Effects of Migrating Inhibitors on Corrosion of Reinforcing Steel in Aggressive Environment
Bibliographic record
Abstract
The durability degradation of reinforced concrete structures is one of the biggest problems hindering the breakthrough in engineering and has attracted a great deal of attention in the field of civil engineering.Corrosion issues of internal steel reinforcement have become a top priority in durability treatment since they seriously threaten the durability and sustainability of the structure.Migrating corrosion inhibitors (MCIs) are a good alternative to prevent or control reinforcing steel corrosion because of their moderate cost and easy application compared with other preventive methods.Our study concerns the investigation of the protective effect of MCI; different concentrations of butanol-1 amin-2 refer 1g/L lysine.Materials under investigation are two kind of low allow carbon steel marked as: Steel 39, Steel 44.The corrosion media is sulfuric acid in presence of chloride ions, in form of NaCl (H2SO4 1M + Cl -10 -3 M).Potentiodynamic polarization method is used for inhibitor efficiency testing.Potentiodynamic polarization measurements showed that the presence of MCI in acidic solution decreases the corrosion current to a good extent.Use of this inhibitor in concentration 12 g/L butanol-1 amin-2 with 1g/L lysine, referring the corrosion protection of steel 39 presents protection efficiency 90.52% and for steel 44 present protection efficiency 75.98% classifieds as good for this extreme aggressive 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) | 0.000 | 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".