Effects of Bamboo Fiber Length and Loading on Mechanical, Thermal and Pulverization Properties of Phenolic Foam Composites
Bibliographic record
Abstract
In order to improve the mechanical properties and toughness of phenolic foams, a reinforcement method using two kinds of bamboo fibers was optimized with respect to the fiber contents. The compressive and flexural properties, thermal stability, friability and morphology of the phenolic foam composites were studied. The mechanical properties of the pristine foam and composites were evaluated by measuring the compressive strength. The results showed that the greatest mechanical properties were achieved by incorporating 2.5wt% of the reinforcement, and the compressive and flexural strengths of the two composites increased by 26.21% and 24.35%, respectively, compared with that of the pristine foam. The results of thermogravimetric testing demonstrated that the addition of bamboo fiber imparted better thermal stability to the phenolic foam, which was mainly attributed to the higher initial thermal decomposition temperature of the bamboo fiber. However, the influences of both reinforcements on the thermal stability of the material were negligible. The incorporation of bamboo fiber decreased the friability of the phenolic foam. Furthermore, the reduction in friability of the foam composites with longer lengths were higher than that in foams with shorter bamboo fibers. Moreover, the morphology and cell sizes of the fiber-reinforced phenolic foams were analyzed by scanning electron microscopy, the results indicated strong bonding between the fibers and phenolic matrix, and the incorporation of the bamboo fibers into the foam resulted in increased cell size of the material. Finally, the thermal conductivity and flame resistance of the phenolic foams reinforced by the bamboo fibers were also measured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".