Enhancements of Mechanical, Thermal Stability, and Tribological Properties by Addition of Functionalized Reduced Graphene Oxide in Epoxy
Bibliographic record
Abstract
The effects of octadecylamine-functionalized reduced graphene oxide (FRGO) on the frictional and wear properties of diglycidylether of bisphenol-A (DGEBA) epoxy are studied using a pin-on-disk tribometer. It was observed that the addition of FRGO significantly improves the tribological, mechanical, and thermal properties of epoxy matrix. Graphene oxide (GO) was functionalized with octadecylamine (ODA), and then reduction of oxygen-containing functional groups was carried out using hydrazine monohydrate. The Raman and x-ray photoelectron spectroscopy studies confirm significant reduction in oxygen-containing functional groups and formation of ODA functionalized reduced GO. The nanocomposites are prepared by adding 0.1, 0.2, 0.5 and 1.0 wt % of FRGO to the epoxy. The addition of FRGO increases by more than an order of magnitude the sliding distance during which the dynamic friction is ≤ 0.1. After this distance, the friction sharply increases to the range of 0.4 - 0.5. We explain the increase in sliding distance during which the friction is low by formation of a transfer film from the nanocomposite to the counterface. The wear rates in the low and high friction regimes are approximately 1.5 x 10-4 mm3/N·m and 5.5 x 10-4 mm3/N·m, respectively. The nanocomposites exhibit a 74 % increase in Young’s modulus with 0.5 wt. % of FRGO, and an increase in glass transition and thermal degradation temperatures.
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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) | 0.000 | 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".