Poly(Ethylene Oxide)/Organosolv Lignin Blends: Relationship between Thermal Properties, Chemical Structure, and Blend Behavior
Bibliographic record
Abstract
Blends of poly(ethylene oxide) with organosolv lignin (Alcell) were prepared by thermal blending. Excellent fiber spinning was achieved over the entire blend ratio. The good thermal properties of the Alcell lignin arise from its unique chemical structure. HMQC 2D NMR analysis revealed the presence of alkoxyl chains at the Cα and Cγ positions of the Alcell lignin side chain structure acting as internal plasticizers and enhancing the thermal mobility of the lignin. The addition of a small amount of Alcell lignin to PEO resulted in an increase of the PEO crystalline domain size. However, both PEO crystallinity and crystalline domain size decreased with lignin incorporation beyond 25 wt %. A negative polymer−polymer interaction energy density “ B ” was calculated on the basis of the melting point depression of PEO and a negative deviation of T g from the weighted average values observed. Good prediction of the T g -composition behavior was obtained indicating the presence of favorable interactions between blend components. FT-IR analysis revealed the formation of a strong hydrogen-bonding system between Alcell lignin and PEO, supporting that hydrogen-bonding interactions are an important factor in the formation of miscible lignin-based polymer blends.
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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".