Lignin Caprolactone-Derived Wood Coatings
Bibliographic record
Abstract
The variation in the characteristics of lignin can influence its potential use for coating applications. In this work, we examined the polymerization of three lignin types, i.e., birch alkaline (BL), wheat straw alkaline (WL), and softwood kraft lignin (SL), with caprolactone (CL) before and after ethanol fractionation. The molecular weight, hydroxyl group, and thermal stability of lignin were significantly altered after ethanol fractionation, affecting its reactivity to ring-opening polymerization with CL and, consequently, the characteristics of the final polymers. Due to more significant changes in the functional groups of SL and WL compared to BL, the polymerization of SL and WL with CL was affected more intensely than that of BL with CL. The wood coating performance (e.g., water contact angle (WCA) and flame-retardant properties) of fractionated lignin-CL polymers was superior to that of their unfractionated counterparts. The wheat straw fractionated lignin (WL E )-CL polymer (WL E P) exhibited a superior WCA (125°) and a limiting oxygen index (27.5%) to other lignin-CL polymers, which were also stable after sand abrasion and knifing. The superior coating performance of WL E P on wood surfaces is attributable to the heightened reactivity of WL E with CL and an increased polymerization of PCL on the lignin backbone. The preliminary cost estimation and robustness of a proposed process confirmed windows of opportunity for fabricating lignin-CL polymers to replace oil-derived polymers for coating applications.
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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".