Enhanced protective properties and UV stability of epoxy/graphene nanocomposite coating on stainless steel
Bibliographic record
Abstract
Epoxy-Graphene (E/G) nanocomposites with different loading of graphene were prepared via in situ prepolymerization and evaluated as protective coating for Stainless Steel 304 (SS304). The prepolymer composites were spin coated on SS304 substrates and thermally cured. Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM) were utilized to examine the dispersion of graphene in the epoxy matrix. Epoxy and E/G nanocomposites were characterized using X-ray diffraction (XRD) and Fourier Transform Infrared (FTIR) techniques and the thermal behavior of the prepared coatings is analyzed using Thermogravimetric analysis (TGA) and Differential scanning calorimetry (DSC). The corrosion protection properties of the prepared coatings were evaluated using Electrochemical Impedance Spectroscopy (EIS) and Cyclic Voltammetry (CV) measurements. In addition to corrosion mitigation properties, the long-term adhesion performance of the coatings was evaluated by measuring the adhesion of the coatings to the SS304 substrate after 60 days of exposure to 3.5 wt% NaCl medium. The effects of graphene loading on the impact resistance, flexibility, and UV stability of the coating are analyzed and discussed. SEM was utilized to evaluate post adhesion and UV stability results. The results indicate that very low graphene loading up to 0.5 wt % significantly enhances the corrosion protection, UV stability, and impact resistance of epoxy 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 | 0.000 |
| 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".