Abstract
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.
Highlights
Light element identification is necessary in materials research to obtain detailed insight into various material properties
We investigated the correlation between the element distribution around the C r2N precipitate and the aging time of High-nitrogen stainless steel (HNS) using scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) spectrum images (SIs) with improved signal-to-noise ratio (SNR) via machine learning (ML) algorithms
In the specimen aged for 1 03 s (Fig. 1a), a cellular type of C r2N began to form within the grains, and the volume fraction of cellular C r2N increased with the aging time (Fig. 1b,c)
Summary
Light element identification is necessary in materials research to obtain detailed insight into various material properties. Analytical characterization techniques, strengthened by both a robust detection limit and nanometer spatial resolution, are required for researching and manufacturing materials with enhanced properties Analytical techniques such as scanning transmission electron microscopy (STEM)-electron energy loss spectroscopy (EELS) and 3D atom-probe tomography (3D-APT) have been widely used to characterize the chemical composition or a phase structure of materials due to their excellent detection limits (0.005–0.1 at%8–10 and 0.001 at%11,12, respectively) and spatial resolutions (0.1 nm[13,14] and 0.2–0.4 nm[15,16,17,18], respectively). It is difficult to detect a light element when alloyed with heavy element(s), because its characteristic X-ray energies are likely to be overlapped by the low-energy L and M peaks of the heavy e lement[26]
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