Corrosion inhibition of mild steel in 1 M HCl by sweet melon peel extract
Bibliographic record
Abstract
Corrosion inhibition of mild steel by sweet melon (Cucumis melo L) peel (SM) extract in 1 M HCl solution was evaluated by weight loss and potentiodynamic polarization methods. Various SM extracts concentrations such as 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5 g/l were added and corrosion rate (CR) of mild steel and inhibition efficiency (IE) were determined at various temperatures from 295 to 333 K. The appreciable decrease in CR with increase in SM extract concentration was observed at each temperature. However, the typically accelerated CR at each SM extract with the rise in temperature corresponded to the increased kinetic activities at the metal/electrolyte interface. By the addition of 0.5 g/l SM extract, ∼5 times lower CR of mild steel at high temperature (333 K) than in blank acidic solution confirmed its strong inhibitive efficacy. The relatively large variation in the anodic Tafel slope and progressive decrease in CR with an increase in the SM extract concentration validated the restricted dissolution of mild steel. The barrier characteristics of the SM extract layer and its chemical interaction with the surface was evaluated from the low activation energy (Ea) values that fluctuated from ∼20 to 23 kJ/mole. The increase in kad increased from 0.602 to 1.053 (g/l)−1 and decrease in ΔG°ad (−3.74 to −4.91 kJ/mole) with an increase in temperature from 295 to 333 K assured the spontaneous interaction of SM extract molecules with the steel surface.
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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".