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Keywords = Pseudo-Voigt fit function

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12 pages, 1599 KB  
Article
Predicting the Coordination Number of Transition Metal Elements from XANES Spectra Using Deep Learning
by Jianan Gao, Ruixuan Chen, Wei Sun and Xiaonan Wang
Inorganics 2025, 13(12), 411; https://doi.org/10.3390/inorganics13120411 - 16 Dec 2025
Viewed by 824
Abstract
X-ray absorption near-edge structure (XANES) spectra are employed to characterise the coordination numbers of metallic elements within materials. However, conventional XANES analysis methods frequently rely on preconceived assumptions regarding the analysed samples, which may not fully satisfy the requirements of scientific research and [...] Read more.
X-ray absorption near-edge structure (XANES) spectra are employed to characterise the coordination numbers of metallic elements within materials. However, conventional XANES analysis methods frequently rely on preconceived assumptions regarding the analysed samples, which may not fully satisfy the requirements of scientific research and industrial applications. To mitigate such reliance, a novel approach based on the Gated Adaptive Network for Deep Automated Learning of Features (GANDALF) is proposed. To effectively extract multi-scale information from the XANES spectrum, the spectrum was segmented into multiple scales. Each segment was fitted using a pseudo-Voigt function, with the absorption edge position. The GANDALF algorithm, a table-based deep learning approach, was employed to model the coordination environment of absorbing elements. The proposed method was validated using a previously published open-access dataset. For vanadium-containing samples, the model achieved R2 values of 0.7837 on test sets with non-integer coordination numbers, whereas the random forest model only achieved 0.6328. Furthermore, our results highlight the significant importance of the post-edge peak when predicting coordination numbers using the full spectrum. Full article
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13 pages, 3467 KB  
Article
Diffraction Enhanced Imaging Analysis with Pseudo-Voigt Fit Function
by Deepak Mani, Andreas Kupsch, Bernd R. Müller and Giovanni Bruno
J. Imaging 2022, 8(8), 206; https://doi.org/10.3390/jimaging8080206 - 23 Jul 2022
Cited by 12 | Viewed by 4886
Abstract
Diffraction enhanced imaging (DEI) is an advanced digital radiographic imaging technique employing the refraction of X-rays to contrast internal interfaces. This study aims to qualitatively and quantitatively evaluate images acquired using this technique and to assess how different fitting functions to the typical [...] Read more.
Diffraction enhanced imaging (DEI) is an advanced digital radiographic imaging technique employing the refraction of X-rays to contrast internal interfaces. This study aims to qualitatively and quantitatively evaluate images acquired using this technique and to assess how different fitting functions to the typical rocking curves (RCs) influence the quality of the images. RCs are obtained for every image pixel. This allows the separate determination of the absorption and the refraction properties of the material in a position-sensitive manner. Comparison of various types of fitting functions reveals that the Pseudo-Voigt (PsdV) function is best suited to fit typical RCs. A robust algorithm was developed in the Python programming language, which reliably extracts the physically meaningful information from each pixel of the image. We demonstrate the potential of the algorithm with two specimens: a silicone gel specimen that has well-defined interfaces, and an additively manufactured polycarbonate specimen. Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
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14 pages, 3202 KB  
Article
Optimal Data Processing Method for the Application of Eu3+ Photoluminescence Piezospectroscopy in Thermal Barrier Coatings
by Yanheng Zhang, Ning Lu and Wei Qiu
Coatings 2021, 11(6), 678; https://doi.org/10.3390/coatings11060678 - 4 Jun 2021
Cited by 9 | Viewed by 3180
Abstract
Thermal barrier coatings (TBCs) are widely used to protect gas turbine blades but internal stress near the interface in TBCs is one of the main causes of thermal barrier failure under thermal cycling. A non-destructive inspection technique based on Eu3+ photoluminescence piezospectroscopy [...] Read more.
Thermal barrier coatings (TBCs) are widely used to protect gas turbine blades but internal stress near the interface in TBCs is one of the main causes of thermal barrier failure under thermal cycling. A non-destructive inspection technique based on Eu3+ photoluminescence piezospectroscopy has been successfully used to analyze the residual stress in TBCs, but systematic and quantitative evaluation of data processing is still needed, especially with respect to the identification of peak positions. In this work, processing methods for Eu3+ photoluminescence spectroscopy data were studied to characterize TBC internal stress. Both physical and numerical experiments were carried out where Eu3+ luminescence spectra were obtained from a sample of europium-doped yttria-stabilized zirconia (YSZ:Eu3+) under step-by-step uniaxial loading, and the simulated spectra were numerically deduced from the measured spectra. The peak shifts were then obtained by processing the spectral data in different ways (Gaussian, Lorentzian, pseudo-Voigt fitting, and the barycenter method), and comparing the results. We found that the Gaussian function, rather than the commonly used Lorentzian function, is the most appropriate method for the application of Eu3+ photoluminescence piezospectroscopy in TBCs because it provides sufficient sensitivity, stability and confidence for quantitative stress analysis. Full article
(This article belongs to the Special Issue Defects, Stresses and Cracks in Thermal Barrier Coatings)
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