Multi-function Tuneable Emittance Smart Coatings for Thermal Control in Harsh Space Environment
Bibliographic record
Abstract
MPB has developed advanced smart radiator devices (SRDs) for passive, dynamic thermal control of space structures and payloads. The SRDs employ a nano-engineered, integrated thin-film structure based on V1-x-yMxNyOn. Dopants, M and N, tailor the transition temperature characteristics of the tuneable IR emittance. This paper describes the progress in MPB's smart thermal radiator towards its validation as an efficient thermal control device for space environment. A set of environmental tests were performed in order to validate the coating resistance and performance stability in space. The tests included random vibration, thermal shock, and accelerated aging. In addition, the thermo-optic characteristics after exposure to Atomic Oxygen (AO) in a simulated LEO environment were similar to the “as deposited” characteristics. Preliminary radiation tests, comparable to 3 years in a GEO environment, indicate very low change in emissivity and solar absorptance relative to the initial values. The experimental environmental test results indicate no significant change in the emittance tuneability,morphology or mass of the SRD coating. MPB's passive thin-film SRD can be applied to Al thermal radiators as a direct replacement for the existing OSR (optical second-reflector) radiator tiles with a net added mass under 100 gm/m2 but with the added benefits of dynamic variation in the thermal radiation to space to significantly improve the thermal stability of the spacecraft for varying operating conditions at a significant mass and power savings relative to traditional techniques.
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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) | 0.003 | 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".