Compare between flax shive and extracted flax shive reinforced PP composites
Bibliographic record
Abstract
Flax shive (FS), the woody part composing of over 70 wt% of the flax straw, is a waste product left after removing the fibers from flax straw. Although the FS is supposed to have lower strength than that of flax fiber, however, its low cost and greater availability could be advantages for the production of low cost composites. Recently, FS has been extracted using environmentally benign processes and thus hemicelluloses and lignin were effectively removed from FS. In this work, FS and extracted flax shive (EFS) were characterized and further used as reinforcing materials for polypropylene (PP) composite. The effect of cellulose content on the composite properties, such as thermal stability and mechanical properties was examined. Compatibilizer was added to get the best compatibility between the FS and the PP matrix. It was shown that EFS present better thermal stability than FS because of its lower flammable noncellulose ratio. FS appears to behave as filler for composite even with compatibilizer. However, with the presence of coupling agent E43, EFS could be upgraded to be a reinforcing material for PP composites. With 30 % EFS, the tensile strength and the modulus of composite can increase almost 8% and 100% over than PP, respectively.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".