Unexplored Possibilities of Nitrogen Alloying of Steel

Foct, J.

doi:10.1007/978-3-642-17665-4_37

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…In a multi-component material, light elements determine the physical, chemical, mechanical, and electrical properties of the material; hence, alloying with light elements can be exploited for many applications. For example, the microstructure and phase stability in ferrous alloys are strongly dependent on the addition of a small amount of C and/or N (~ 1 wt%), which in turn dramatically changes their mechanical properties and corrosion resistance 1 – 5 . In addition, the distribution/concentration of light elements at the nanometer scale substantially affects the phase formation, which determines the performance of the material 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

“…This alloy has been used in a wide range of applications, from medical materials to the ship and oil industries. It is an interesting material owing to its high fatigue strength, tolerance for fracture at low and high temperatures, cold working hardening, and high wear resistance, among other features 1 – 3 . These characteristics originate from the high N content (> 0.4 wt%), which acts as a strong austenite stabilizer and an interstitial solid solution strengthener 2 – 7 , 41 , 42 , and also improves the corrosion resistance of stainless steel when in solid solution 42 .…”

Section: Introductionmentioning

confidence: 99%

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Nanoscale light element identification using machine learning aided STEM-EDS

Kim

Bae

et al. 2020

Sci Rep

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Light element identification is necessary in materials research to obtain detailed insight into various material properties. However, reported techniques, such as scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) have inadequate detection limits, which impairs identification. In this study, we achieved light element identification with nanoscale spatial resolution in a multi-component metal alloy through unsupervised machine learning algorithms of singular value decomposition (SVD) and independent component analysis (ICA). Improvement of the signal-to-noise ratio (SNR) in the STEM-EDS spectrum images was achieved by combining SVD and ICA, leading to the identification of a nanoscale N-depleted region that was not observed in as-measured STEM-EDS. Additionally, the formation of the nanoscale N-depleted region was validated using STEM–electron energy loss spectroscopy and multicomponent diffusional transformation simulation. The enhancement of SNR in STEM-EDS spectrum images by machine learning algorithms can provide an efficient, economical chemical analysis method to identify light elements at the nanoscale.

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