Suppr超能文献

利用自种子X射线自由电子激光脉冲的高能量分辨率非共振光谱学。

High-energy-resolution off-resonant spectroscopy with self-seeded x-ray free-electron laser pulses.

作者信息

Sohn Jang Hyeob, Kang Gyeongbo, Choi Tae-Kyu, Lee Gyusang, Lee Changhoo, Chun Sae Hwan, Park Jaeku, Shin Dongbin, Cho Byoung-Ick

机构信息

Department of Physics and Photon Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, South Korea.

XFEL Division, Pohang Accelerator Laboratory, POSTECH, Pohang, Gyeongbuk 37673, South Korea.

出版信息

Struct Dyn. 2024 Mar 26;11(2):024304. doi: 10.1063/4.0000243. eCollection 2024 Mar.

Abstract

This paper presents the implementation of high-energy-resolution off-resonant spectroscopy (HEROS) measurements using self-seeded x-ray free-electron laser (XFEL) pulses. This study systematically investigated XFEL conditions, including photon energy and accumulated shot numbers, to optimize the measurement efficiency for copper foil samples near the -edge. The x-ray absorption spectra reconstructed using HEROS were compared with those derived from fluorescence-yield measurements. The HEROS-based spectra exhibited consistent line shapes independent of the sample thickness. The potential application of HEROS to high-temperature copper was also explored. HEROS offers distinct advantages including scan-free measurement of x-ray absorption spectra with reduced core-hole lifetime broadening and self-absorption effects. Using self-seeded XFEL pulses, HEROS facilitates single-shot-based pump-probe measurements to investigate the ultrafast dynamics in various materials and diverse conditions.

摘要

本文介绍了利用自种子X射线自由电子激光(XFEL)脉冲进行高能量分辨率非共振光谱(HEROS)测量的实现过程。本研究系统地研究了XFEL条件,包括光子能量和累积脉冲数,以优化对铜箔样品在-边缘附近的测量效率。将利用HEROS重建的X射线吸收光谱与通过荧光产率测量得到的光谱进行了比较。基于HEROS的光谱呈现出与样品厚度无关的一致线形。还探索了HEROS在高温铜方面的潜在应用。HEROS具有明显优势,包括无扫描测量X射线吸收光谱,减少了芯孔寿命展宽和自吸收效应。利用自种子XFEL脉冲,HEROS有助于基于单次脉冲的泵浦-探测测量,以研究各种材料在不同条件下的超快动力学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cfd/10972604/7dd08ef69916/SDTYAE-000011-024304_1-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验