Tuning the localization of finely dispersed cellulose nanocrystal in poly (lactic acid)/bio‐polyamide11 blends
Bibliographic record
Abstract
ABSTRACT A versatile approach to control the localization of cellulose nanocrystal (CNC) in PLA/PA11 blends is presented. A PEO/CNC mixture with a high level of CNC dispersion is prepared through a combination of high pressure homogenization and freeze‐drying. The prepared PEO/CNC mixture is then incorporated into the PLA/PA11 blends using two different strategies. Typically for CNC/PLA/PA11, the CNCs selectively localize in PA11. However, PEO‐coated CNC particles segregate into PLA irrespective of whether the PEO/CNC mixture is premixed with PLA or PA11. It is suggested that a strong interaction between PEO and CNC particles combined with the PLA/PEO miscibility facilitates the localization of PEO‐coated CNC in the PLA. The localization of PEO‐coated CNC in the PLA has no effect on the morphology of the PLA‐5PEO/PA11 with matrix/dispersed phase form. However, 2 wt % PEO‐coated CNC in the co‐continuous (PLA‐5PEO)/PA11 50/50 vol % blend diminishes the phase thickness from 11 ± 1 to 4 ± 1.5 μm. This is attributed to a retarded relaxation of the PLA phase. This work outlines a strategy to control the CNC localization into a given polymeric phase in a binary polymer–polymer mixture. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2018, 56, 576–587
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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".