Investigation of the thermostability of poly(ethylene terephthalate)–hemp fiber composites: Extending natural fiber reinforcements to high‐melting thermoplastics
Bibliographic record
Abstract
ABSTRACT The thermal stability of poly(ethylene terephthalate) reinforced with 1, 5, 10, 15, and 20% hemp fibers was investigated with the aim of extending the applications of biocomposites to high‐melting thermoplastics. The material was injection‐molded following compounding with a torque‐based Rheomix at 240, 250, and 260°C. A combination of thermogravimetric methods at 5, 10, and 20°C/min, Liu and Yu's collecting temperature method, and Friedman's kinetic method were used for testing and analysis. A significant thermostability for all formulations was observed below 300°C; this demonstrated their potential for successful melt processing. Moreover, two degradation steps were observed in the temperature ranges 313–390 and 390–490°C. The associated apparent activation energies within the temperature ranges were determined as 150–262 and 182–242 kJ/mol, respectively. We found that the thermostability was significantly affected by the heating rates; however, the effect of the temperature of the mixing chamber was negligible. These findings suggest that the successful melt processing of high‐melting thermoplastics reinforced with natural fibers is possible with limited fiber thermodegradation. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42500.
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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".