Transparent Wood Fabrication and Applications: A Review
Bibliographic record
Abstract
Wood cellulose is an abundant bio-based resource with diverse applications in construction, cosmetics, packaging, and the pulp and paper industries. Transparent wood (TW) is a novel, high-quality wood material with several advantages over traditional transparent materials (e.g., glass and plastic). These benefits include renewability, UV shielding, lightweight properties, low thermal expansion, reduced glare, and improved mechanical strength. TW has significant potential for various applications, including transparent roofs, windows, home lighting structures, electronic devices, home decoration, solar cells, packaging, smart packaging materials, and other high-value-added products. The mechanical properties of TW, such as tensile strength and optical transmittance, are typically up to 500 MPa (Young's modulus of 50 GPa) and 10-90%, respectively. Fabrication methods, wood types, and processing conditions significantly influence the mechanical and optical properties of TW. In addition, recent research has highlighted the feasibility of TW and large-scale production, making it an emerging research topic for future exploration. This review attempted to provide recent and updated manufacturing methods of TW as well as current and future applications. In particular, the effects of structural modification through various chemical pretreatment methods and impregnation methods using various polymers on the properties of TW biocomposites were also reviewed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".