Uncovering the Role of Hydroxylation and Oxygen Depletion on the Surface Binding Energy of Adsorbates: A Molecular Dynamics Study
Bibliographic record
Abstract
Introduction: The surfaces of airless, planetary bodies like the Moon and Mercury are constantly exposed to several processes that eject atoms into the exosphere1,2. While the composition and densities of these exospheres are 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. While complex laboratory studies drive reliance on theoretical models to study these exosphere-surface interactions.Molecular dynamics (MD) simulations rely on interatomic potentials to model interactions at the atomic level without requiring user-provided inputs. However, their high computational load restricts the size and timescale of simulations. As an alternative, MD can be employed to determine critical mineral-specific parameters, such as the surface binding energy (SBE), that can then serve as inputs for large-scale ejection models.This methodology has been used to study mineral-specific SBEs of both crystalline and amorphous surfaces4,5. More recently, we also considered the effect of adsorbates (i.e., atoms that eject below the escape energy and return to the surface) on SBE and subsequent emission processes for crystalline and amorphous surfaces6. However, the surfaces of these planetary bodies are much more complex than has been considered in these studies. For example, space weathering causes transient or localized hydroxylation of surface minerals via solar wind proton interactions7, as well as dynamic oxygen depletion due to preferential sputtering8. No study has considered how these different structures will affect the SBE and subsequent predicted exospheres. Without considering these complexities the MD derived SBEs are limited in their application to exosphere modelers.Methodology: This study evaluates the adsorption of Na onto hydroxylated and oxygen depleted SiO2, albite, and anorthite, determining SBE distributions using MD simulations.Results: Figure 1 displays the SBE distributions of adsorbed Na on 100% hydroxylated and clean (i.e., non-hydroxylated) SiO2, albite, and anorthite surfaces.Figure 1. SBE distributions of adsorbed Na on the hydroxylated surfaces of SiO2, albite, and anorthite. The whole lines represent the hydroxylated SBE distributions of each surface, and the dashed lines represent the clean surface (i.e., non-hydroxylated) SBE distributions.First, it is evident that the hydroxylation of SiO2, albite, and anorthite surfaces results in a distinct shift of SBE distributions and significantly reduces the average SBEs of adsorbed Na. The average SBE of Na adsorbed onto a clean SiO2 surface is 5.7 eV, while the average SBE of Na adsorbed onto a hydroxylated SiO2 surface is 2.8 eV. We attribute these findings to the unique bond types and interactions formed in each case.In the clean surface models, it is expected that adsorbed Na atoms form ionic bonds with the surface oxygen atoms which have a high bond strength. In the hydroxylated surface models, the surface oxygen atoms are already bonded to hydrogen, reducing its availability for interactions with the adsorbed Na. Instead, Na may be interacting via hydrogen bonding or weak van der Waals interactions with -OH groups, resulting in lower SBEs. These findings align with previous relevant literature9,10, however future experimental work is required for validation.In summary, the localized hydroxylation of surface minerals on the Moon and Mercury suggests that lower energy sites are available for adsorbates, making ejection processes of these atoms significantly more efficient. It is therefore paramount to understand the extent of localized hydroxylation on the surface of airless bodies in order to then quantify the contributions of different sources into the exosphere. Next, surface oxygen depletion of these surfaces will be investigated. The development of a database of SBEs that capture the complexities of surfaces on airless planetary bodies is crucial for connecting source processes to atomic species and achieving a complete understanding of the surface-exosphere connection.References:1. Killen, R.M., et al. (1999)2. Wurz, P., et al. (2022)3. McClintock, et al. (2018)4. Morrissey, L., et al. (2022)5. Morrissey, L., et al. (2024a)6. Morrissey, L., et al. (2024b)7. Yeo, L.H., et al. (2024)8. Killen, R.M., et al. (2007)9. Wang, X. (2023)10. Wang, G., et al. (2020)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".