Atomic-scale simulations of solar wind sputtering of airless bodies by solar wind ions
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».