Plasma modification of wood to improve the performance of clear coatings
Bibliographic record
Abstract
Wood is susceptible to photodegradation, particularly by ultraviolet (UV) light which triggers photochemical reactions in wood up to 100 µm from the surface. Painting may protect the wood from sunlight, however, consumers prefer clear-coatings because they reveal wood’s grain. Unfortunately, clear finishes on wood used outdoors fail within 1-2 years because sunlight transmitted through the clear-coating degrades the underlying wood. I hypothesized that the performance of clear-coatings on wood used outdoors would be improved if the clear-coating could penetrate deeply into the wood and bond with sub-surface layers of wood that are less affected by UV light. Glow discharge plasma derived from water was used to etch and open up flow paths at the surface of the refractory wood species, black spruce. Image analysis was used to assess the penetration of two polyurethane clear-coatings into wood, and the adhesion (pull-off) of clear-coatings to unweathered wood was measured. A second experiment examined the changes in color, gloss and integrity of clear-coats on plasma treated and coated specimens subjected to artificial accelerated weathering. Plasma treatment of black spruce for 20 min significantly increased the penetration of clear coatings into sub-surface layers of wood (50-75 µm). However, the adhesion of the polyurethane clear coatings on wood depended much more on coating type (adhesion of the water-borne was better than that of the oil-borne coating). Plasma pre-treatments decreased film failure of coatings on flat-sawn specimens exposed to artificial weathering, but increased the surface yellowing of coated wood specimens. The oil-borne polyurethane performed better in terms of three performance criteria (redness, yellowness and gloss), while the water-borne was superior in terms of changes in lightness. I conclude that plasma treatment can increase the penetration of coatings into wood if the plasma can etch the wood and open up important flow-paths. However, plasma treatments have no positive effect on the adhesion of polyurethane coatings to black spruce wood and adhesion is influenced more by the type of coating. Finally, plasma treatments can have positive and negative effects on the exterior performance of coatings on wood depending on the criteria used to assess performance.
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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".