Multiphase identification in Ni–PbTe contacts by EBSD and aberration-corrected STEM

Ferreres, Xavier Reales; Casillas, Gilberto; Yamini, Sima Aminorroaya; Gazder, Azdiar A.

doi:10.1016/j.matdes.2019.108252

Cited by 4 publications

(2 citation statements)

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“…Cu and Ni are usually used as electrodes due to their high electrical conductivity and good wettability with solders . However, the solubility of both Cu and Ni in PbTe is high, , much higher than the amounts added in PbTe in this work. This explains that no Cu or Ni particles were observed in the hot-pressed PbTe pellets here.…”

Section: Resultsmentioning

confidence: 99%

Screening Metal Electrodes for Thermoelectric PbTe

Liu

Zhang

et al. 2023

ACS Appl. Mater. Interfaces

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Historically, both p- and n-type PbTe show extraordinary thermoelectric figures of merit within 300–600 °C for power generation applications. A full realization of the potential of these high-performance thermoelectric materials on a device level largely depends on the electrical and thermal contacts with the metal electrodes. Chemical inertness with a slow diffusion could be an important criterion for the selection of metal electrodes. In this work, the diffusion of the total 12 potential metal electrodes in PbTe diffusion couples are focused on and sorted, suggesting the superiority of Co as an electrode for its low diffusion coefficient and interfacial contact resistivity, inertial to PbTe and compatibility in temperature for sintering. The strategy used in this work is believed to be applicable to the selection of electrodes for other thermoelectric materials.

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Section: Resultsmentioning

confidence: 99%

Screening Metal Electrodes for Thermoelectric PbTe

Liu

Zhang

et al. 2023

ACS Appl. Mater. Interfaces

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“…Additionally, electron diffraction has been extensively used as a complementary technique to elucidate crystallographic information from materials under various modalities such as selected area electron diffraction, microdiffraction, nanobeam diffraction (NBD), or convergent beam electron diffraction (CBED) and electron backscattering diffraction (EBSD). [ 2 ] Besides, coupled with STEM, electron diffraction can also be collected while the beam continuously scans the sample using precession electron diffraction (PED) coupled with one of the abovementioned detectors can lead to get 4D‐STEM data which have been recently developed to elucidate structure at smaller scales, but further development needs to be done in the collection and analysis of big data. [ 3 ] EBSD provides the analysis of crystalline phases, strain, and crystal orientation mapping at larger scale (microns), while PED‐based method produces similar maps but reaching nanometric resolution, both methodologies are limited by the probe size, one can classify the ranges of the analyzed area in squared microns and nanometers, respectively.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning for the Segmentation of Small Crystalline Particles at the Atomic Level

Bárcena-González

Hernandez-Robles

Mayoral

et al. 2023

Cryst. Res. Technol.

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Electron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D-STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images that could prevent the need of large volumes of data to train deep learning models. The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filters are convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded with aberration-corrected microscopy, offering new possibilities of analysis at nanometric scale.

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