Isotherm moisture absorption kinetics in natural-fiber-reinforced polymer under immersion conditions
Bibliographic record
Abstract
In natural-fiber-reinforced polymer, absorption of water or moisture is a significant issue in maintaining strength and stiffness. To enhance the understanding of water and moisture sorption behavior, the kinetics of moisture sorption in natural-fiber-reinforced polymers are investigated under immersion conditions. Samples of hemp-bast-fiber-reinforced polyethylene are prepared using an injection molding technique at different hemp fiber volume fractions (v f ). The samples are then immersed in water for 274 days. Moisture content and uptake rate are analyzed at different fiber volume factions and matrix crystallinity percentages. A simplified two-dimensional contraction model is developed to investigate the contraction effect on the moisture uptake; it shows that a matrix with high crystallinity has more stiffness contraction on the reinforcing natural fibers, which limits the maximum amount of the absorbed moisture. The Fickian diffusion is found to be the dominant mechanism, shifting toward pseudo-Fickian or anomalous diffusion depending on the natural fiber volume fraction and the crystallinity percentages of the matrix. The natural-fiber-reinforced polymers diffusivity is evaluated and modeled to characterize the ability of liquid molecules to diffuse into these composites at different hemp fiber volume fractions. Both the crystallinity percentage of the matrix material and the volume fraction of the reinforcing fibers were found to interactively affect the sorption kinetics of the tested natural-fiber-reinforced polymers.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".