Lags in Desorption of Lunar Volatiles

Abstract

Monte Carlo simulations of gas motion inside a granular medium are presented in order to understand the interaction of lunar gases with regolith and improve models for surface-boundary exospheres, a common type of planetary atmosphere. Results demonstrate that current models underestimate the lifetime of weakly bonded adsorbates (e.g., argon) on the surface by not considering the effect of Knudsen diffusion, and suggest that thermal desorption of adsorbates should be modeled as a second-or-higher-order process with respect to adsorbate coverage. An additional discrepancy between present models and outgassing from a realistic porous boundary is found for surface-adsorbate systems containing a distribution of activation energies (e.g., water). In that case, the mobility of adsorbates between desorption events (i.e., surface diffusion), not considered in global models of the exosphere, controls their surface residence time via transitions between sites of low and high binding energy. Without mobility the equatorial surface retains more water over a lunar day because sites of low binding energy are not repopulated by motion along the grain surface when depleted. The effects of Knudsen and surface diffusion apply to other volatile species and help us partly understand why measurements of lunar exosphere constituents appear to indicate stronger bonding of gas with the lunar surface than measured in some laboratory experiments.

Establishing a Best Practice for SDTrimSP Simulations of Solar Wind Ion Sputtering

“…perfectly flat surface or an ensemble of uniform spherical grains (Cassidy & Johnson 2005;Kulchitsky et al 2018;Sarantos & Tsavachidis 2021). As shown in the present study, when considering sputtering from a spherical-shaped grain there is an increase in the total yield as compared to a flat surface due to the influence of oblique impacts in the cosine distribution.…”

Surface Exospheric Interactions

“…Additionally, the likely mobility of water adsorbates on surfaces with a distribution of desorption energies further changes the residence time by enabling motion between low and high energy sites. Sarantos and Tsavachidis (2021) have recently suggested that surface diffusion affects the desorption rate in a nonlinear manner, suppressing desorption at lower temperatures yet enhancing desorption at higher temperatures.…”

“…Using kinetic parameters that represent alkali, argon, and water adsorbates, it was demonstrated that surface and Knudsen diffusion, processes competitive to desorption, reduce the desorption rates from a porous medium beyond the rate reduction due to re-adsorption. Sarantos and Tsavachidis (2021) pointed out that thermal desorption from a granular medium is a decelerating process because it initiates Knudsen diffusion which in turn slows down desorption. These authors proposed that thermal desorption of adsorbates from a powder is not a first-order (i.e.…”

A Comprehensive Model for Pickup Ion Formation at the Moon

“…Additionally, the model deliberately simplifies some aspects of both the neutral exosphere and the pickup ion distributions. The greatest simplification likely originates in the neutral distributions, as our analytic descriptions do not necessarily take into account complex processes such as the effects of solar radiation pressure of Na and K dynamics (e.g., Ip, 1991; Matta et al., 2009; Smyth & Marconi, 1995; Wilson et al., 2003) or reactions between individual atoms and the lunar surface that may alter equilibrium density distributions (e.g., Sarantos & Tsavachidis, 2020, 2021). Nevertheless, the model is constructed in such a way that future three‐dimensional neutral distributions for any species derived from such Monte Carlo methods can easily replace any of the analytic distributions in the model, if so desired.…”

“…We emphasize that the nature of this model is to use time‐independent analytic descriptions for both the neutral density distributions and pickup ion dynamics, instead of more complex and computationally intensive neutral Monte Carlo (e.g., Grava et al., 2015; Hurley et al., 2016; Killen et al., 2012; Lee et al., 2011; Sarantos & Tsavachidis, 2021) or ion particle‐tracing techniques (e.g., Cladis et al., 1994; Poppe, Halekas, Samad, et al., 2013). Thus, the model does not, for example, track individual macroparticles (for either neutral or ionized species) through a grid‐based domain or use time‐dependent inputs from, for example, upstream solar wind variability or ionization rate variability.…”