Optimization and Characterization of Borassus Fiber-Reinforced Epoxy Composites with Caesalpinia Bonducella Seed Shell Powder Using Response Surface Methodology
Bibliographic record
Abstract
Utilizing reinforced lignocellulosic fibers in polymer matrix composites (PMCs) is a highly effective approach, since it reduces the necessity for more often used synthetic fibers.To investigate the uses of particulate matrix composites (PMCs), we concentrated on studying the fibers from Borassus, which are underutilized and have not been well researched.In this study, we used particles of Caesalpinia bonducella seed shell powder (CBSSP) and Borassus fibers (BF) as a reinforcing agent.The research aimed to assess the effectiveness of different CBSSP and Borassus fibers (BF) through treatment with 5% NaOH.The use of alkali treatment to CBSSP and BF samples significantly improved the compatibility between the biomaterial's characteristics and the natural fillers in the epoxy-BF composites.This enhancement was noted in the findings of physicochemical, XRD, FTIR, thermal and morphological analysis.In response surface analysis, a first-degree polynomial model was employed to maximize tensile strength (TS), tensile modulus (TM), flexural strength (FS), impact energy (IE), and moisture absorption of reinforced fiber.The optimization was done by considering the composition and length of the fiber.The Response Surface methodology (RSM) numerical model was used to analyze the mentioned characteristics and develop an ideal Epoxy-BF composite with minimum moisture absorption, maximum tensile modulus, flexural strength, and impact energy.After analyzing the data, it was concluded that the most effective setup for the Epoxy-BF composite is to use a 4 mm fiber length reinforcement combined with a loading reinforcement of 25 wt % of biomaterials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".