Examination of water uptake performance and mechanical properties of PLA/flax fiber biocomposites with the coupling agent
Bibliographic record
Abstract
The paper reports, the effect of water sorption on the microstructural and flexural properties of the flax fiber/ polylactic acid (PLA) biocomposites compared to the composites with maleic anhydride (MAH) as coupling agents and alkali treatment. In the current study, five different biocomposites which are 15 % wt. flax/PLA, 25 % wt. flax/PLA as control group and15 % wt. flax/PLA, 25 % wt. flax /PLA, and 35% wt. flax / PLA with 5 % wt. MAH was produced. Ten different soaking times were studied to understand the water absorption behavior of the biocomposites. To investigate mechanical properties of the biocomposites impact test was applied on the dry and 750 h, 1850 h water sorption composites. A three-point bending test was performed on the dry and 1850 h water sorption biocomposites to determine flexural properties. Short flax fiber-reinforced PLA matrix biocomposites were compounded using extrusion and manufactured by injection molding. Flax fiber surface was treated using sodium hydroxy solution to advance the interface interaction between fiber-matrix and surface performance of the fiber and matrix. According to the results, alkali treatment improved the water gain resistance of the composites since its enhancement of the interfacial bonding. Alkali-treated composites with maleic anhydride showed the better impact and flexural strength than composites without alkali-treated after 1850 h water sorption.
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 | 0.000 | 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".