Design of micro‐ and macro‐scale polymeric metamaterial solutions for passive and active thermal camouflaging applications
Bibliographic record
Abstract
Abstract This work utilizes predictive modeling techniques to guide and inform metamaterial design for heat management solutions and thermal radiation control. Specifically, micro‐ and macro‐scale polyethylene‐based solutions are proposed for passive and active thermal camouflage. A micro‐scale post design is proposed for highly‐tunable infrared emissivity based on varying unit cell geometrical configurations. Actively modulating these micro‐features through lateral straining of up to 3% allows for redshifting the emissivity spectrum by up to 0.5 µm. Macro‐scale lenticular lens designs allow for a more passive form of camouflage due to its emissive stability for a range of configurations (e.g., single and sandwiched structures, increasing lens radii and height). Overall, the proposed metamaterial designs allow the tailoring of optical properties to improve thermal radiating performance.
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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) | 0.001 | 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".