Corrosion Resistance Mechanism of Mica–Graphene/Epoxy Composite Coating in CO2-Cl− System
Bibliographic record
Abstract
The working environment for tubing in oil and gas fields is becoming more and more serious due to the exploration of unconventional oil and gas resources, leading to the increasing need for a protective internal coating to be used in tubing. Therefore, a new mica–graphene/epoxy composite coating with different graphene contents (0.0, 0.2, 0.5, 0.7, and 1.0 wt.%) was prepared to improve the tubing resistance to a corrosive medium, an autoclave was used to simulate the working environment, and an electrochemical workstation assisted by three-electrodes was used to study the electrochemical characteristics of the coating. The results showed that the addition of a certain amount of graphene into the mica/epoxy coating significantly improved the corrosion resistance of the composite coating, and when the graphene content increased, the corrosion resistance of the mica/epoxy coating first increased and then decreased when the corrosion current density of a 35 wt.% 800# mica/epoxy coating with a 0.7 wt.% graphene content was the lowest (7.11 × 10−13 A·cm−2), the corrosion potential was the highest (292 mV), the polarization resistance was the largest (3.463 × 109 Ω·cm2), and the corrosion resistance was improved by 89.3% compared to the coating without graphene. Furthermore, the adhesion of the coating with 0.7 wt.% graphene was also the largest (8.81 MPa, increased by 3.4%) and had the smallest diffusion coefficient (1.566 × 107 cm2·s−1, decreased by 76.1%), and the thermal stability improved by 18.6%. Finally, the corrosion resistance mechanism of the composite coating with different graphene contents at different soaking times was revealed based on the electrochemistry and morphology characteristics other than water absorption and contact angle.
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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 | 0.001 | 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) | 0.002 | 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".