Studies on<i>Rhoeo discolor</i>plant extract as a natural corrosion inhibitor
Bibliographic record
Abstract
The use of plant extracts as natural corrosion inhibitors is gaining prominence as they contain different organic compounds adsorbed on the surface of metals. Studies were carried out on the ethanolic extract of the abundantly available Rhoeo discolor leaves, commonly known as boat lily, as it is a natural inhibitor in hydrochloric acid (HCl) (0.1–0.5 M) on mild steel. R. discolor plant extract is biodegradable, cheap and readily available in nature. Inhibition efficiencies were obtained from weight loss, potentiodynamic polarisation and electrochemical impedance spectroscopic study at 30°C. A scanning electron microscopy report confirms the adsorption of the inhibitor on mild steel. Fourier transform infrared spectroscopy and gas chromatography and mass spectroscopy reports support the presence of functional groups in the inhibitor. The effect of inhibitor dosage from 0.1 to 0.5 g/l is investigated at 30°C. Corrosion inhibition efficiency for mild steel in 0.1 M of hydrochloric acid and 0.5 g/l of inhibitor is found to be maximum at 94.6% estimated by the weight loss method. The potentiodynamic polarisation method indicates 85.28%, and it stands at 88.60% by the impedance method. Results suggest that R. discolor acts as a good inhibitor of the corrosion process on 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) | 0.000 | 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".