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

Area of Science:

• Atomic and Molecular Physics
• Condensed Matter Physics
• Materials Science

Background:

• Electron scattering experiments are crucial for understanding material properties.
• Extracting liquid-phase cross-sections is challenging due to sample handling and stability.
• Existing methods often lack the precision needed for detailed electron-matter interaction studies in condensed phases.

Purpose of the Study:

• To develop a novel method for determining electron-liquid phase cross-sections (surface and bulk).
• To apply machine learning techniques for inverting electron energy loss spectra and extracting cross-section data.
• To validate the method's accuracy using neon as a test case.

Main Methods:

• Measurement of differential energy loss spectra for electrons scattered from a liquid micro-jet.
• Utilizing a Monte Carlo simulation for electron transport in liquids to analyze scattering signatures.
• Applying machine learning algorithms to simulated energy loss spectra for data inversion and cross-section extraction.

Main Results:

• Successfully extracted the elastic cross-section for neon with 9% accuracy (1-100 eV).
• Simultaneous extraction of elastic and ionization cross-sections achieved 18% accuracy for elastic and 1% for ionization.
• Identified areas for methodological improvement to enhance simultaneous cross-section determination accuracy.

Conclusions:

• The proposed method demonstrates a viable approach for measuring electron-liquid cross-sections.
• Machine learning significantly aids in the complex data analysis required for cross-section extraction.
• Further research is needed to optimize simultaneous extraction accuracy for multiple cross-section types.