Development of Visibly Opaque Polyolefin Sheets While Preserving Infrared-Light Transparency
Bibliographic record
Abstract
, FeO yellow, FeO light brown, and FeO dark brown-were each mixed with polyethylene (PE) wax in a 1:1 ratio and blended with LLDPE at concentrations of 1, 3, and 5 wt%. Tensile strength tests showed minimal changes at lower pigment concentrations, with values near that of pure LLDPE (14 MPa), and slight reductions at 5 wt%. IR transparency tests, conducted using both direct and reflected heat sources, showed that white-pigmented sheets maintained over 85% transparency, while colored pigments exhibited slightly reduced IR transmittance, ranging from 70% to 91%. Fourier Transform Infrared Spectroscopy (FTIR) analysis confirmed that the critical IR transparency range of 8-12 μm remained unaffected with both pure and pigmented sheets. On the other hand, ultraviolet-visible (UV-VIS) testing showed that white-pigmented sheets experienced enhanced visible-light absorption with increasing pigment concentration and thickness, while color-pigmented sheets exhibited high opacity. Additionally, micro-structuring was performed on the LLDPE sheets to further modify their IR properties, which resulted in effective scattering of IR radiation. These findings highlight the potential of pigmented LLDPE sheets for applications requiring both visual opacity and IR transparency, such as thermal management and camouflage, as well as applications requiring tunable IR properties.
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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".