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Record W4412122532 · doi:10.5194/epsc-dps2025-1213

Atomic-scale simulations of solar wind sputtering of airless bodies by solar wind ions

2025· preprint· en· W4412122532 on OpenAlexaff
Anastasis Georgiou, Benjamin Alan Clouter-Gergen, K. Nordlund, Flyura Djurabekova, Eduardo M. Bringa, Liam S. Morrissey

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSolar windIonSputteringScale (ratio)Environmental sciencePhysicsMeteorologyAstrobiologyAstronomyAtmospheric sciencesAerospace engineeringMaterials sciencePlasmaNanotechnologyEngineeringThin film

Abstract

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IntroductionSputtering of surfaces by ion irradiation is an important process in planetary science, influencing the exospheric composition and surface evolution of airless bodies such as the Moon, Mercury, icy bodies, and asteroids. Such bodies without a significant atmosphere or intrinsic magnetic field are directly impacted by solar wind (SW) ions originating from the Sun’s corona and consisting of approximately 95% protons (H+), 4% alpha particles (He++) and 1% minor ions. These impacts can cause atoms from the surface of the airless body to be ejected into its exosphere, influencing its formation and composition. For example, sodium (Na) abundance in Mercury’s exosphere has been correlated to SW activity and magnetic field dynamics [1].Binary collision approximation (BCA) models have been used to model sputtering of regolith grains like those found on the surface of the Moon and Mercury [2–4]. While BCA models can be used to understand the implantation and ejecta characteristics, they require key user-specified inputs such as surface binding energy (SBE) which can be derived through molecular dynamics (MD) simulations [5]. In addition, BCA models can only simulate single atom ejections without taking into account molecules that can be ejected while also being unable to simulate the complex bond breakage and formation occurring during energetic impacts.Despite being more computationally expensive, MD simulations can provide an alternative method of simulating the entire sputtering process of surfaces by SW ions. While MD sputtering simulations of planetary surface silicates are not well studied, previous research has used MD to study the ejection of atoms and molecules from icy surfaces [6] by energetic ion impacts. In addition, Huang et al. used MD simulations with a reactive force field (ReaxFF), which allows for bond breakage and formation, to study the implantation of SW hydrogen on the Moon.MethodologyIn this study, we use MD with a ReaxFF potential to simulate the sputtering process of energetic SW ions impacting an amorphous albite substrate. We impact the albite surface with 1 keV hydrogen and 4 keV helium (similar to SW conditions) at an angle normal to the substrate surface. The simulation includes both cumulative and non-cumulative bombardment. During cumulative impacts, the surface is continuously bombarded by ions over time, whereas in non-cumulative impacts, the surface resets to its initial state before being bombarded again with a hydrogen ion. After each impact for both cases, we sample the system for any ejected atoms or molecules and record their energy, velocity and ejection angle. We then compare our MD simulations to similar BCA models and available experimental data.ResultsInitial results show the ability of MD simulations to better understand SW sputtering on mineral substrates, potentially removing the need for complex calculations of SBEs and the errors introduced by BCA modelling. These preliminary results show a complex distribution of the sputtering yield, dominated by O atoms. From these initial 50 H and He impacts, no molecules were ejected from the substrate. In addition, we observe an initial sputtering yield of 0.14 for H ions and 0.32 for He ions, a behaviour that is expected due to the higher energy of the He ions. In both incident ion cases, more than 50% of the sputtering yield is O atoms. This agrees well with MD simulations of O SBEs that suggest that O can be weakly bound to the surface. Building on these results, we will use cluster computing resources to significantly increase statistics by simulating thousands of impacts (both static and dynamic) and better capture the sputtering yield, sputter energy, angle and ion backscatter. This will allow us to evaluate preferential sputtering and how the energy distribution (and thus the SBE) can potentially vary as weathering via SW progresses. We will then compare these findings to predictions from BCA modelling, highlighting the significance of molecular interactions and surface change in the sputtering process. Furthermore, we anticipate that the data will reveal insights into the role of surface roughness and defect structures on the ejection dynamics of atoms from the amorphous albite surface. In addition, unlike BCA models MD can identify any molecules that may sputter from the silicate surface. Finally, further simulations will aim to study the sputtering process of adsorbed sodium on amorphous albite as BCA models can only model adsorbed species as changes in the concentration and not as chemically adsorbed species.References[1] R.M. Killen, M. Sarantos, A.E. Potter, P. Reiff, Icarus 171 (2004) 1–19.[2] N. Jäggi, A. Mutzke, H. Biber, J. Brötzner, P.S. Szabo, F. Aumayr, P. Wurz, A. Galli, Planet Sci J 4 (2023) 86.[3] P.S. Szabo, R. Chiba, H. Biber, R. Stadlmayr, B.M. Berger, D. Mayer, A. Mutzke, M. Doppler, M. Sauer, J. Appenroth, J. Fleig, A. Foelske-Schmitz, H. Hutter, K. Mezger, H. Lammer, A. Galli, P. Wurz, F. Aumayr, Icarus 314 (2018) 98–105.[4] L.S. Morrissey, M.J. Schaible, O.J. Tucker, P.S. Szabo, G. Bacon, R.M. Killen, D.W. Savin, Planet Sci J 4 (2023) 67.[5] L.S. Morrissey, O.J. Tucker, R.M. Killen, S. Nakhla, D.W. Savin, Astrophys J Lett 925 (2022) L6.[6] C. Anders, H.M. Urbassek, 482 (2019) 2374–2388.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.238
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
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