Component analysis and correction of characteristic X-ray yields in the measurement of keV electron-impact atomic inner-shell ionization cross-sections using the thick target method

Inverting cross-sections from thick-target characteristic X-ray yields is an ill-posed inverse problem. Some studies have calculated cross-sections using analytical formulas that ignore the contributions of electron scattering effects, secondary electrons, and bremsstrahlung, without correcting the experimental yields. In this work, simulations were performed for electrons with energies below 30 keV incident on thick Si, Zr, Ag, and W targets at angles of 45° and 90° relative to the target surface, and the sources and relative contributions to the characteristic X-ray yields were analyzed for the Si and Zr K-shells, the Ag L-shell, and the W L αβγ lines. This paper also processed the aforementioned corrected experimental yields for thick targets using the numerical-neural network method to obtain reliable cross-sections. These results were compared with DWBA theoretical values and cross-sections inverted from uncorrected literature yields using the Tikhonov regularization method; the maximum deviation reached 14%, indicating the necessity of yield correction.

Authors

Institutions

Publication Details

Journal
Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms
Published
2026-09-11
DOI
https://doi.org/10.1016/j.nimb.2026.166306
Primary Topic
X-ray Spectroscopy and Fluorescence Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Component analysis and correction of characteristic X-ray yields in the measurement of keV electron-impact atomic inner-shell ionization cross-sections using the thick target method

Jiaolong Wu, Y. Guo, Kun He, Ying Wu et al.
Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms
X-ray Spectroscopy and Fluorescence Analysis
article

Component analysis and correction of characteristic X-ray yields in the measurement of keV electron-impact atomic inner-shell ionization cross-sections using the thick target method

Jiaolong Wu, Y. Guo, Kun He, Ying Wu, Zhijie Xin
article en

Abstract

Inverting cross-sections from thick-target characteristic X-ray yields is an ill-posed inverse problem. Some studies have calculated cross-sections using analytical formulas that ignore the contributions of electron scattering effects, secondary electrons, and bremsstrahlung, without correcting the experimental yields. In this work, simulations were performed for electrons with energies below 30 keV incident on thick Si, Zr, Ag, and W targets at angles of 45° and 90° relative to the target surface, and the sources and relative contributions to the characteristic X-ray yields were analyzed for the Si and Zr K-shells, the Ag L-shell, and the W L αβγ lines. This paper also processed the aforementioned corrected experimental yields for thick targets using the numerical-neural network method to obtain reliable cross-sections. These results were compared with DWBA theoretical values and cross-sections inverted from uncorrected literature yields using the Tikhonov regularization method; the maximum deviation reached 14%, indicating the necessity of yield correction.

Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and AtomsVol. 581
North China Electric Power University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
X-ray Spectroscopy and Fluorescence Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.