Interaction of Lunar Dust Simulants with Materials: Importance of Charging
Bibliographic record
Abstract
Abstract Materials exposed to lunar regolith dust and other environmental factors on the Moon may suffer permanent damage, risking catastrophic failures. Lunar dust poses the greatest threat. Preventive measures are crucial, with principles emerging to deter dust accumulation in vacuum conditions. Charging of dust and surfaces significantly affects adhesion. Charging sources include photoemission, solar wind, and secondary electron emission from Earth’s magnetosphere, resulting in positive charge on the dayside and negative charge on the nightside. In framework of NASA’s “Regolith Adherence Characterization (RAC) Payload” project, we initiated a program on conducting experiments in our Lunar Environment Simulator on interaction of lunar dust simulants with materials, similar to the RAC Payload experiment. As part of this program, we conducted a series of experiments to understand the effects of charges accumulating on dust simulants and the surfaces they interact with on the adhesion and mitigation of dust. We adapted a number of methods to charge the dust, - tribological, vacuum ultraviolet and plasma, and used a nanocoulomb meter set-up to evaluate the dust charge. A rotating disk sample holder enhances dust flow uniformity. In the experiments we measured the dust charge acquired under different conditions, with the aim to understand interaction models. This paper presents initial findings and discusses relevant models.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".