2019
DOI: 10.1002/adem.201900823
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Correlative Nano‐Computed Tomography and Focused Ion‐Beam Sectioning: A Case Study on a Co‐Base Superalloy Oxide Scale

Abstract: Herein, the capabilities of a correlative tomography approach combining laboratory nano X‐ray computed tomography, focused ion‐beam sectioning, and energy‐dispersive X‐ray spectroscopy are utilized for the characterization of multilayered oxide scales of Co‐base superalloys regarding their 3D morphology and chemical composition. The combination of complementary 3D imaging techniques allows for a precise and reliable segmentation of the pore space, oxide precipitates, and different phases of the initial materia… Show more

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Cited by 4 publications
(2 citation statements)
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References 58 publications
(128 reference statements)
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“…Several approaches for scale-bridging 3D analysis have been demonstrated. For instance, a semidestructive workflow combining nano-CT with FIB-SEM tomography enabled a superior segmentation of 3D datasets and thorough analysis of samples of several microns in size [39,40]. As a nondestructive approach, the combination of 360 • -ET and nano-CT proved to reliably reveal the structure of complex material systems [41,42], e.g., macroporous zeolite particle agglomerates, with nm resolution, while, at the same time, covering decently sized volumes up to (40 µm) 3 , enabling the acquisition of statistically relevant information [43].…”
Section: Introductionmentioning
confidence: 99%
“…Several approaches for scale-bridging 3D analysis have been demonstrated. For instance, a semidestructive workflow combining nano-CT with FIB-SEM tomography enabled a superior segmentation of 3D datasets and thorough analysis of samples of several microns in size [39,40]. As a nondestructive approach, the combination of 360 • -ET and nano-CT proved to reliably reveal the structure of complex material systems [41,42], e.g., macroporous zeolite particle agglomerates, with nm resolution, while, at the same time, covering decently sized volumes up to (40 µm) 3 , enabling the acquisition of statistically relevant information [43].…”
Section: Introductionmentioning
confidence: 99%
“…Each expected mineral type can be unswervingly assigned to the particles by applying this approach. All particles from a respective mineral type are segmented in Arivis Vision4D by applying a machine-learning algorithm [3] and classified regarding their specific morphology, frequency and distribution, starting with the BSE/EDXS-informed Nano-CT virtual slices, according to Lenz, Wirth et al [4]. The segmentation routine transfers the chemical information to the entire Nano-CT volume, providing each mineral's morphology and distribution.…”
mentioning
confidence: 99%