The effect of morphology and chemical characteristics of cellulose reinforcements on the crystallinity of polylactic acid
Bibliographic record
Abstract
Abstract The aim of this work has been to study the crystallization behavior of composites based on polylactic acid (PLA) and three different types of cellulose reinforcements, viz., microcrystalline cellulose (MCC), cellulose fibers (CFs), and wood flour (WF). The primary interest was to determine how the size, chemical composition, and the surface topography of cellulosic materials affect the crystallization of PLA. The studied composite materials were compounded using a twin‐screw extruder and injection‐molded to test samples. The content of cellulose reinforcements were 25% by weight. The MCC and WF were shown to have a better nucleating ability than CFs based on differential scanning calorimetry and optical microscopy studies. It is difficult to visualize that transcrystallization will occur during melting process and this process is influenced by the morphological and chemical characteristics of the reinforcement. Bulk crystallization seems to be mainly dependent on the processing temperature. The cold crystallization process was shown to improve the thermal stability and storage modulus of the composites. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 300–310, 2006
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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".