Effect of poly(ethylene oxide) molecular mass on miscibility and hydrogen bonding with lignin
Bibliographic record
Abstract
Abstract The effect of the molecular mass of poly(ethylene oxide) (PEO) on lignin-PEO blends was studied using thermal analysis and FT-IR. Differential scanning calorimetry (DSC) analysis revealed miscible blends over the entire blend ratio. A negative deviation in T g from a simple weighted average was observed, indicating the existence of relatively weak favorable interactions between blend components. Analysis of the data revealed no difference in the magnitude or propensity of intermolecular interactions with increasing PEO molecular mass in the kraft lignin-PEO blends. By contrast, the fitting parameters obtained for organosolv lignin were substantially different; the higher molecular mass PEO had a higher propensity to form slightly stronger intermolecular inter-actions than the lower molecular mass PEO. Low molecular mass poly(ethylene glycol) dimethylether (M-PEG)-lignin blends had a much higher degree of crystallinity than the PEO blends, resulting in an increase in T g at high PEG content. FT-IR analysis revealed the presence of strong intermolecular hydrogen bonding between lignin and PEO. However, the band shape of the ν OH region of the M-PEG blends was slightly different from the PEO blends; some of the original lignin inter- and intramolecular hydrogen bonding was still present in the M-PEG-lignin 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".