Hardness of chemically densified Yellow birch in relation to wood density, polymer content and polymer properties
Bibliographic record
Abstract
Abstract Density of wood can be increased by filling its porous structure with polymers. Such densification processes aim to increase hardness of wood and are particularly interesting for flooring applications. This study aims to evaluate efficiency of different polymers for chemical densification based on the polymer properties. Yellow birch (Betula alleghaniensis Britt.) was chemically densified with seven monomer mixtures through acrylate monomer impregnation and electron beam in-situ polymerization. Chemical retention and polymer content of densified woods were recorded. Hardness of treated and untreated Yellow birch was measured and compared to hardness of Jatoba (Hymenaea courbaril L.). All densified woods showed higher or comparable hardness to Jatoba. Hardness of densified wood was analyzed in relation to initial density of wood and polymer content of the material using multivariable linear mixed models. Efficiency of polymers for chemical densification was evaluated through effect of polymer content on hardness with interaction coefficients. Polymer films corresponding to monomer impregnating mixtures were prepared through low energy electron beam and characterized by their glass transition temperature, micro hardness, indentation modulus and crosslinking density. Polymers showed statistically significantly different efficiencies and were separated in two main groups. Overall, polymer efficiency increased with increasing glass transition temperature of polyacrylates.
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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".