Revisiting Electronic and Nuclear Sputtering from Ions at Mercury Using Linear Cascade Theory
Bibliographic record
Abstract
Abstract This study revisits calculations using linear cascade theory (LCT) to estimate the relative importance of the ion-induced collisional sputtering yield (also referred to as knock-on, nuclear, or kinetic sputtering) and the ion-induced electronic sputtering yield. We focus on sputtering of Na from Mercury’s surface using data from the Mercury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) mission. The updated nuclear and electronic sputtering yields for H and He solar wind ions at 1 keV amu −1 , respectively, are approximately an order of magnitude larger than the values calculated using LCT in M. A. McGrath et al. Compared to this earlier work, our study uses a factor of 10 larger Na surface fraction and a factor of 3 lower total atom surface density based on MESSENGER data that were not available when the McGrath et al. study was carried out. Additional differences are the use of new data more relevant to Mercury’s surface minerals for the nuclear and electronic stopping-power cross sections and the surface binding energies. For the conditions considered in this study, the nuclear sputtering yields calculated using LCT show good agreement with the values calculated using recent binary collision approximation models. We qualitatively compare estimates of the Na sputtering source rate to other source processes for Mercury’s exosphere, considering recent studies of the precipitating ion flux based on MESSENGER data. Future experiments that measure the yield and ejecta energy spectra for simulated Mercury surface conditions, along with advanced modeling of ion–surface interactions, are required to reduce uncertainties and support exospheric studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".