Long-term water aging of composites with bamboo fiber and montmorillonite filler
Bibliographic record
Abstract
This article examines the degradation mechanisms and mechanical properties of unfilled and montmorillonite-filled bamboo fiber-composites, under long-term water aging (up to 120 days) at room temperature. The main findings show that the water absorption and hydrolysis processes simultaneously occur during aging. The presence of clay fillers (3 wt %) reduces the water absorption content of composites from 18.69% to 15.68% after 120 aging days. However, their moisture absorption rate increases after adding particles. Scanning electron microscope images confirm the development of structural damage induced by the extra-swelling of particles, enabling more water absorption after long-term aging. Interestingly, the unfilled composites lose about 18.7% and 17.94% of their initial tensile strength and modulus after 120 aging days, which is attributed to the swelling/plasticization mechanisms and the chemical degradation at the interface, respectively. However, the tensile strength and modulus of montmorillonite-filled composites drop by 35.78% and 32.47%, respectively. This poor resilience to aging is attributed to the development of microstructural damage. The results of this work provide a clear insight into the long-term mechanical properties of composite materials and shed light on their systematic aging mechanisms, with a view to their application in harsh environments.
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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".