Rheology of LDPE‐based semiflexible fiber suspensions
Bibliographic record
Abstract
Abstract Molten LDPE suspensions containing fibers of different flexibilities have been investigated in simple shear and small and large amplitude oscillatory shear (LAOS) flow. The suspensions exhibited viscosity and normal stress overshoots in stress growth experiments, and the magnitude and width of the overshoots became larger as the fiber flexibility increased. LAOS was used to help understanding the relationship between stress growth and fiber orientation. For all composites, the stress signal decreased with time in LAOS, and this behavior was more pronounced in the case of the more rigid fibers. The energy dissipated per LAOS cycle was evaluated for each composite, and it showed that less energy was dissipated as fiber flexibility decreased. In addition, the dissipated energy decreased with time and this has been interpreted in terms of a reduction of fiber contacts. The first normal stress difference showed a nonsinusoidal periodic response, and fast Fourier transform analysis indicated the presence of a first harmonic corresponding to the applied frequency for the fiber‐filled systems, in addition to the second harmonic observed for the neat LDPE. It resulted in asymmetrical strain‐normal force Lissajou curves for the suspensions, with this asymmetry being more pronounced in the case of the more rigid fibers. This has been attributed to a more extensive fiber orientation for the latter. POLYM. COMPOS., 31:1474–1486, 2010. © 2009 Society of Plastics Engineers
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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".