Corrosion and Corrosion Inhibition of Mild Steel in H2SO4 Solutions by Zizyphus Spina-Christi as Green Inhibitor
Bibliographic record
Abstract
The corrosion and corrosion inhibition of mild steel in 1.0 M H2SO4 containing 10% ethyl alcohol(EtOH) by Zizyphus Spina-Christi (ZSC) extracts (aqueous extract and alcoholic extract) has been studied using chemical techniques( hydrogen evolution(HE) and mass loss(ML)) and electrochemical techniques(electrochemical impedance spectroscopy(EIS) and potentiodynamic polarization(PDP)). The effect of acid concentration on the corrosion rate of mild steel increases with acid concentration (0.25-1.5) M showing first order corrosion reaction without changing the reaction mechanism, and the results showed that when the concentration of ZSP extracts (aqueous extract and alcoholic extract) increased the rate of steel corrosion is decreased, which indicates that the inhibition of the corrosion process is produced. Electrochemical impedance spectroscopy results showed that the corrosion and corrosion inhibition of steel occurred mainly by charge transfer. The electrochemical results of polarization also showed that the extracts of ZSC plant act as mixed type inhibitors, they retarded both cathodic and anodic reaction. The experimental results from chemical and electrochemical studies were fit Langmuir isotherm. Values of equilibrium constant of adsorption Kads and the standard free energy of adsorption , for the extracts are calculated.
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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".