Synergistic Protection of Mild Steel in Sodium Chloride Solutions Using Mixtures of Sustainable Corrosion Inhibitors
Bibliographic record
Abstract
Mild steel material has broad application in marine construction, due to their availability, low cost, and high tensile strength. Nevertheless, the major limitation of mild steel is its low corrosion resistance. The use of eco-friendly inhibitors in protecting mild steel from aggressive media is one of the cheap and practical means competing with conventional phosphate and chromate inhibitors. Herein, the synergism corrosion inhibition effects between key lime and chlorpheniramine drug were examined as sustainable inhibitors for mild steel in 24.5 M NaCl solutions through electrochemical measurement, and morphology of the same was characterized using scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy (SEM/EDX). The electrochemical investigations revealed that in the presence of 2ml key lime and 8ml chlorpheniramine drug the optimum inhibitive efficiency of 81.82% was achieved, this is connected to the protecting layer build-up on the surface of the mild steel, thus, blocking the harsh ion diffusion pathway. The polarization plot shows that the inhibitors complex the deposition led to the corrosion inhibition mixed-type mechanisms. The films formed on the mild steel surface were proved by the SEM/EDX result. The adsorption result shows that the inhibitors fit the Langmuir adsorption isotherm. Keywords: Synergistic effect, sustainable corrosion inhibition, NaCl solution, electrochemical techniques, mild steel
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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".