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

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.

Electron and Positron Scattering Cross Sections from CO2: A Comparative Study over a Broad Energy Range (0.1–5000 eV)

“…This is mainly for atomic and molecular gases, 100 − 103 but an extension to liquids has recently been examined. 104 In the case of e-CO 2 scattering in the gas phase, there seem to be enough cross section data that such AI/AL approaches might be gainfully applied, complementing the extensive recent work of Guerra, Alves and co-workers on this gas (see the review 105 ), as well as other electron transport simulation procedures as such recently applied by Gracía-Abenza et al 106 to water vapor.…”

“…It would be remiss of us not to mention the recent work on cross section data sets evaluation, using artificial intelligence (AI) and machine learning (ML) processes, that are being explored by the James Cook University group. This is mainly for atomic and molecular gases, − but an extension to liquids has recently been examined . In the case of e-CO 2 scattering in the gas phase, there seem to be enough cross section data that such AI/AL approaches might be gainfully applied, complementing the extensive recent work of Guerra, Alves and co-workers on this gas (see the review), as well as other electron transport simulation procedures as such recently applied by Gracía-Abenza et al to water vapor.…”

An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients

“…The application of ANNs towards determining complete cross-section sets through the inversion of macroscopic experimental data has been the focus of a recent project at James Cook University [3,[19][20][21]32]. While the technique has predominately been used to improve existing cross-section sets [19][20][21], the determination of complete cross-section sets for complex targets remains elusive due to the ill-posed nature of the problem.…”

“…To select each hyperparameter, a simple tuning process was conducted that compared the validation loss between reasonable values for batch size (32,64,128), number of hidden layers (2,3,4,5), hidden layer size (32,64,128,256) and the optimiser used (NAdam, Adam). For simplicity, this processes was conduced with a conventional ANN in which each layer contained an equal number of neurons and the output consisted of an elastic and ionisation cross-section along with three electronic excitation cross-sections.…”

Experimental setup for probing electron-induced chemistry in liquid micro-jets

“…To our knowledge, the only previous setup combining electrons and microjets is that of Tian and co-workers [18,19] which monitors ions created at the interface by a time-of-flight technique. The prospects of extracting collision cross sections from electronmicrojet scattering have been explored by Muccignat et al [20] We are interested in chemical changes which are induced by electron impact in the liquid itself. We have developed a setup that enables recirculating of the liquid samples for multiple irradiations by electrons.…”