Controlling lignin particle size for polymer blend applications
Bibliographic record
Abstract
ABSTRACT Softwood lignin produced by the LignoForce SystemTM was physically processed using different milling approaches to ascertain effective and scalable means to yield micro to submicron particles of controllable and uniform size. Our work suggests that wet ball‐milling using a small milling medium is the most reliable method in terms of processing efficiency and particle‐size controllability. Controllable particle size reduction would permit lignin to be used as an effective filler in polymer blends. We show that wet‐milled lignin could, subsequently, be oven‐ or spray‐dried, and, subsequently, blended with, for instance, polypropylene (PP) through co‐extrusion. The spray‐drying method produced spherical lignin aggregates smaller and more uniform than oven‐dried ones. As a consequence, spray‐dried lignin demonstrated a more uniform distribution within the polymer melt, leading to noticeable improvement in the strain—or flexibility—of the lignin‐PP polymer blends. Furthermore, it is confirmed that the investigated drying methods had no effect on the thermal stability of the resulting lignin‐PP blends. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44669.
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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) | 0.002 | 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".