Corrosion Mechanism of A333 and API 5L in 0.02 M Acidic Crude Oil Using Sodium Sulfite- Ginger Extract as Inhibitor at Different Temperature and Fluid Velocity of the Medium
Bibliographic record
Abstract
A333 and API 5L steel are typical metals used in oil and gas industries.Techniques of gravimetric method, linear polarization resistance, and EIS measurements have been used to examine the synergistic corrosion inhibition performance of sodium sulfite, SS, and ginger extract, GE, on aforementioned metal dissolution in 0.02 M acidic crude oil in pipelines at various temperatures, fluid velocities which is new parameter in term of corrosion studies and could be considered as novel study, and inhibitor concentrations.Moreover, the combination of SS and GE and using them as a synergistic corrosion inhibitor in the acidic crude oil medium could be considered as a novelty.The results remarkably reveal enchantment of SS-GE inhibitory by mitigating metal attack of anodic area by providing polarization to become cathodic surface.Effect of temperature and fluid velocity on the thermodynamic adsorption equilibrium was evaluated and obeys Freundlich isotherm at all studied temperature.Unsignificant temperature effect can be seen.
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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) | 0.001 | 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".