Effect of Advanced Chemical Treatments on the Tensile and Bending Properties of Date Palm Composites
Bibliographic record
Abstract
The contemporary focus on lignocellulosic waste valorization has triggered a transformative shift towards the utilization of biocomposite materials.This study endeavours to determine the impact of advanced chemical treatments on the properties (mechanical and physical) of composites based on date palm fibres (DPF).The investigation involves the application of soda (NaOH) and silane coupling agent (SCA) treatments, followed by IR spectroscopy analysis.The experimental framework includes the incorporation of 5 and 25% of treated and untreated fibres into polyvinyl chloride (PVC) composites, which are subsequently subjected to both tensile and 3-point bending deformations.Notably, the findings reveal a substantial improvement in mechanical properties when comparing Young's modulus of untreated fibres with those treated with SCA and NaOH with 7.2%, 75.37% and 37.04% in 5% filler content to the neat matrix.Similarly, in 25% filler content FTS composites showed 156.77%, 141.17% and 96.66% improvement respectively, similar results were found in bending modulus.This outcome confirms the potential of incorporating chemically treated date palm fibres to enhance the mechanical performance of composite materials.The advancements achieved through SCA and NaOH treatments present opportunities for the development of biocomposites with superior mechanical attributes.Such enhanced materials hold promise for a myriad of industrial applications, marking a significant stride towards sustainable and innovative solutions in the realm of lignocellulosic waste valorisation.
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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".