Protection of Mild Steel against Corrosion Using Mixed Inhibitor Combinations
Bibliographic record
Abstract
Abstract Under controlled conditions of heat and mass transfer, the inhibitive characteristics of mixed inhibitor combinations on mild steel have been investigated. To achieve such control under turbulent flow, a rotating cylinder electrode system was used to provide quantified hydrodynamics, mass and heat transfer conditions. Potentiostatic polarization experiments were carried out in both the inhibited and uninhibited brackish water solutions, under isothermal and heat transfer conditions. Under isothermal conditions, the limiting current density values of oxygen reduction in the brackish water followed Eisenberg equation. The presence of heat transfer enhanced the oxygen transfer rate over and above the value under isothermal conditions. The corrosion and passivation potentials, and the passive current density values were a complex function of temperature, flow rate and heat transfer. The anodic current density values increased with the increase in temperature, flow rate and the presence of heat transfer. The anodic dissolution kinetics were activation polarization controlled affected by temperature. In this investigation, the inhibition of mild steel against corrosion is believed to be due to the oxide and complex films formed on the metal surface. The mixed inhibitor combination showed high protection efficiency under the studied heat and mass transfer conditions.
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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) | 0.000 | 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".