Effectiveness of bark extracts and CeO2 nano particles as coating additives for the protection of heat-treated jack pine
Bibliographic record
Abstract
High temperature heat-treatment of wood for wood preservation is more beneficial compared to chemical treatment. There are several advantages of heat-treated wood compared to kiln dried wood due to the chemical modifications during high temperature heat-treatment. Heat-treated woods have improved dimensional stability, improved thermal insulating properties, and improved biological resistance. Also high temperature heat-treatment changes wood color to a dark brown color which is very important for decorative purposes. But unfortunately this color is not stable and it changes to grey or white depending on the wood species during weathering. Protection of heat-treated wood against discoloration due to weathering is the main objective of this study without changing its natural appearance. For this purpose waterborne acrylic polyurethane base was chosen for their high durability against weathering and non toxic nature. Since heat-treated wood is green product minimal use of chemicals during coating formulation was another very important factor. For this reason natural antioxidants were extracted from barks which are easily biodegradable and the source is renewable in nature. Also CeO2 nano particles were also used alone or together with lignin stabilizer to achieve a better protection against weathering on heat-treated jack pine. The protective characteristics of these coatings are compared with highly pigmented industrial coating under accelerated weathering condition. The results showed better protection of these acrylic polyurethane coatings compared to commercially available coatings used in this study. The chemical modifications during accelerated weathering of these coated heat-treated wood surface was monitored by XPS analysis and the morphological changes took place during weathering was studied by fluorescence microscope analysis.
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) | 0.001 | 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".