Sustainable Corrosion Inhibition of Mild Steel in Hydrochloric Acid Using Extracts of Phaseolus Vulgaris and Vicia Faba
Bibliographic record
Abstract
The inhibitory effects of extracts from Phaseolus vulgaris and Vicia faba beans, obtained from boiled beans, on the corrosion of EN Fe37-3FN mild steel in a 0.5 M hydrochloric acid medium were meticulously investigated utilizing electrochemical techniques and Electrochemical Impedance Spectroscopy (EIS).The corrosion rate was observed to decrease by 40% upon the introduction of 20 mg/L of the Phaseolus vulgaris extract, with a notable reduction of 70% when the concentration was increased to 2 g/L and beyond.The Vicia faba extract, however, displayed a slightly inferior inhibitory performance, with corrosion rates diminishing by 20% at 20 mg/L and 60% at concentrations of 2 g/L and above.The adsorption of extract constituents onto the steel surface was found to conform to the Langmuir adsorption isotherm model, thereby facilitating the calculation of the sorption equilibrium constant and revealing that the adsorption was primarily physical in nature.These extracts, which are typically discarded by-products of bean cooking, have emerged as cost-effective, readily accessible, and environmentally benign alternatives for mitigating steel corrosion in acidic environments.
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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) | 0.000 | 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".