Tailoring the molecular and thermo–mechanical properties of kraft lignin by ultrafiltration
Bibliographic record
Abstract
ABSTRACT This study has shown that ultrafiltration allows the selective extraction from industrial black liquors of lignin fraction with specific thermo‐mechanical properties, which can be matched to the intended end uses. Ultrafiltration resulted in the efficient fractionation of kraft lignin according to its molecular weight, with an accumulation of sulfur‐containing compounds in the low‐molecular weight fractions. The obtained lignin samples had a varying quantities of functional groups, which correlated with their molecular weight with decreased molecular size, the lignin fractions had a higher amount of phenolic hydroxyl groups and fewer aliphatic hydroxyl groups. Depending on the molecular weight, glass‐transition temperatures ( T g ) between 70 and 170°C were obtained for lignin samples isolated from the same batch of black liquor, a tendency confirmed by two independent methods, DSC, and dynamic rheology (DMA). The Fox–Flory equation adequately described the relationship between the number average molecular masses ( M n ) and T g 's‐irrespective of the method applied. DMA showed that low‐molecular‐weight lignin exhibits a good flow behavior as well as high‐temperature crosslinking capability. Unfractionated and high molecular weight lignin ( M w >5 kDa), on the other hand, do not soften sufficiently and may require additional modifications for use in thermal processings where melt‐flow is required as the first step. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131 , 40799.
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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".