Abstract
Chemical imaging, such as mass imaging, provides a distribution image of a particular matter and is crucial for analyzing the chemical and physical mechanisms of a sample. However, methods that provide molecular or elemental distribution do not always have sufficiently high spatial resolution to evaluate the nanosized structures in a sample. To address this issue, a multimodal data analysis method was developed by integrating the obtained low spatial resolution chemical images with complementary methods. In this study, the hydrogen distribution of a steel sample was measured using electron stimulated desorption (ESD) and scanning electron microscopy (SEM). ESD provided the time-course images of hydrogen distribution in the steel sample, whereas SEM provided the outline of the steel sample structure. The multimodal images of the same sample were fused, and then all the data were analyzed together to extract detailed physical and chemical information that cannot be observed by only one of the methods. The alignment of the images obtained using different methods was evaluated based on the minimization of each pixel subtraction. Three different data analysis methods, principal component analysis, least absolute shrinkage and selection operator, and autoencoder, are applied to the image fusion dataset of the ESD image and SEM images to help elucidate the hydrogen permeation behavior through the steel structure.
Highlights
Multiple analysis methods such as molecular or elemental mapping and crystal structure analysis are often required to characterize samples that have complex structures and features
The resolution of both images was adjusted to 250 × 250 pixels, and the final size of the scanning electron microscopy (SEM) image fused with the electron stimulated desorption (ESD) images was 316 μm × 480 μm
In the principal component analysis (PCA) results of ESD time-course hydrogen image data, the contribution ratios for principal components (PCs) 1, 2, and 3 are 61.02%, 7.79%, and 4.63%, respectively, which indicates that PC1 has the most information on the ESD data
Summary
Multiple analysis methods such as molecular or elemental mapping and crystal structure analysis are often required to characterize samples that have complex structures and features. The fusion data were analyzed via principal component analysis (PCA) to obtain PCA score images with a higher spatial resolution and PCA loadings with detailed spectrum information. In this study, modified image data fusion procedures were developed, and the image fusion dataset was analyzed using PCA, the least absolute shrinkage and selection operator (LASSO), and autoencoder. ESD provides a two-dimensional distribution of hydrogen adsorbed on the steel surface by scanning an electron beam.[8] the spatial resolution of ESD is insufficient for observing crystal structures in steel samples. An image data fusion method for the multimodal data of a hydrogen flowing steel sample obtained via ESD and SEM images was developed to obtain hydrogen specific distributions with sufficiently high spatial resolution for identifying the differences between crystal structures
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