For Fe-based 18Cr10Mn0.4N0.5C(0–2.17)Mo (in wt %) austenitic stainless steels, effects of Mo on pitting corrosion resistance and the improvement mechanism were investigated. Alloying Mo increased pitting and repassivation potentials and enhanced the passive film resistance by decreasing number of point defects in the film. In addition, Mo reduced critical dissolution rate of the alloys in acidified chloride solutions, and the alloy with higher Mo content could remain in the passive state in stronger acid. Thus, it was concluded that the alloying Mo enhanced pitting corrosion resistance of the alloys through increasing protectiveness of passive film and lowering pit growth rate.

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.