Role of graphene concentration on electrochemical and tribological properties of graphene-poly(methyl methacrylate) composite coatings
Bibliographic record
Abstract
This study aims to investigate the influence of graphene nanoplatelet (GNP) concentration on the electrochemical and tribological properties of GNP-poly(methyl methacrylate) (PMMA) composite coatings. GNP-PMMA coatings were prepared with varying GNP concentrations (0.5, 1.0, 3.0, and 5.0 wt %) using the drop-casting method onto AA6061 aluminum alloy substrates. Results showed that the addition of 1.0 wt % GNP increased the tensile strength of PMMA but further increase reduced the tensile strength and fracture strain of the composites. Permeability studies indicated that 1.0GNP-PMMA had the lowest water vapour transition rate. All GNP-PMMA coatings showed a higher coating resistance and impedance modulus at the lowest frequency compared to neat PMMA with 1.0GNP-PMMA having the highest |Z| 0.01 Hz value in comparison to the composites with higher GNP concentrations. According to Raman mapping, an increase in the concentration of GNP in the composite resulted in the agglomeration of graphene, which caused the debonding of the graphene-PMMA interfaces and also resulted in a higher number of shear fronts and other defects on the fracture surface that reduced barrier properties of graphene. The specific wear rate of 1.0GNP-PMMA was lower than that of neat PMMA, indicating improved wear resistance. The coefficient of friction was lowest for 5.0GNP-PMMA, although this was due to a higher amount of material being transferred to the counterface. Accordingly, optimizing the GNP concentration enables the development of high-performance PMMA coatings with enhanced strength, improved barrier properties, and reduced wear rates, making them well-suited for applications such as corrosion protection and tribological coatings.
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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 | 0.001 |
| 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".