Surface energy modification by radiofrequency inductive and capacitive plasmas at low pressures on sugar maple: an exploratory study.
Bibliographic record
Abstract
The wood products industry is going through hard times in both Canada and the US. It is faced with competition from emerging economies and substitution products. The North American economy is slowing down with decreasing demand for wood products. Under these conditions, the industry should be innovative and develop the next generation of wood products. Plasma technology could be used to improve wood surface properties and compensate for the variations to be expected from an organic living material, which is sensitive to its environment (moisture, water, temperature, ultraviolet light). In recent years, the plastic and textile industries have begun experimenting with plasma technology to activate surfaces, mainly to improve coating/substrate adhesion. The literature on potential applications of plasma treatment to wood surfaces is very limited. This report describes the results of an exploratory study on the effect of plasma treatments on sugar maple wood using different gases and mixtures (N2, H2, O2, and Ar) at different pressures (13.3-665 Pa). Water wettability and adhesion between surface and waterborne polyurethane acrylate coatings were also studied. The results show that it was possible, under certain conditions, to significantly increase wood/coating adhesion by 30-100%. This improvement is correlated with improvements in wood surface energy and coating penetration depth. In addition, chemical analyses showed that, with some plasma types, the treatment led to new atoms being grafted.
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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".