Simulating Space Weathering in the Transmission Electron Microscope via Dynamic in Situ Heating and Helium Irradiation of Olivine
Bibliographic record
Abstract
The chemical composition, microstructure, and optical properties of grains on the surfaces of airless bodies are predominantly altered by micrometeorite impacts and solar wind irradiation. These processes drive space weathering and result in the formation of features including chemically-altered, amorphous grain rims, Fe nanoparticles (npFe), and vesiculated grain textures. These characteristics have been identified in returned samples from the surfaces of the Moon and asteroid Itokawa. In order to advance our understanding of the formation of these microstructural and chemical features in returned samples, we have simulated space weathering processes for a variety of materials via laboratory experiments. These experiments include ion irradiation to simulate solar wind exposure and laser irradiation and in situ heating to simulate micrometeorite impacts. While these experiments have provided considerable insight into the formation mechanisms of many space weathering features, they are predominantly static and typically performed separately. Here we present results from the simulated space weathering of olivine grains via He irradiation and dynamic heating, both performed in situ inside the transmission electron microscope (TEM). These experiments allow for the real-time observation of chemical and microstructural changes resulting from the superposed effects of ion irradiation and pulsed heating.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".