Simulating the Feasibility of Using Liquid Micro-Jets for Determining Electron–Liquid Scattering Cross-Sections

Muccignat, Dale L., Stokes, Peter W., Cocks, Daniel G., Gascooke, Jason R., Jones, Darryl B., Brunger, Michael J., and White, Ronald D. (2022) Simulating the Feasibility of Using Liquid Micro-Jets for Determining Electron–Liquid Scattering Cross-Sections. International Journal of Molecular Sciences, 23 (6). 3354.

[img]
Preview
PDF (Published Version) - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview
View at Publisher Website: https://doi.org/10.3390/ijms23063354
 
4
587


Abstract

The extraction of electron–liquid phase cross-sections (surface and bulk) is proposed through the measurement of (differential) energy loss spectra for electrons scattered from a liquid micro-jet. The signature physical elements of the scattering processes on the energy loss spectra are highlighted using a Monte Carlo simulation technique, originally developed for simulating electron transport in liquids. Machine learning techniques are applied to the simulated electron energy loss spectra, to invert the data and extract the cross-sections. The extraction of the elastic cross-section for neon was determined within 9% accuracy over the energy range 1–100 eV. The extension toward the simultaneous determination of elastic and ionisation cross-sections resulted in a decrease in accuracy, now to within 18% accuracy for elastic scattering and 1% for ionisation. Additional methods are explored to enhance the accuracy of the simultaneous extraction of liquid phase cross-sections.

Item ID: 74589
Item Type: Article (Research - C1)
ISSN: 1422-0067
Keywords: Cross-section, Electron, Liquid microjet, Machine learning, Monte Carlo
Copyright Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited: 23 Nov 2022 01:51
FoR Codes: 51 PHYSICAL SCIENCES > 5106 Nuclear and plasma physics > 510699 Nuclear and plasma physics not elsewhere classified @ 100%
Downloads: Total: 587
Last 12 Months: 13
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page