Insulative Biobased Glaze from Wood Laminates Obtained by Self‐Adhesion
Bibliographic record
Abstract
Abstract The combination of optical transparency and mechanical strength is a highly desirable attribute of wood‐based glazing materials. However, such properties are typically obtained by impregnation of the highly anisotropic wood with index‐matching fossil‐based polymers. In addition, the presence of hydrophilic cellulose leads to a limited water resistance. Herein, this work reports on an adhesive‐free lamination that uses oxidation and densification to produce transparent all‐biobased glazes. The latter are produced from multilayered structures, free of adhesives or filling polymers, simultaneously displaying high optical clarity and mechanical strength, in both dry and wet conditions. Specifically, high values of optical transmittance (≈85.4%), clarity (≈20% with low haze) at a thickness of ≈0.3 mm, and highly isotropic mechanical strength and water resistance (wet strength of ≈128.25 MPa) are obtained for insulative glazes exhibiting low thermal conductivity (0.27 W m−1 K−1, almost four times lower than glass). The proposed strategy results in materials that are systematically tested, with the leading effects of self‐adhesion induced by oxidation rationalized by ab initio molecular dynamics simulation. Overall, this work demonstrates wood‐derived materials as promising solutions for energy‐efficient and sustainable glazing applications.
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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".