Characterization of natural fiber from manau rattan (Calamus manan) as a potential reinforcement for polymer-based composites
Bibliographic record
Abstract
Researches on novel natural fibers in polymer-based composites will help promote the invention of novel reinforcement and expand their possible applications. Herein, in this study, novel cellulosic fibers were extracted from the stem of manau rattan (Calamus manan) by mechanical separation. The chemical, thermal, mechanical and morphological properties of manau rattan fibers were comprehensively analyzed and studied by Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) analysis, thermogravimetric analysis (TGA), single fiber tensile test and scanning electron microscopy (SEM). Component analysis results showed that the cellulose, hemicellulose and lignin contents of C. manan fibers were 42wt%, 20wt%, and 27wt%, respectively. The surface of the rattan fiber was hydrophilic according to the oxygen/carbon ratio of 0.49. The C. manan has a crystalline index of 48.28%, inducing a maximum degradation temperature of 332.8 °C. This reveals that it can be used as a reinforcement for thermoplastic composites whose operating temperature is below 300 °C. The average tensile strength can reach (273.28 ± 52.88) MPa, which is beneficial to improve the mechanical properties of rattan fiber reinforced composites. The SEM images displayed the rough surface of the fiber, which helped to enhance the interfacial adhesion between the fibers and matrices in composites. These results indicate the great potential of C. manan fibers as the reinforcement in polymer-based composites.
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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) | 0.000 | 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".