Tuning the localization of finely dispersed cellulose nanocrystal in poly (lactic acid)/bio‐polyamide11 blends
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it