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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".