Effect of fiber surface treatment of poultry feather fibers on the properties of their polymer matrix composites
Bibliographic record
Abstract
Abstract This study develops the enabling technology needed to transform the fibers of poultry feather (FPF), a waste product left over after processing poultry in the food processing industry, as reinforcement filler material for manufacturing composite materials. We successfully fabricated composite materials from biopolymers (polylactide, PLA) and FPF that were produced by the extruder system. FPF‐reinforced polypropylene (PP) composites were also compounded and molded and compared to PLA/FPF composite. The composites were evaluated via thermal and mechanical analysis. To enhance the adhesion between the polymer matrix and the FPF, the FPF have been treated with sodium hydroxide, 10% maleinized polybutadiene rubber, and a silane‐coupling agent. X‐ray photoelectron spectroscopy was used to analyze the influence of modifications on the properties of fibers and found that the coupling agent was localized at the surface of the fibers. Thermal behavior of pretreated fibers was also studied by thermogravimetric analysis. All treatments clearly enhanced thermal performance of fibers. This enhancement of fiber properties, along with an improvement in fiber/matrix adhesion, led to improvement in the mechanical properties of the composite materials. It was found that the surface‐treated fiber‐reinforced materials offered superior mechanical properties compared to untreated fiber‐reinforced composite materials. Moreover, morphological studies by the scanning electron microscopy demonstrated that better adhesion between the fiber and the matrix was achieved especially for the surface‐treated fiber‐reinforced composite materials. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
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".