Coating Performance on Oil-heat Treated Wood for Flooring
Bibliographic record
Abstract
Thermal modification of wood in a hot-oil bath is a green process, which improves wood properties using natural products. The process imparts a uniform brown color to the wood and increases its dimensional stability. The improved properties create value-added opportunities for some wood species to be used for high performance applications such as flooring products. This study focused on the optimization of the oil-heat-treatment process to find different approaches for saving energy and also evaluating the performance of water-based coatings on oil-heat treated wood. Effects of process variables on development of wood drying defects such as, checking, cupping, crooking, bowing, twisting, and grain raise were evaluated. This included investigation of effects of Initial wood moisture content and delayed cooling of treated wood in an oven or under a thermal blanket. Our results showed that wood can be treated at an initial moisture content around 8 to 10 percent and cooled in a blanket instead of in an oven without increasing wood defects. Testing the performance of four commercially formulated water-based coatings on heat-treated wood showed that the coatings had an overall better color retention, abrasion, and scratch resistance on the heat-treated wood than on the untreated wood. However, the adhesion of all of the coatings was lower on the heat-treated wood when compared with untreated wood samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".