High Performance Space Lubrication of MoS<sub>2</sub> with Tantalum
Bibliographic record
Abstract
Abstract Molybdenum disulfide coatings have been employed as lubricants for spacecraft since the 1950s but continue to face major engineering challenges including performance in both terrestrial air and deep space vacuum environments and service lifetimes on the order of decades without maintenance. Co‐deposition of MoS 2 with additive compounds provide enhancements in some circumstances but a lubricant which can perform in all space‐facing environments with long lifetimes remains an ongoing problem. Herein, it is demonstrated the multi‐environment adaptable performance of a novel MoS 2 + tantalum lubricant coating, which excels as a lubricant in both terrestrial and space environments while the benchmark space‐qualified commercial MoS 2 lubricants do not. It is noted that the 10% tantalum additive exhibits preferential oxidation in air to preserve the lubricating ability of MoS 2 while forming phases of TaS 2 , which aid in the exceptional lubrication of MoS 2 in ultra‐high vacuum. Additionally, completely different tribofilms of small particles and compact sheets are noted for air and vacuum environments, respectively, which allows for adaptable lubricating mechanisms from a single coating depending on the environment. This novel coating sets the benchmark as the first demonstrated instance of a fully versatile space lubricant which offers high‐performance in both terrestrial and deep space environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".