Surface modification of lignin for applications in polypropylene blends
Bibliographic record
Abstract
ABSTRACT The surface modification of wet‐milled softwood lignin produced with the LignoForce System was successfully carried out in a one‐step aqueous process. Different hydrophobic molecules, including cetyl trimethyl ammonium bromide, poly(ethylene oxide), polyethylene‐block‐poly(ethylene glycol), dodecenyl succinic anhydride, and alkyl ketene dimer (AKD), were investigated to design the hydrophobicity of lignin with the objective of improving the adhesion and compatibility in polymer blends composed of polar lignin particles and, for example, nonpolar polypropylene (PP). AKD, among all of the investigated approaches, proved to be the simplest and most effective for significantly increasing the contact angle of lignin while preserving the original micrometer size of wet‐milled, spray‐dried lignin particles. This treatment led to a noticeable improvement in the stiffness of lignin–PP composite blends, with an increase of approximately 15% in Young's modulus. The compatibility of the AKD‐treated lignin with PP was assessed through tensile strength measurements and blend morphology observation, whereas the mechanism of AKD interaction with lignin was investigated with contact angle measurement, differential scanning calorimetry, Fourier transform infrared spectroscopy, and 1H‐NMR spectroscopy measurements. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45103.
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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".