Layer-by-Layer Assembly of a Self-Healing Anticorrosion Coating on Magnesium Alloys
Bibliographic record
Abstract
Fabrication of self-healing anticorrosion coatings has attracted attention as it has the ability to extend the service life and prevent the substrate from corrosive attack. However, a coating system with a rapid self-healing ability and an improved corrosion resistance is rarely reported. In this work, we developed a self-healing anticorrosion coating on a magnesium alloy (AZ31). The coating comprises a cerium-based conversion layer, a graphene oxide layer, and a branched poly(ethylene imine) (PEI)/poly(acrylic acid) (PAA) multilayer. We incorporated the graphene oxide as corrosion inhibitors and used the PEI/PAA multilayers to provide the self-healing ability to the coating systems. X-ray diffraction (XRD) and Raman spectroscopy were used to characterize the composition of the multilayers, and scanning electron microscopy (SEM) was used to analyze the surface morphology. The electrochemical impedance spectroscopy (EIS) results illustrate the improved corrosion resistance of the coating. The proposed coating also has a rapid self-healing ability in the presence of water.
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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".