Effect of Silicon Carbide on the Mechanical and Thermal Properties of Snake Grass/Sisal Fiber Reinforced Hybrid Epoxy Composites
Bibliographic record
Abstract
In this study, an attempt was made to develop and characterize Snake Grass Fiber (SGF)/Silicon Carbide (SiC)/epoxy and Snake Grass Fiber/Sisal Fiber (SF)/Silicon Carbide/epoxy hybrid composites using a compression moulding technique. Mechanical characteristics of the produced hybrid composites such as tensile, flexural, and hardness tests were analyzed. Also experiments have been carried out to predict the thermal stability of the fabricated composite samples. The interface between fiber and matrix was examined by using Scanning Electron Microscopy (SEM). Among SGF/SiC/epoxy and SGF/SF/SiC/epoxy composites, it has been observed that hybrid composite SGF/SF/SiC/epoxy exhibits the higher hardness of 82 Shore-D, tensile strength of 51 MPa and flexural strength of 73 MPa. In contrast to the mechanical properties, the percentage of water absorption was lower in the SGF/SiC/epoxy hybrid composite. It is proven from the results that the SGF/SF/SiC/epoxy hybrid composites will enhance the strength of the composites. This composite material is also a potential candidate for the hardware of energy devices including electrochemical energy along with Fuel Cell systems.
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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".