NPs are closer to the center. The areas of gold NPs are promising candidates for plasmonic or photonic devices for energy storage or transport.
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.
A range of heat treatments have been developed for wrought Inconel 718 to obtain desired properties. For additively manufactured Inconel 718, the recently developed standard ASTM F3301 provides guidance for heat treatment of powder bed fusion specimens. Although this standard is based on standards developed for wrought Inconel 718, it does not include direct aging. Since direct aging reduces the number of processing steps, it can result in a post processing cost reduction if the desired properties are obtained. In this study, we characterized the microstructure and tensile behavior of Inconel 718 specimens produced by a laser powder bed fusion process. The specimens were heat treated according to two different routines after stress relieving: a full heat Treatment versus a one-step direct aging process. Differences in the resulting texture and grain morphology were observed.
The ex-situ stress-strain behavior was broadly similar. However, a slight increase in yield strength was observed for the direct aged specimen. In order to understand this behavior, investigations with in-situ synchrotron Energy dispersive X-ray diffraction tensile testing revealed differences in the load partitioning among different Crystal directions. Importantly, the elastic anisotropy expressed by the magnitude of the diffraction elastic constants showed a dependency on the microstructures.
Since additive manufacturing processes typically introduce heterogeneous microstructures and residual stresses, the applicability of parts produced in an as-built state is limited. Therefore, often different post-processing treatments are necessary to obtain the desired stress state and properties.
For additively manufactured Inconel 718, the recently developed standard ASTM F3301 provides guidance for the heat treatment of powder bed fusion specimens. Although this standard is based on standards developed for wrought Inconel 718, it does not include the direct aging variant. In this study, we characterized the microstructure and tensile behavior of Inconel 718 specimens produced by a laser powder bed fusion process. The specimens were heat-treated according to two different routines after stress relieving: a full heat treatment cycle versus a one-step aging process. Differences in the resulting texture and grain morphology were observed. Although these differences prevail, the ex-situ tensile behavior was broadly similar. Minor differences were observed in yield strength and work hardening rate for the direct aged specimen. In order to understand this behavior, investigations with in-situ tensile testing during synchrotron energy-dispersive X-ray diffraction measurements revealed differences in the load partitioning among different crystal directions. This was attributed to microstructural differences between the heat treatment variants. Further analysis emphasized that the various strengthening mechanisms are present to a different extent depending on the heat-treatment cycle applied. In addition, the elastic anisotropy expressed by the differences in the diffraction elastic constants displayed a dependence on the microstructure. Importantly, a precise knowledge of such constants is indispensable to reliably determine residual stresses in parts.
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
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.
A study was made on the effect of creep loading on the precipitate radii evolution of the aluminum alloy 2618A.
The overageing process of the alloy was investigated under load at a temperature of 190 °C with stresses between 79 and 181 MPa and compared to stress free isothermal ageing. The precipitates responsible for strength were characterized using dark-field transmission electron microscopy (DFTEM). This allows the experimental Determination of radii distributions of the rod-shaped Al2CuMg precipitates and the evaluation regarding their mean precipitate radius. It was found that the mean precipitate radius enables the comparison of the different microstructural conditions of crept and uncrept samples. The mean precipitate radii of the samples experiencing creep are significantly higher than those of undeformed samples. It was shown that the acquired radii distributions are viable to determine averaged particle radii for comparison of the aged samples. A ripening process including pipe diffusion along dislocations describes the data on coarsening very well for the creep samples.
Transmission electron microscopy study of rapid thermally annealed Pd/Ge contacts on In0.53Ga0.47As
(1996)
Phase formation in rapid thermally annealed Pd/Ge contacts on In0.53Ga0.47As has been investigated by means of cross-sectional transmission electron microscopy, convergent-beam electron diffraction, and energy-dispersive x-ray analysis. Solid-phase regrowth is observed to occur similarly as in Pd/Ge contacts on GaAs or InP. The reaction starts at low temperatures with the formation of an amorphous Pd–In–Ga–As layer, which crystallizes at elevated temperatures yielding hexagonal Pd4In0.53Ga0.47As being first described in this work. At temperatures .250 °C, this phase decomposes due to epitaxial solid-phase regrowth of In0.53Ga0.47As and formation of Pd–Ge phases. The stable composition is reached at temperatures .350 °C with excess Ge diffused through top Pd–Ge to the contact interface and growing epitaxially on the semiconductor.