Incorporating graphene-modified mica and conductive nickel particles for enhanced corrosion resistance in epoxy zinc-rich coatings
Bibliographic record
Abstract
Epoxy zinc-rich coatings usually require high zinc content to ensure its anti-corrosion performance. However, excessive zinc powder content will reduce the mechanical properties of the coating, increase the economic cost, harm the environment, etc. Therefore, this paper aims to reduce the amount of zinc powder and improve the corrosion performance of epoxy zinc-rich coatings by introducing two kinds of conductive particle materials, conductive graphene-mica powder and conductive nickel. Conductive graphene was first loaded on mica powder and the obtained conductive graphene-mica powder and the conductive nickel were introduced to the epoxy zinc-rich coatings to partially replace zinc component. The anti-corrosion properties of the coating were systematically evaluated by EIS and salt spray test. The resulting epoxy zinc-rich coating with nickel powder or conductive graphene-mica demonstrates outstanding salt spray resistance, lasting up to 2,000 h, exhibiting superior anti-corrosion performance at reduced zinc content of 60% or 45% compared to conventional coatings with 70% pure zinc powder. This study introduces a novel conductive mica material and investigates conductive metal nickel additive, effectively reducing zinc content in epoxy zinc-rich coatings, which offers valuable insights for developing high-performance anti-corrosion coatings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".