Effect of Vacuum Time, Formulation, and Nanoparticles on Properties of Surface-Densified Wood Products
Bibliographic record
Abstract
Surface-densified wood products were prepared with only a short vacuum impregnation process instead of the traditional time-consuming pressurizing stage.The top layer of engineered wood flooring planks was successfully impregnated with low-viscosity 1,6 hexanediol dimethacrylate and trimethylolpropane trimethacrylate as well as layered silicate nanoparticles by vacuum impregnation of 30 s to 10 min.Treating tests involved two species, maple and oak, and Brinell surface hardness, impact resistance, and abrasion resistance of the treated wood specimens were measured.Brinell surface hardness increased from 5.05-15.42MPa for maple, the greatest improvement of 205% being obtained with a 30-s vacuum.For oak, Brinell surface hardness increased from 5.25-11.05MPa with a 60-s vacuum, an improvement of 108%.Impact resistance was based on measurements of indentation diameters and depths in falling ball tests.Decreases in indentation diameters from 4.96-2.84mm and indentation depths from 0.172-0.034mm were observed for maple treated with nanoparticle-containing formulations and a 60-s vacuum impregnation, indicating that impact resistance of a one-step, short vacuum impregnation time dramatically improved wood surface hardness.Measurements of abrasion resistance properties of surfacedensified specimens were based on specimen weight loss with time following abrasion tests.Weight loss values decreased considerably with treated wood.A factorial experimental design provided information on effects of vacuum time, nanoparticles, and wood species on properties of impregnated wood specimens.Impacts of individual factors and their interactions were analyzed with Statistical Analysis System.
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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".