RETRACTED: Effect of TiO<sub>2</sub> Nanofillers on the Mechanical and Abrasive Wear Properties of Epoxy Reinforced with Carbon Fabric Hybrid Composites
Post-publication record
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Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Recent studies show that nanofillers greatly contribute to the increase in the mechanical and abrasive behaviors of the polymer composite. In the current study, epoxy composites were made by hand lay-up with the reinforcement of carbon fabric and titanium dioxide (TiO 2 ) nanoparticles as secondary reinforcement in weight percentages of 0.5, 1.0, and 2.0. Hardness, tensile, and abrasive wear tests have been carried out for the fabricated composites. The obtained results confirm that as the percentage of filler addition increases, hardness of the carbon epoxy (CE) composite increases, and significant enhancement of 10.25% hardness is confirmed in 2 wt % nano TiO 2 -added CE composite. The CE composite filled with 2 wt % of TiO 2 nanofiller shows 15.77 and 9.15% improvement of tensile strength and modulus, respectively, compared to unfilled CE composites. The abrasive wear volume exhibits a nearly linear increasing trend as the abrading distance increases. In addition, it is discovered that the abrasive wear volume is greater for higher applied loads. The inclusion of nano TiO 2 reduced the wear loss in the CE composite for all abrading distances, regardless of the load, low or high. The scanning electron microscopy analysis of worn surfaces was carried out to analyze the contribution of the filler to improve the wear resistance.
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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) | 0.002 | 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".