Mathematical Modelling of the Interfacial Adhesion of Date Palm/Epoxy
Bibliographic record
Abstract
In recent years, high interests has emerged to use natural fibres as alternative reinforcements synthetic due to its unique benefits regarding renewability, recyclability, degradability, lightweight, and low cost. Recent investigations revealed that the mechanical performance of fibre reinforced polymer composites (FRPCs) is predicated mainly on the interfacial adhesion of fibre with the matrices. In the current work, an empirical approach was exploited to develop mathematical models using linear regression routines available in SPSS IBM program. Such models are established to determine the functional interrelations between, each of the fibres diameters and the percentage of chemical treatment, as independent or response variables, and the interfacial bonding between the DPF and Epoxy resin. Both single fibre tensile testing (SFTT) and single fibre fragmentation testing (SFFT) are considered to study the interfacial adhesion of fibre with matrix and to reflect the real loading conditions. Such testing procedures are carried out for Date Palm Fibres (DPFs) and Date palm fibre reinforced Epoxy composites (DPFEs) with different fibre diameter (0.3-0.7 mm) under different NaOH concentrations (0-9 wt.%). Experimental testing results indicated that the optimum interfacial adhesion and strength of the fibre can be achieved with small fibre diameter when 6 wt. % NaOH concentrations is employed. The use of higher NaOH concentration generally leads to deterioration in the fibre strength. Developed models, on one hand, proved to have the capability to qualitatively and quantitatively grasp the true relationships and, on the other hand, to emphasize the high potential to utilize natural fibres as a replacement of synthetic fibres with affirmation taking into consideration the role of diameter size and chemical treatment of fibres to reach the optimum mechanical behaviour of NFRPCs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".