In situ experimental formation and growth of Fe nanoparticles and vesicles in lunar soil
Bibliographic record
Abstract
Abstract We report the results of the first dynamic, in situ heating of lunar soils to simulate micrometeorite impacts on the lunar surface. We performed slow‐ and rapid‐heating experiments inside the transmission electron microscope to understand the chemical and microstructural changes in surface soils resulting from space‐weathering processes. Our slow‐heating experiments show that the formation of Fe nanoparticles begins at ~575 °C. These nanoparticles also form as a result of rapid‐heating experiments, and electron energy‐loss spectroscopy measurements indicate the Fe nanoparticles are composed entirely of Fe 0 , suggesting this simulation accurately mimics micrometeorite space‐weathering processes occurring on airless body surfaces. In addition to Fe nanoparticles, rapid‐heating experiments also formed vesiculated textures in the samples. Several grains were subjected to repeated thermal shocks, and the measured size distribution and number of Fe nanoparticles evolved with each subsequent heating event. These results provide insight into the formation and growth mechanisms for Fe nanoparticles in space‐weathered soils and could provide a new methodology for relative age dating of individual soil grains from within a sample population.
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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".