Biobased Phenol Formaldehyde Resins Derived from Beetle-Infested Pine Barks—Structure and Composition
Bibliographic record
Abstract
In this study, two types of biobased bark-derived phenol formaldehyde (PF) resins, namely, liquefied bark-PF and bark extractive-PF, were synthesized from acid-catalyzed phenol-liquefied bark and bark alkaline extractives, respectively. The biobased resins were characterized for their chemical compositions and molecular structures using the liquid-state 13 C nuclear magnetic resonance (NMR) technique. The results indicated that the introduction of bark components (either as liquefied bark or as bark extractives) to the phenolic resin synthesis affected resin structures and curing performance. Methylene ether bridges were found in the bark-derived PF resins. Bark components made the formation of para–ortho -methylene linkage more favorable in bark-derived PF resins than in lab PF resins. Molecular structures of the liquefied bark-PF resin differed significantly from those of the bark extractive-PF resins. The liquefied bark-PF resin showed a higher ratio of para–para/ortho–para -methylene link (−CH 2 −), a higher unsubstituted/substituted hydrogen (−H/–CH 2 OH) ratio and a higher methylol/methylene (−CH 2 OH/–CH 2 −) ratio than the bark extractive-PF resin. The tannin components of the bark extractives accelerated the curing rate of the resulting bark extractive-PF resin. The bark extractives made the ortho position of phenol react more favorably with formaldehyde than the para position. The liquefied bark with phenolated structures had more reactive sites toward formaldehyde than the bark extractives and accelerated the curing rate of the resulting liquefied bark-PF resin.
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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".