Performances of Alkaloid Extract from<i>Rauvolfia macrophylla</i>Stapf toward Corrosion Inhibition of C38 Steel in Acidic Media
Bibliographic record
Abstract
Abstract Alkaloid extract from Rauvolfia macrophylla Stapf (AERMS) was studied as the corrosion inhibitor for C38 steel in 1 M HCl and 0.5 M H2SO4 using electrochemistry and surface analysis. The corrosion inhibition was efficient and proceeds via adsorption of AERMS on the steel surface due to the active functional groups present in the molecules. AERMS acts as a mixed inhibitor in HCl and as a cathodic inhibitor in H2SO4. In H2SO4 corrosive medium, the presence of iodides improves the adsorption of the alkaloid molecules by reducing the surface charge of the electrode and thus substantially decreases the corrosion rate. Two pure alkaloids (tetrahydroalastonine (THA) and perakine (PER)) were quantitatively isolated from AERMS, and their anticorrosive properties for C38 steel in 1 M HCl and 0.5 M H2SO4 were evaluated. THA showed the highest efficiency while the performance of PER was less important compared to the extract. This confirms that the efficiency of AERMS was the result of the complementary action of the chemical compounds present in the extract.
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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".