Polymer Nanocomposite Coatings for CO2 Pipeline Corrosion Control: A Comprehensive Review
Bibliographic record
Abstract
Carbon dioxide (CO2) is the most significant greenhouse gas, accounting for 77% of global warming and is produced by the combustion of fossil fuels in industries. Carbon capture, storage and utilization (CCUS) is a possible pathway in achieving the emission reduction target set by the Canadian government in 2050. The transportation of the captured CO2 to storage is a critical factor in the CCUS process, which is frequently hindered by corrosion. The impurities in CO2 lead to corrosion risks, which are generally addressed using inhibitors, corrosion-resistant alloys, and polymer coatings in the oil and gas sector. However, CO2 corrosion is more complex than CO2 sweet corrosion. It is difficult to obtain a single inhibitor capable of mitigating CO2 corrosion in pipelines, and corrosion-resistant alloys are too expensive to be used throughout all sections of the pipeline. Polymers are employed as coatings. For gaseous and supercritical CO2, which leads to defects in the coatings, such as blisters and porosity. As a result, researchers have focused on using nanocomposite coatings to control CO2 corrosion. This review paper focused on the interactions of CO2 with impurities on polymer and polymer nanocomposites. In particular, the most commonly used clay and graphene polymer nanocomposites coatings and their interactions with CO2 were discussed. Further, the transport properties of CO2 through polymers and polymer nanocomposites and the interaction mechanism were analyzed. The paper concludes with the processing methods used for the polymer and polymer nanocomposite coatings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".