Investigating and Predicting the Effects of Fiber Chemical Composition and Treatment on the Mechanical Properties of Natural Fiber Composites by Response Surface Method
Bibliographic record
Abstract
This paper aims to integrate the response surface methodologies to investigate the effect of the cellulose and hemicellulose contents on the mechanical properties of the polypropylene-based composites with green fiber reinforcement conditions.In this study, the tested data are collected from various literature resources demonstrating the green fiber type, the chemical treatment condition, and the resultant mechanical properties, i.e., the tensile modulus and tensile strength.Accordingly, the response surface analysis is utilized to obtain a high-accuracy first order regression model to formulate the influence of the cellulose, hemicellulose, and the treatment condition on the tensile characteristics of the green composite.The results showed that the biocomposites samples with higher cellulose and lower hemicellulose percentages have significantly better tensile modulus properties.However, such samples would have lower tensile strength qualities.Additionally, the presence of chemical treatment can significantly improve the tensile properties of the polypropylene-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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".