Application of AC-DC-AC Accelerated Aging to Assess the Galvanic Corrosion Risk of Mild Steel Coated with Graphene-Embedded Epoxy Coatings
Bibliographic record
Abstract
This study presents a novel approach to evaluate the galvanic corrosion risk of mild steel coated with graphene-embedded epoxy coatings. The potential for graphene platelets to promote anodic dissolution of the underlying steel substrate via galvanic corrosion mechanisms was systematically assessed through the accelerated alternating current-direct current-alternating current (AC-DC-AC) technique and cathodic disbondment testing. The possible risk of displacing cathodic reactions from the coating–steel interface to the dispersed graphene platelets within the epoxy matrix was investigated by evaluating the degradation trend of the graphene-containing coating under the AC-DC-AC test. The degradation behaviour of both pure epoxy and graphene-embedded epoxy coatings during accelerated aging was characterized using electrochemical impedance spectroscopy (EIS) measurements. The finding highlighted the negligible catalytic effect of incorporated graphene platelets on the anodic dissolution of steel substrate. On the other hand, as an inert filler, graphene platelets contributed to the enhancement of the structural integrity of the epoxy matrix during the AC-DC-AC test and natural immersion in NaCl 3.5 wt % solution by enhancing the barrier performance of the coating. Despite their spectacular barrier performance, damaged graphene-containing coatings performed inferiorly against corrosion-induced delamination compared to pure epoxy. Samples underwent the cathodic disbondment test to eliminate the effect of substrate anodic dissolution from corrosion-induced delamination. The accelerated delamination of graphene-embedded epoxy coatings was attributed to the destructive impact of graphene platelets on the interfacial adhesion of the epoxy matrix to the steel substrate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".