Assessment of the self-consistency of electron-THF cross sections using electron swarm techniques: Mixtures of THF–Ar and THF–N2

Toward a complete and comprehensive cross section database for electron scattering from NO using machine learning

We review experimental and theoretical cross sections for electron scattering in nitric oxide (NO) and form a comprehensive set of plausible cross sections. To assess the accuracy and self-consistency of our set, we also review electron swarm transport coefficients in pure NO and admixtures of NO in Ar, for which we perform a multi-term Boltzmann equation analysis. We address observed discrepancies with these experimental measurements by training an artificial neural network to solve the inverse problem of unfolding the underlying electron-NO cross sections while using our initial cross section set as a base for this refinement. In this way, we refine a suitable quasielastic momentum transfer cross section, a dissociative electron attachment cross section, and a neutral dissociation cross section. We confirm that the resulting refined cross section set has an improved agreement with the experimental swarm data over that achieved with our initial set. We also use our refined database to calculate electron transport coefficients in NO, across a large range of density-reduced electric fields from 0.003 to 10 000 Td.

An improved set of electron-THFA cross sections refined through a neural network-based analysis of swarm data

We review experimental and theoretical cross sections for electron transport in α-tetrahydrofurfuryl alcohol (THFA) and, in doing so, propose a plausible complete set. To assess the accuracy and self-consistency of our proposed set, we use the pulsed-Townsend technique to measure drift velocities, longitudinal diffusion coefficients, and effective Townsend first ionization coefficients for electron swarms in admixtures of THFA in argon, across a range of density-reduced electric fields from 1 to 450 Td. These measurements are then compared to simulated values derived from our proposed set using a multi-term solution of Boltzmann's equation. We observe discrepancies between the simulation and experiment, which we attempt to address by employing a neural network model that is trained to solve the inverse swarm problem of unfolding the cross sections underpinning our experimental swarm measurements. What results from our neural network-based analysis is a refined set of electron-THFA cross sections, which we confirm is of higher consistency with our swarm measurements than that which we initially proposed. We also use our database to calculate electron transport coefficients in pure THFA across a range of reduced electric fields from 0.001 to 10 000 Td.

A dynamical (e,2e) investigation into the ionization of the outermost orbitals of R-carvone

We report an experimental and theoretical investigation into the dynamics of electron-impact ionization of R-carvone. Experimental triple differential cross sections are obtained in asymmetric coplanar kinematic conditions for the ionization of the unresolved combination of the three outermost molecular orbitals (41a-39a) of R-carvone. These cross sections are compared with theoretical cross sections calculated within a molecular 3-body distorted wave (M3DW) framework employing either a proper orientation average or orbital average to account for the random orientation of the molecule probed in the experiment. Here, we observe that the overall scattering behavior observed in the experiment is fairly well reproduced within the M3DW framework when implementing the proper average over orientations. The character of the ionized orbitals also provides some qualitative explanation for the observed scattering behavior. This represents substantial progress when trying to describe the scattering dynamics observed for larger molecules under intermediate-impact energy and asymmetric energy sharing scattering conditions.