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用于从XPS数据中提取跨各种构型液体界面的化学分辨深度剖面的反演模型:PROPHESY。

Inversion model for extracting chemically resolved depth profiles across liquid interfaces of various configurations from XPS data: PROPHESY.

作者信息

Ozon Matthew, Tumashevich Konstantin, Lin Jack J, Prisle Nønne L

机构信息

Center for Atmospheric Research, University of Oulu, PO Box 4500, Finland.

出版信息

J Synchrotron Radiat. 2023 Sep 1;30(Pt 5):941-961. doi: 10.1107/S1600577523006124. Epub 2023 Aug 23.

Abstract

PROPHESY, a technique for the reconstruction of surface-depth profiles from X-ray photoelectron spectroscopy data, is introduced. The inversion methodology is based on a Bayesian framework and primal-dual convex optimization. The acquisition model is developed for several geometries representing different sample types: plane (bulk sample), cylinder (liquid microjet) and sphere (droplet). The methodology is tested and characterized with respect to simulated data as a proof of concept. Possible limitations of the method due to uncertainty in the attenuation length of the photo-emitted electron are illustrated.

摘要

介绍了一种用于从X射线光电子能谱数据重建表面深度轮廓的技术PROPHESY。反演方法基于贝叶斯框架和原始对偶凸优化。针对代表不同样品类型的几种几何形状开发了采集模型:平面(块状样品)、圆柱体(液体微射流)和球体(液滴)。作为概念验证,该方法针对模拟数据进行了测试和表征。阐述了由于光发射电子衰减长度的不确定性导致该方法可能存在的局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b8b/10481271/0b3a2ee26902/s-30-00941-fig1.jpg

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