Integrated Thin-Film Smart Coatings with Dynamically-Tunable Thermo-Optical Characteristics
Bibliographic record
Abstract
This paper presents experimental results regarding a new approach to smart radiator devices (SRD) employing a smart, integrated thin-film structure based on V1-x-yMxNyOn that can be applied to existing thermal blankets such as Kapton or to thermal radiators such as Al. The smart coating facilitates thermal control by dynamically modifying the thermo-optic characteristics of the underlying substrate in response to the ambient temperature and/or a control voltage. This methodology has significant advantages over competitive technologies in terms of weight, cost, structural simplicity, and integration with the space structure. The effective emissivity of the film/substrate structure can be reduced dynamically by changing the behavior of the smart coating from insulator to metallic. High quality VO2 films have been prepared using a hybrid reactive laser ablation technique. These films can exhibit high transmittance in the insulating state, exceeding 75% from the NIR to beyond 12 μm, and correspondingly high reflectance in the metallic state. Additional dopants, M and N, are employed to tailor the metal-insulator transition characteristics, providing control of the transition temperature, slope and hysteresis.
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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 | 0.001 | 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".