Comparative Study of Carbonaceous and Polymer Conductive Additives on Anticorrosion Performance of Epoxy Zinc-Rich Primer
Bibliographic record
Abstract
Zinc-rich primers are among the most promising organic coating systems for improving the corrosion resistance of metals in the marine environment. However, the high zinc content results in poor coating adhesion, high cost, insecurity and pollution. To decrease the zinc dust content, the carbonaceous and polymer conductive additives carbon black (CB), conductive graphite (CG), multiwalled carbon nanotubes (MWCNT) and polyaniline (PANI) were introduced to partially replace the zinc dust in the primers. A comparative study of the anticorrosion performance of epoxy zinc-rich primer (ZRP) is presented herein to systematically discuss and elaborate on the effects of the different conductive additives. There were no blisters, rust or corrosion products presented on the coatings of the CB-modified series due to the good dispersion and conductivity of nanosized CB clusters, while the zinc corrosion products covered the surface of the MWCNT-modified series samples, which was attributed to the excessive electrical conductivity resulting in high consumption of zinc powder. The lamellar CG provided an additional blocking barrier for the coatings based on the maze effect. The transition from the intrinsic state to the doped state of PANI resulted in corrosion protection for the coatings depending on the cathodic and barrier function. The experimental results suggested that the formula with 2 wt.% CB and 67 wt.% zinc dust had the most promising anticorrosion properties, which was also demonstrated by the high Rct and low CPEdl values calculated according to the equivalent electrical circuit analyses.
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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".