Effects of polymer‐grafted natural nanocrystals on the structure and mechanical properties of poly(lactic acid): A case of cellulose whisker‐<i>graft</i>‐polycaprolactone
Bibliographic record
Abstract
Abstract In this work, polysaccharide nanocrystals—rodlike cellulose whiskers (CWs)—were surface‐grafted with polycaprolactone (PCL) via microwave‐assisted ring‐opening polymerization, and filaceous cellulose whisker‐graft‐polycaprolactone (CW‐g‐PCL) nanoparticles were produced. Moreover, the resultant nanoparticles were incorporated into poly(lactic acid) (PLA) as a matrix, and they showed superior function for enhancing the mechanical performance of PLA‐based materials in comparison with platelet‐like nanoparticles of starch nanocrystal‐graft‐PCL. The optimal loading level of CW‐g‐PCL was 8 wt %, and this resulted in simultaneous enhancements of the strength and elongation of approximately 1.9‐ and 10.7‐fold, respectively, over those of the neat PLA material. In this case, the rigid CW nanoparticles contributed to the endurance of higher stress, whereas the grafted PCL chains improved the association between the PLA matrix and the CW‐g‐PCL filler and hence facilitated the transfer of stress to the rigid CW nanoparticles. Furthermore, such a fully biodegradable PLA‐based nanocomposite shows great potential for environmentally friendly materials because of its high mechanical performance. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
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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".