Erosion Wear and Hardness of Glass Fiber / Epoxy with Nano and Micro TiO2 Hybride Composites
Bibliographic record
Abstract
The objective of this research is to prepare and study (Glass Fiber / Epoxy) with nano and micro Titanium dioxide (TiO2) composites with different volume percentage of nano and micro TiO2 powder of 2%, 4%, and 6% volume fraction. Atomic force microscopy techniques was used to measure the grain size of nano TiO2 powder (average diameter was 46 nm) and particle size analyzer techniques was used. Erosive wear behavior of this composite under three different impingement angles of 30°, 60° and 90° and three angular silica sand abrasive particle sizes approximately 425, 600 and 850 μm and hardness (shore D) were studied. In general the erosion wear of micro composites have lower erosion resistance than other nano-based compounds. Erosion resistance increase as the volume fraction increase. Nano composites of GF / EP with TiO2 have many advantages over micro composites from the view point of wear and hardness tests. The worn surface features of unfilled and filled G-E composites were examined using scanning electron microscopy (SEM) and results indicates more severe damage to matrix and glass fiber in unfilled composite system as compared to TiO2 filled composites.
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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".