Identification of collected volatile condensable material (CVCM) from ASTM E595 of silicone damper fluid
Bibliographic record
Abstract
Polydimethylsiloxane damping fluids used for structural deployment mechanisms are not required to be low outgassing. During normal use, these damping fluids are typically encapsulated; however, an unintentional leak may occur which would cause an undesirable contamination at the leak point and form volatile condensable that could reach contamination-sensitive surfaces, degrading the performance of satellites. The collected volatile condensable material (CVCM) at 25 °C from ASTM E595 of a damping fluid, MeSi-300K, was < 0.10%, when the damping fluid was maintained at 125 °C for 24 hours under 10-6 Torr vacuum. MeSi-300K viscosity is 300,000 cSt, which indicates an average molecular weight (MW) of 204,000. This large MW polymer would contain about 2,756 dimethyl siloxane (DMS) units in the chain. These long chains are not expected to be volatile; however, during manufacture, linear chains and cyclic compounds of a smaller number of DMS units produced are volatile. Gas chromatography mass spectrometry (GC-MS) was used to identify the CVCM. Characterization of these materials revealed that the CVCM contained higher MW siloxanes, straight chain and cyclic, in the range of 682 to 1196 (9 to 16 DMS units), whereas CVCM from spacequalified, silicone-based materials have lower MW, 222 to 542 (3 to 7 DMS units). Consequently, contamination from MeSi-300K material would produce greater amounts of higher-MW siloxanes than space-qualified silicones. These higher-MW species would be harder to remove by evaporation and could remain on sensitive surfaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".