Novel Inhibitors Containing Multi-Functional Groups for Pipeline Corrosion Inhibition in Oilfield Formation Water
Bibliographic record
Abstract
In this work, two novel inhibitors containing multi-functional groups were developed, and their inhibition performance for corrosion of X65 pipeline steel in CO2-saturated oilfield formation water was investigated using electrochemical measurements, surface characterization, and scanning vibrating electrode technique. Moreover, the inhibitors and the inhibition mechanism were further investigated by electrochemical quartz crystal microbalance and x-ray photoelectron spectroscopy. The inhibitors are able to decrease the corrosion rate of the steel in the solution by over 100 times at a concentration of 3.2 × 10−3 M, and the inhibition efficiency can exceed 98% for each inhibitor. The inhibitors are mixed-type ones, reducing both anodic and cathodic current densities, while the corrosion potential remains essentially constant. Corrosion inhibition is attributed to the formation, on the surface, of a film that consists of an insoluble complex containing inhibitor film and ferrous scale. The corrosion inhibition of the added inhibitor is more effective for the corroded electrode than for the freshly prepared steel electrode. This is attributed to the formation of the complex film by reactions of the inhibitor molecules and the pre-formed corrosion scale, and this film is more effective than the adsorptive inhibitor film for corrosion inhibition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".