Spalling resistance of thermally grown oxide based on NiCoCrAlY(Ti) with different oxide peg sizes

Liu, Yang; Choi, Ryutan; Zheng, Yuan; Bidabadi, Mohammad Hassan Shirani; Rehman, Abdul; Zhang, Chi; Chen, Hao; Yang, Zhigang

doi:10.1007/s12598-019-01339-7

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…The chemical composition (wt%) of this alloy characterized by nanoscopic SIMS is Ni 0.32, Cr 0.20, Al 0.08, and Y 0.4, balanced with Co, which is designed as a corrosion- and wear-resistant alloy employed in turbine blades. 65 The mass spectrum shown in Figure 12 was constructed by TOF-SIMS Explorer 1.3.1.0 software from the total ions information of the scanned region. In this spectrum, ToF-ML identifies Al + , Cr + , Co+, Ni+, Ca + , Ti + .…”

Section: Discussionmentioning

confidence: 99%

“…Here we omit the tabular peak analysis and directly insert ML-ToF-assigned-labels as the expert-assigned labels are available for only a few peaks. 65 The mass spectrum shown in Figure 12 was constructed by TOF-SIMS Explorer 1.3.1.0 software from the total ions information of the scanned region. In this spectrum, ToF-ML identifies Al + , Cr + , Co+, Ni+, Ca + , Ti + .…”

Section: Secondary Ion Mass Spectrometrymentioning

confidence: 99%

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Machine-learning-enhanced time-of-flight mass spectrometry analysis

Varanasi

Schwarz

et al. 2021

Patterns

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Summary Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Secondary Ion Mass Spectrometrymentioning

confidence: 99%

Machine-learning-enhanced time-of-flight mass spectrometry analysis

Varanasi

Schwarz

et al. 2021

Patterns

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show abstract

“…The chemical composition (wt.%) of this alloy characterized by Nanoscopic Secondary Ion Mass Spectrometry (Nano-SIMS) is Ni-0.32, Cr-0.20, Al-0.08, Y-0.4, balanced with Co, which is designed as a corrosion and wear resistant alloy employed in turbine blades [64]. The mass spectrum shown in Figure 12 was constructed by TOF-SIMS Explorer 1.3.1.0 software from the total ions information of the scanned region.…”

Section: Corrosion and Wear Co-based Alloymentioning

confidence: 99%

Machine-learning-enhanced time-of-flight mass spectrometry analysis

Varanasi

Schwarz

et al. 2020

Preprint

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Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based from patterns formed in their mass-to-charge ratios (m/z) and relative isotopic abundances. However, this identification step still mainly relies on individual user's expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different timeof-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community a open-source, intelligent mass spectra analysis.

show abstract