Study of Organosolv Lignins as Adhesives in Wood Panel Production
Bibliographic record
Abstract
Organosolv lignins obtained from sugar maple bark and wood were studied as adhesives for wood particleboard production. Organosolv pulping of sugar maple wood and bark was carried out in the presence of Lewis acid FeCl3 as a catalyst. The organosolv lignins recovered from this process were investigated by determination of Klason plus acid-soluble lignin content, of sugars by HPLC analysis, and of ash content. Structural characterizations of these lignins were performed by Fourier-transform infrared (FT-IR) and by 31P NMR. The results of the latter studies indicate that the content of free phenolic groups was more important in bark than in wood lignin. The gel permeation chromatography (GPC) analyses results suggested that the weight-average molecular mass of wood lignin was higher than that of bark lignin. The studied organosolv lignins were used for the preparation of particleboards as recovered and in combination with glyoxal or isocyanate. It was found that sugar maple bark lignin, as such or modified with isocyanate, was a more efficient adhesive than its wood counterpart. On the contrary, it was the organosolv wood lignin combined with glyoxal which was a more efficient adhesive than its bark counterpart. In combination with isocyanate, it was the sugar maple bark organosolv lignin which was determined to have the best adhesive performance of all studied lignins.
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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".