Synthesis and Performance Evaluation of Multialkylated Aromatic Amide Oligomeric Surfactants as Corrosion Inhibitor/Drag Reducing Agents for Natural Gas Pipeline
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Drag reducing agents (DRAs) including amphiphiles and polymers can enhance energy efficiency and transmission volume in natural gas pipelines. However, the correlation between DRA molecular structure and drag reduction efficiency remains unclear. In this paper, the multialkylated aromatic amides (MAA) oligomeric surfactants with different numbers of amide group/ n -dodecane chain (from 1 to 3) were first synthesized and characterized. Then, the potential efficiency of MAA as the corrosion inhibitors (CIs)/DRAs for natural gas pipelines was investigated by interfacial activity analysis, film-forming property test, electrochemical polarization curve measurement, and in-door loop test. The results showed that the molecular structure is the key factor influencing the performance of MAA. By increasing the number of amide group/ n -dodecane chains, the interfacial activity of MAA improves greatly, thus outstandingly affecting the film-forming property of the MAA on the carbon steel sheet. For MAA-1, the formed film is too thin to cover the surface roughness, and then the corrosion inhibiting (78.64%)/drag reducing (0–2%) rates are the lowest. For MAA-3, the formed film is the thickest and smooth, thus imparting the largest corrosion inhibiting (93.88%)/drag reducing (10–14%) rates to MAA-3. For MAA-2, the formed film is thicker but not smooth, and the corrosion inhibiting (89.88%)/drag reducing (4–6%) rates are intermediate. We speculate that the structure of the MAA greatly influences the adsorption and self-assembly of MAA on the inner pipe wall and then generates different performances. This work is helpful for guiding the development of natural gas DRAs with high efficiency.
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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".