Experimental evaluation and optimization of kenaf-coir based hybrid composite incorporated with titanium carbide nano-fillers
Bibliographic record
Abstract
In the current decade, a number of industries have moved their attention towards emerging sustainable technologies in order to better support socio-economic and environmental considerations. The present research investigates a unique hybrid composite developed by the amalgamation of natural kenaf-coir fibers, with resin of epoxy, incorporated with titanium carbide (TiC) nanoparticles. This study also presents the development process involved in manufacturing the composites, along with mechanical testing and optimization of these composite samples. The nanofillers of TiC are utilized in wt. percentages of 0%, 3%, 4%, and 5%, while coir and kenaf fibers are incorporated at 0%, 3%, 4%, and 5% by weight, and the thickness of the samples is varied at 2, 3, 4, and 5mm. The mechanical attributes of composites are evaluated using a vacuum bag molding process, with subsequent testing and optimization performed through Taguchi and ANOVA analysis to discover the optimal sample combination. The findings indicate that the most effective composite formulation includes 4% TiC, 5% kenaf, 5% coir, and a thickness of 3 mm, which provides the highest tensile modulus and strength among all tested samples. The integration of kenaf fibers with coir fibers and TiCs as fillers significantly improves the tensile and flexural attributes of the hybrid composite in contrast to composites made with coir or kenaf fibers alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".