Pine Wood Treated with a Citric Acid and Glycerol Mixture: Biomaterial Performance Improved by a Bio-byproduct
Bibliographic record
Abstract
Wood material is a good reservoir for biogenic carbon storage. The use of wood material for outdoor products such as siding in the building construction sector presents limits. These limits are bound to the nature of wood material (hygroscopic property and anatomical structure). They are responsible for the dimensional variation associated with moisture content variations. Fungal attacks and coating layers adhesion on wood surface, are other problems. This research investigated the feasibility of impregnation with environmentally friendly chemicals, i.e., a citric acid-glycerol mixture (CA-G). The anti-swelling efficiency (ASE), hardness, biodegradation, and coating adhesion tests were performed on softwood specimens. ASE results were up to 53%. The equilibrium moisture content of the treated specimens was less than half of the untreated ones. FTIR spectroscopy showed bands at 1720 to 1750 cm-1, indicating the presence of ester bonds, and scanning electron microscopy images confirmed the polymerization and condensation of treatment solution inside the wood structure. Hardness and decay resistance were increased; however, treatment reduces coating adhesion. In conclusion, CA-G represents a promising eco-responsible solution for improving the technical performance of outdoor wood products.
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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) | 0.001 | 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".