Wood terpenes as bio-based monomers in latex for sustainable coatings
Bibliographic record
Abstract
Recent global market disruptions, including the COVID-19 crisis, inflation, and oil crises, have highlighted the need for industries to reduce dependence on petrochemicals. However, the coating industry remains reliant on petrochemicals due to a lack of knowledge about local and sustainable alternatives. This study explored the potential of wood extractives as precursors for producing high-quality wood coatings. Terpenes were modified through acrylation, and bio-based latexes were synthesized from these modified terpenes. Analysis showed that all tested latexes had conversion levels above 88.5%. The bio-based films were characterized, and their transparency, measured by ultraviolet-visible spectroscopy, exceeded 80%. The good incorporation of bio-based monomers in the latex films was confirmed by thermogravimetric analysis and pyrolysis-gas chromatography-mass spectrometry. Comparative analysis between bio-based and conventional latexes showed equivalent results in particle size, molecular weight, glass transition temperature, and minimum film formation temperature. However, bio-based films exhibited lower hardness. The study suggests that using monomers derived from wood extractives offers a viable alternative to petrochemicals, utilizing abundant forest residues. This approach could address raw material shortages and help make the coatings industry more sustainable by reducing its reliance on petrochemicals.
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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".