Carrot (Daucus Carota L.) Peels Extract as an Herbal Corrosion Inhibitor for Mild Steel in 1M HCl Solution
Bibliographic record
Abstract
The inhibition of corrosion on mild steel in 1M HCl solution was evaluated by utilizing carrot (Daucus carota L.) peels (CP) extract. Study performed by gravimetric and Potentiodynamic polarization techniques. Various concentrations of CP extracts ranging from 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5 (v/v) were used and corrosion rate (CR) on mild steel and inhibition efficiency (IE) were investigated at three temperatures (298K, 308K, and 323K). Corrosion rate increase with the increase in temperature. As inhibitor concentration increases, corrosion rate decreases and IE decreases at elevated temperature. The substantial reduction in CR with the increase in the concentration of CP extract was noted at studied temperatures. However, the increase in the CR at each CP extract along with the increase in the temperature tallied to the increase in kinetic activities at the electrolyte and metal interface. Results show that with the increase of 0.5 g/l CP extract, about 3 times lower CR of mild steel at studied temperatures than in pure 1M HCl solution affirm its robust inhibitive efficiency. Comparatively large change in the anodic Tafel slope and gradual decline in CR with an increase in the CP extract concentration confirmed the restricted dissolution of mild steel. Surface examination suggest that a layer of inhibitor material adsorbed on the surface of mild steel at low temperature is responsible for high IE and this phenomenon is characterized as chemisorption. Weight loss data used to test three well known adsorption isotherm Langmuir, Temkin and Freundlich models and found that data is fitted well to all the models to certain extent however Freundlich Isotherm is found to be best fitted with as the correlation coefficient (R2) values reaching to unity, which showed the applicability of the models to the process.
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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) | 0.001 | 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".