EFFECT OF FIBERS ALKALI TREATMENT ON THERMAL BEHAVIOUR OF CURAUÁ/ POLYESTER COMPOSITES
Bibliographic record
Abstract
The interaction between natural fibers and polymeric matrices can be the main disadvantage to expansion of natural fibers in industry application.Alkali treatments are usually applied on natural fibers to remove the lignin and extractives improving the adhesion between the reinforcement and matrix, and consequently, the thermal properties.The curauá, a typical plant from Amazon region, becomes very attractive since it presents high cellulose content, low density, and high strength.The primary objective of this work is to evaluate the thermal behavior and cure parameters of the curauá/ polyester composites through thermal analysis.The untreated curauá fibers and alkali treated by solutions of KOH 10% (w/v) or NaOH 5% (w/v) were mixed to polyester resin and a hardener (2 phr).Further, the curauá/ polyester composites were fabricated by hand lay-up method with fiber fraction of 10 or 20 wt% and cured for 24 hours.Thermogravimetry and its derivative curves (TG-DTG) and differential scanning calorimetry (DSC) analysis were used to determine composites thermal behavior as well its glass transition temperature (Tg).TG-DTG analysis revealed that the curauá treated with NaOH 20 wt%/ polyester composite presented the higher thermal stability compared to the neat matrix and other composites.Besides, the composites with both treated fibers presented higher Tg than the neat matrix, increasing with fiber content.The composite reinforced with curauá treated with NaOH 10 wt% shows the higher change in Tg, increasing to 11.3 °C.The DSC analysis showed that the second exothermic peak that appears in the curves is associated with the cure of composite between 117-119 °C.Finally, the composites with fibers treated with alkaline presented better thermal stability than composite reinforced with untreated fibers.
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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".