Mechanical, Curing Parameters and Water Absorption of Hybrid Date Palm Leaf-Orange Peel Fibers Reinforced Unsaturated Polyester Composites
Bibliographic record
Abstract
In this study, polymer-hybrid natural fibers composites were prepared using unsaturated polyester resin (UPR) as the matrix and a filler using date palm leaf fiber (DPLF) and orange peel fiber (OPF). The effect of DPLF and OPF on mechanical behavior (tensile strength and elongation at break), moisture absorption, UPR gel time (tgel) and peak exothermic temperature (Tpeak) were determined. The composites of UPR reinforced with DPLF and OPF were processed by hand lay-up technique. The UPR weight fraction was maintained at 90%, and DPLF/OPF proportions varied so that the percentage of natural fiber was 10 wt%. Seven (07) composites were prepared (C1, C2, C3, C4, C5, C6 and C7) with different DPLF:OPF ratios (0:0, 1:0, 0.75:0.25, 0.5:0.5, 0.33:0.67, 0.25:0.75, 0:1) respectively in order to screen the possible interactions. DPLF were surface modified using 6% Alkali treatment, OPF were used without surface modification. Unlike DPLF, OPF showed considerable increase of UPR tgel and Tpeak which act as natural inhibitor. Tensile strength and fracture strength were also impacted negatively and positively depending on the different fiber proportions. Absorption tests showed a decrease in the composites hydrophobicity which increases significantly with higher DPLF proportions.
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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".