Angular distributions of sputtered ejecta are critical inputs for simulating regolith sputtering on airless bodies, where surface topography and porosity determine if particles escape or redeposit. We present new analysis and simulations demonstrating that, although current binary collision approximation (BCA) frameworks can reliably predict either total sputter yields or angular distributions for minerals, they cannot reproduce both simultaneously. This is a limitation with direct implications for exosphere and surface-evolution models that require realistic, three-dimensional ejecta plumes. Using SDTrimSP^[1] for flat targets, SPRAY^[2] for AFM-resolved rough surfaces, and SDTrimSP-3D^[3] for porous regolith analogs, we quantify how morphology and binding energies control plume tilt and shape via a dedicated three-dimensional lobe-fitting approach designed for full-plume implementation in modeling tools. This framework addresses the need to move beyond simplified cosine laws and to incorporate azimuthal structure when propagating sputtered particles in regolith and exosphere simulations.

Across surface morphologies, rough and porous targets deflect ejecta toward the surface normal, reducing the strongly forward-facing plumes typical of flat surfaces and introducing geometry-mediated effects such as shadowing and redeposition that reshape both yield and energy distributions. In this regime, we find a robust tradeoff between reproducing sputter yield and plume tilt: the “compound/hybrid” binding-energy model^[4] capable of matching experimental mineral yields^[5] fails to reproduce the experimentally observed forward tilt at oblique incidence on flat and, more significantly, on rough surfaces (Fig. 1). Conversely, adopting increased surface-binding energies (SBEs) informed by recent molecular dynamics work^[6]  strengthens the forward tilt and better matches rough-surface angular data but underestimates absolute mass yields by factors of 3–4.

Methodologically, we map flat-surface SDTrimSP ejecta to measured roughness via ray tracing in SPRAY and construct porous regolith stacks in SDTrimSP-3D, then fit the resulting plumes with a single- or tri-lobe model that captures central and side “winglet” components associated with single-collision recoils (Fig. 2). This procedure allows for consistent comparison to laboratory angular data and direct use of fitted plumes in forward models. For rough surfaces, the simulations confirm a forward tilt relative to the sample surface normal but with magnitudes that depend sensitively on the binding-energy scheme.  Only the higher-SBE case reproduces the observed forward-tilted distributions. For porous targets, preliminary comparisons and prior 3D simulations indicate stronger backscattering, further emphasizing the need for morphology-aware angular inputs in regolith sputtering models.

These findings have two key implications. First, exosphere and regolith models should incorporate full 3D angular distributions rather than rely on simple cosine laws, especially for rough and porous substrates where plume tilt and azimuthal structure are non-negligible. Second, the inability to match yield and angle concurrently suggests missing physics in BCA implementations—most notably, yield-enhancing processes such as molecule and cluster sputtering that are not represented in single-atom BCA ejecta—and cautions against parameter tuning. In practice, our results recommend using binding-energy models that best reproduce angular distributions when plume directionality controls transport and redeposition, while acknowledging that total yields must then be corrected for non-BCA processes if absolute source strengths are required.

In summary, angular distribution data are indispensable for realistic regolith sputtering simulations, yet existing BCA parameterizations cannot simultaneously reproduce experimental mass yields and angular distributions across flat, rough, and porous mineral surfaces. This demonstrates a fundamental limitation of current BCA-based models for planetary surfaces and motivates hybrid or extended frameworks that incorporate missing yield mechanisms while retaining accurate, morphology-dependent plume geometry.

References

[1] Mutzke, A., Toussaint, U. v, Eckstein, W., Dohmen, R. & Schmid, K. (2024). SDTrimSP Version 7.00. Technical Report

[2] Cupak, C. et al. Appl. Surf. Sci. 570, 151204 (2021).

[3] von Toussaint, U., Mutzke, A. & Manhard, A. Phys. Scr. T170, 014056 (2017).

[4] Jäggi, N. et al. Planet. Sci. J. 4, 86 (2023).

[5] Biber, H. et al. Planet. Sci. J. 3, 271 (2022).

[6] Morrissey, L.S. et al. Planet. Sci. J. 5, 272 (2024).

How to cite: Jäggi, N., Woodson, A. K., Szabo, P. S., Brötzner, J., Aumayr, F., and Dukes, C. A.: Modeling Limits of BCA for Sputtering Regolith, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-227, https://doi.org/10.5194/epsc2026-227, 2026.

Regolith roughness is expected to modulate the sputter flux of surface atoms into the exospheres of airless bodies such as Mercury, and studies have shown that roughness from the nanometer scale upward promotes redeposition and diminishes total sputtering yields. Experiments involving irradiation of olivine, plagioclase, and single-element powders demonstrate yield reductions from 15% to 67% as compared to smooth targets, but the grain size dependence of this effect has not been adequately parameterized. Understanding this mechanism is therefore of critical importance for quantifying desorption and sputter ejection and for predicting the relative contributions of release processes from regolith surfaces.

We measured the sputter depletion of adsorbed Na from polished natural olivine and from synthetic granular forsterite targets with narrow grain size distributions from 45 μm up to 520 μm. Na vapor was deposited onto each target in an ultrahigh vacuum system and then irradiated at either 15° or 60° incidence (from global surface normal) to prescribed fluence steps using 4 keV He+ ions. All experimental steps were carried out at room temperature (~300 K). After each fluence step an X-ray photoelectron spectrum was acquired and used to quantify the remaining Na surface concentration. Depletion rates and cross sections were then extracted from the concentration vs. fluence data for each target.

For the polished targets, Na concentrations exhibited single-exponential decay with increasing fluence, and depletion at 60° incidence outpaced that at 15° due to increased energy deposition near the surface as expected from theory. Conversely, for all granular samples the Na concentrations exhibited at least double-exponential decay, suggesting a bimodal removal process, and depletion from the granular targets occurred more quickly at 15° than at °60, implying enhanced access to grain facets at near normal incidence. We propose that this reflects faster removal of Na that is directly exposed to the incident ion flux, convolved with slower removal of shadowed Na by reflected incident ions. We found that the shadowed sodium was sputtered away 10–100 times more slowly than the exposed Na, with a transition from single-exponential to double-exponential decay at some threshold between nanoscale roughness (polished targets) and microscale roughness (<45 um). These results may help to explain, for example, why current models underestimate the persistence of Na density enhancements in Mercury’s dayside exosphere, as observed by the MESSENGER spacecraft’s UltraViolet and Visible Spectrometer.

Additionally, we present a novel ray-tracing model that incorporates SDTrimSP simulated sputter distributions and high-resolution, triangulated surface reconstructions of the experimental sputter targets, obtained via digital optical microscopy. The model tracks the fluence of primary and reflected incident ions onto each facet of the triangulated surface, along with the transfer of adsorbate atoms from facet to facet and from each facet into space (i.e. loss). By tuning this model to reproduce our laboratory observations, we investigate the redistribution of adsorbate atoms with increasing fluence and the associated surface shape parameters that modulate the transition from fast, single-exponential sputter depletion to slow, double-exponential sputter depletion.

How to cite: Woodson, A. K., Jaeggi, N., and Dukes, C. A.: Bimodal sputter depletion of adsorbates from granular, regolith-like targets: laboratory measurements and comparative simulations, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-1115, https://doi.org/10.5194/epsc2026-1115, 2026.

Introduction: The surfaces of airless, planetary bodies like the Moon and Mercury are constantly exposed to several processes that eject atoms into the exosphere^1,2. While the composition and densities of these exospheres are partially known based on observational data from exploratory missions³, the source of each component is not well understood. This uncertainty necessitates better constraints for accurately interpreting the observed exospheric data, which requires both experimentally constrained measurement and atomistic-level physical insight.

Laboratory studies provide valuable measurements of adsorption and desorption behavior, while molecular dynamics (MD) simulations enable direct investigation of the atomic-scale interactions that cannot be resolved experimentally. By combining these approaches, it becomes possible to better constrain the physical mechanisms controlling surface-exosphere interactions on bodies such as Mercury and the Moon.

In this work, MD simulations of Na and K adsorption on silica surfaces are combined with thermal stimulated desorption (TSD) experiments of Na and K adsorbed onto silica. The study focuses on understanding how surface coverage and environmental hydroxylation influence adsorption energetics and desorption behavior⁴. The combined computational and experimental approach not only provides validation between methods, but also demonstrates how MD simulations can explain experimentally observed trends and identify surface processes relevant to planetary environments.

Methodology: MD simulations were performed to investigate Na and K adsorption on silica surfaces under varying surface coverages and surface chemistries. Surface binding energies (SBEs) were calculated for adsorbed Na and K atoms to quantify the strength of adsorbate-surface interactions and to identify how adsorption behavior evolves from isolated adsorbates to high-coverage surfaces⁵. Additional simulations examined H covered silica surfaces to evaluate the effect of hydroxylation on adsorbed Na SBEs.

TSD experiments were conducted by collaborators using Na- and K-dosed Apollo samples⁶. Following adsorbate deposition, samples were heated to approximately 1000K while monitoring desorption behavior to derive activation energies associated with adsorbate release from the surface. The experimental desorption energetics were compared directly with the MD-derived SBEs to investigate the physical origins of the experimentally observed desorption behavior.

Results:  The combined MD and experimental results demonstrate strong agreement between stimulated adsorption energetics and experimentally observed desorption behavior. Figure 1 displays the SBE distributions of adsorbed Na on a bare silica surface, Na covered surface, and hydroxylated surface.

MD simulations show that isolated Na atoms adsorbed directly onto bare silica surfaces form strongly bound adsorption states with SBEs exceeding 5 eV. These energies are larger than the effective experimental detection range of the TSD studies, which are insensitive to adsorption energies above approximately 3.5 eV at the maximum experimental temperature of 1000K. Consequently, strongly bound adsorbates detected in the MD results are unlikely to desorb during experimental heating cycles.

This provides important insight into the physical experiments, suggesting that residual adsorbates likely remain on the silica surface between experimental runs despite minimal dosing conditions. Over time, this accumulated coverage may produce increasingly significant Na-Na or K-K interactions and lower-energy adsorption states, consistent with the experimentally observed desorption energies. In this way, the MD simulations provide a physical explanation for experimental behavior that cannot be directly measured.

Further MD simulations demonstrate that hydroxylated silica surface significantly reduce adsorbate SBEs relative to bare silica (by ~2.9 eV). This suggests that exposure of experimental samples to ambient atmospheric water or H during sample transfer may hydroxylate undercoordinated surface oxygens, producing weaker adsorption states and lower desorption temperatures in the TSD measurements. Similar hydroxylation processes are also expected on Mercury and the Moon due to solar wind hydrogen implantation, likely resulting in spatially variable adsorption energetics across the planetary surface.

Together, these results demonstrate how combining MD simulations with laboratory experiments enables a more reliable interpretation of both datasets while providing improved insight into the surface processes governing volatile behavior on airless planetary bodies.

Conclusion: This study demonstrates the strength of integrating MD simulations with laboratory thermal desorption experiments to investigate surface processes on airless planetary bodies. While experiments provide direct measurements of adsorbate desorption behavior, MD simulations reveal the underlying atomic-scale interactions responsible for the observed trends. The combined approach identifies the important roles of surface coverage and hydroxylation in controlling adsorption energetics and explains how experimentally inaccessible strongly bound adsorption states may influence measured desorption behavior.

By coupling computational and experimental methods, this work provides a more physically complete understanding of adsorbate-surface interactions and highlights the importance of considering realistic surface environments when interpreting laboratory measurements and modelling planetary surface-exosphere systems.

References:

1. Killen, R.M., et al. (1999)

2. Wurz, P., et al. (2022)

3. McClintock, et al. (2018)

4. Yeo, L.H., et al. (2024)

5. Morrissey, L., et al. (2025)

6. McLain, J.L., et al. (2024)

How to cite: Ricketts, A., McLain, J., Yeo, L. H., Sarantos, M., Verkercke, S., and Morrissey, L.: A Combined Computational and Experimental Approach to Investigating Volatile-Surface Interactions on Airless Planetary Bodies, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-640, https://doi.org/10.5194/epsc2026-640, 2026.

How to cite: Amorós-Trepat, M., Brecher, N., Peschel, A., and Reiss, P.: Studying the Influence of Surface Diffusion on Lunar Subsurface Water Migration, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-849, https://doi.org/10.5194/epsc2026-849, 2026.

Water on the Moon is crucial for understanding its origin and evolution and is also of interest for future human exploration. Recent observations show that water ice and hydroxyl are mainly concentrated in permanently shadowed regions near the lunar poles, and these species have also been detected in the lunar exosphere. However, how exospheric water is distributed and how it is coupled to the surface remain poorly constrained.

Using the Ion Mass Analyzer onboard the Kaguya spacecraft, we report the detection of water ions originating from the lunar exosphere. We identify statistically significant low-energy water ions and characterize their latitudinal distribution, showing that their flux is enhanced in the polar regions compared with lower latitudes, consistent with previous remote-sensing observations of lunar water. From the observed flux, we estimate near-surface exospheric water density, comparable to previous in-situ measurements.

These results demonstrate that the spatial distribution of surface water is reflected in the exospheric ion population, indicating a dynamically coupled surface–exosphere system. Ion measurements therefore provide a powerful tool for investigating the release, transport, and loss of water on airless bodies.

How to cite: Masunaga, K., Yokota, S., Harada, Y., Keika, K., Terada, N., Sakai, S., Matsuoka, A., Nakagawa, H., Arai, Y., Saito, Y., Nishino, M., Ohtake, M., Asamura, K., Shimizu, H., Takahashi, F., and Matsushima, M.: Polar enhancement of lunar water ions observed by Kaguya: Implications for surface-exosphere coupling, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-424, https://doi.org/10.5194/epsc2026-424, 2026.

A range of physical and chemical processes characterises the tenuous atmospheres of the Galilean moons. Io, the innermost Galilean moon, is subject to extreme tidal forces that drive intense volcanic activity, ultimately responsible for Io's SO₂-dominated atmosphere. Next to SO₂, where the sublimation of surface frost is expected to be the main source [1], dissociation products (e.g., SO, S, O), along with compounds connected with volcanic eruptions (e.g., S₂, NaCl, KCl) have been detected in Io’s atmosphere. On the contrary, the main atmospheric species of the icy Galilean moons are expected to be water-related products, such as H₂O, O₂, and H₂. Moreover, dissociation products (O, H) and non-water-related species, such as CO₂, have been detected.

In this study, we use the Direct Simulation Monte Carlo (DSMC) model ultraSPARTS (ultrafast Statistical PARTicle Simulation package) [2, 3] to compare the atmospheres of the Galilean satellites. For this purpose, we investigate the influence of various processes, including sublimation, radiolysis, and outgassing from (cryo-)volcanic plumes. To model the sublimation of SO₂ and H₂O, we apply an adapted version of the thermal model THERMPROJRS [4] to constrain the surface frost temperature on the satellites, which governs the vapour pressure and, therefore, determines whether sublimation or condensation occurs.  Furthermore, we investigate the influence of different collision cross-sections and particle collisions on the macroscopic atmospheric properties.

In the 2030s, the atmospheres of the icy Galilean moons will be studied extensively by the JUICE and Europa Clipper missions using high-resolution mass spectrometry, providing an unprecedented opportunity to compare and verify our results with in-situ data.

Acknowledgements:

This work has been carried out within the framework of the National Centre of Competence in Research PlanetS supported by the Swiss National Science Foundation under grant 51NF40_205606. The authors acknowledge the use of ultraSPARTS from Plasma T.I., Taiwan.

References:

[1] Giles, R. S., et al. (2024). Icarus, 418, 11615.
[2] http://www.plasmati.com.tw/
[3] Klaiber, L. M. (2024). Three-dimensional DSMC modelling of the dynamics of Io’s atmosphere. PhD thesis, University of Bern.
[4] Spencer, J. R. (1989). Icarus 78, 337-354.

How to cite: Schlarmann, L., Vorburger, A., Mosimann, T., Magee, B., Amsler Moulanier, A., Fatemi, S., Thomas, N., and Wurz, P.: Comparing the Galilean moon atmospheres using the DSMC method, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-946, https://doi.org/10.5194/epsc2026-946, 2026.

The discovery of widespread hydration across the lunar surface [1,2,3] is one of the most surprising results of the last twenty years in the field of planetary science. Further considerations have extended to exospheric migration of water on other airless bodies, such as Mercury and Ceres [4]; other works have considered the coupling of multiple volatile species within the lunar volatile inventory [5,6], or have considered the icy moons of the outer Solar System [7], with the seasonal migration of CO2 on the Uranian moon Ariel [8,13] being one such example.

A difficulty in modelling volatile transport is the interplay between various physical and chemical processes, including adsorption kinetics, photochemistry, and transport kinematics. Hence, Monte Carlo modelling has remained the dominant mode of investigation, producing qualitative outputs surveying emergent phenomena. The development of a comprehensive and quantitative forward-modelling approach has remained an outstanding problem.

To produce such a model, we turn to Markov processes [9]. The governing equations of these processes generate, via Kramers-Moyal expansion [10], such familiar results as the Fokker-Planck, continuity, and diffusion equations.

We derive a Markov master equation for a global volatile ensemble from mass-balance. With straightforward probability theory, we model adsorption kinetics at a molecular level, and ballistic transport on a global scale, providing an integrated analytic approach to global volatile dynamics.

Going further, we use tensor products between Markov processes [11,12] to model the interplay between volatile species, allowing for capture of kinetic schemes driven by surficial and photochemical reactions.

We implement a simplified, straightforward model. The global ensemble was taken to be 10^30 water molecules. 4200 timesteps per lunation are calculated for 100,000 timesteps - approximately 607 seconds per timestep. We neglect implantation and loss mechanisms as well as topography. We use an analytic model of lunar surface temperatures [14]. Desorption probabilities are calculated from the Eyring-Polanyi equation [15,16], with an activation energy of 0.7 eV. The Armand distribution [17,18] drives ballistic-hop transport.

Figure 1: Lunar surficial water abundance; simplified model run, timestep 8200.

Timestep 8200 is shown in Figure 1. Volatile concentration is heightened in the southern winter. The initial volatile distribution was random; onset of equilibrium conditions is rapid. A dusk-dawn asymmetry is present, reproducing the results of Schörghofer [19]. Latitudinal stratification is visible, with the band of minimal concentration dependent on solar declination.

We conclude by presenting paths forward in volatile modelling efforts enabled by this approach.

[1] Pieters, C.M., Goswami, J.N., Clark, R.N., Annadurai, M., Boardman, J., Buratti, B., Combe, J.P., Dyar, M.D., Green, R., Head, J.W. and Hibbitts, C., 2009. Character and spatial distribution of OH/H2O on the surface of the Moon seen by M3 on Chandrayaan-1. science, 326(5952), pp.568-572.

[2] Sunshine, J.M., Farnham, T.L., Feaga, L.M., Groussin, O., Merlin, F., Milliken, R.E. and A’Hearn, M.F., 2009. Temporal and spatial variability of lunar hydration as observed by the Deep Impact spacecraft. Science, 326(5952), pp.565-568.

[3] Clark, R.N., 2009. Detection of adsorbed water and hydroxyl on the Moon. Science, 326(5952), pp.562-564.

[4] Schörghofer, N., Benna, M., Berezhnoy, A.A., Greenhagen, B., Jones, B.M., Li, S., Orlando, T.M., Prem, P., Tucker, O.J. and Wöhler, C., 2021. Water group exospheres and surface interactions on the Moon, Mercury, and Ceres. Space Science Reviews, 217(6), p.74.

[5] Huebner, W.F. and Mukherjee, J., 2015. Photoionization and photodissociation rates in solar and blackbody radiation fields. Planetary and Space Science, 106, pp.11-45.

[6] Smolka, A., Nikolić, D., Gscheidle, C. and Reiss, P., 2023. Coupled H, H2, OH, and H2O lunar exosphere simulation framework and impacts of conversion reactions. Icarus, 397.

[7] Steckloff, J.K., Goldstein, D., Trafton, L., Varghese, P. and Prem, P., 2022. Exosphere-mediated migration of volatile species on airless bodies across the solar system. Icarus, 384, p.115092.

[8] Cartwright, R.J., Nordheim, T.A., DeColibus, R.A., Grundy, W.M., Holler, B.J., Beddingfield, C.B., Sori, M.M., Lucas, M.P., Elder, C.M., Regoli, L.H. and Cruikshank, D.P., 2022. A CO2 Cycle on Ariel? Radiolytic production and migration to low-latitude cold traps. The Planetary Science Journal, 3(1), p.8.

[9] Livi, R. and Politi, P., 2025. Nonequilibrium Statistical Physics: a Modern Perspective. 2nd edition. Cambridge University Press.

[10] Kramers, H.A., 1940. Brownian motion in a field of force and the diffusion model of chemical reactions. physica, 7(4), pp.284-304.

[11] Dayar, T., 2012. Analyzing Markov chains using Kronecker products: theory and applications. Springer Science & Business Media.

[12] Giry, M., 2006, October. A categorical approach to probability theory. In Categorical Aspects of Topology and Analysis: Proceedings of an International Conference Held at Carleton University, Ottawa, August 11–15, 1981 (pp. 68-85). Berlin, Heidelberg: Springer Berlin Heidelberg.

[13] Grundy, W.M., Young, L.A., Spencer, J.R., Johnson, R.E., Young, E.F. and Buie, M.W., 2006. Distributions of H2O and CO2 ices on Ariel, Umbriel, Titania, and Oberon from IRTF/SpeX observations. Icarus, 184(2), pp.543-555.

[14] Crider, D.H. and Vondrak, R.R., 2000. The solar wind as a possible source of lunar polar hydrogen deposits. Journal of Geophysical Research: Planets, 105(E11), pp.26773-26782.

[15] Eyring, H., 1935. The activated complex in chemical reactions. The Journal of chemical physics, 3(2), pp.107-115.

[16] Evans, M.G. and Polanyi, M., 1935. Some applications of the transition state method to the calculation of reaction velocities, especially in solution. Transactions of the Faraday Society, 31, pp.875-894.

[17] Armand, G., 1977. Classical theory of desorption rate velocity distribution of desorbed atoms; possibility of a compensation effect. Surface Science, 66(1), pp.321-345.

[18] Schörghofer, N., 2022. Statistical thermodynamics of surface-bounded exospheres. Earth, Moon, and Planets, 126(2), p.5.

[19] Schorghofer, N., 2014. Migration calculations for water in the exosphere of the Moon: Dusk‐dawn asymmetry, heterogeneous trapping, and D/H fractionation. Geophysical Research Letters, 41(14), pp.4888-4893.

How to cite: Eshbaugh, H. and Bowles, N.: Stochastic Modelling of Volatile Transport in Surface-Bound Exospheres, Europlanet Science Congress 2026, The Hague, The Netherlands, 6–11 Sep 2026, EPSC2026-737, https://doi.org/10.5194/epsc2026-737, 2026.