Lignin as a Key Component in Lignin-Containing Cellulose Nanofibrils for Enhancing the Performance of Polymeric Diphenylmethane Diisocyanate Wood Adhesives
Bibliographic record
Abstract
Lignin-containing cellulose nanofibrils (LCNFs), an emerging family of nature-based nanomaterials, have been successfully applied in many commercial polymer systems to enhance their properties and functional performance. In particular, LCNFs were used in polymeric diphenylmethane diisocyanate (pMDI) wood adhesives as a functional compound to significantly reinforce bonding properties. In order to elucidate the mechanisms for the interactions between lignin in LCNFs and pMDI wood adhesive, 13 C– 1 H HSQC 2D-NMR experiments were carried out on dissolved ball-milled LCNFs to reveal the molecular–structural characteristics of lignin and polysaccharides in LCNFs and identify hydroxyl groups contained in these biomolecules that can be involved in reactions with the pMDI model compound. Moreover, a lignin-free CNF was prepared by chlorite treatment as a control group to illustrate the role of the lignin component in affecting the resulting adhesives. It was found that lignin was the integral component in LCNFs that is responsible for the increased reactivity as shown by curing kinetics, the improved thermal stability as shown by TGA, and the higher compatibility with nonpolar reagents as shown by lap-shear tests. The fundamental knowledge gained from this study allowed us to better understand the function of the lignin component as a distinctive feature of LCNFs, and insights into the role of lignin in improving pMDI wood adhesive performance would be highly beneficial for applying LCNFs as a novel sustainable nano-bio-reinforcing agent for a wider range of polymeric systems.
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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) | 0.000 | 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 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".