High-temperature creep behavior of wheat straw isotactic/impact-modified polypropylene composites
Bibliographic record
Abstract
Short-term creep behavior of wheat straw composites made with either polypropylene homopolymer or impact-modified copolymer was studied at elevated temperatures (50–90°C). Various mineral fillers were tried to enhance creep resistance in the composite materials among which wollastonite was found to be the most effective one. All formulations exhibited higher creep deformations at higher temperatures where the effect of temperature was more pronounced for composites containing the impact-modified copolymer as the matrix. Short-term creep was successfully modeled using a three-parameter power law and Burgers model, and the dependence of the power law model parameters on temperature was evaluated to quantify temperature dependence of creep behavior. Long-term creep at 50°C was predicted based on the short-term data using the time–temperature superposition (TTS) principle, the power law model, and the Burgers model, and the results were compared with actual long-term data. While TTS was found to be the best method to accurately extrapolate short-term data, the failure of the two other methods was found to be related to the time scale of the deformation in short-term creep tests. The results of this study will provide a basis for the design of natural fiber thermoplastic composites to be used in under the hood applications in auto industry.
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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".