Characterization of Dwarf Palm Leaf Extract (DPLE) (Chamaerops Humilis L. Extract) as an Eco-Friendly Corrosion Inhibitor for Carbon XC70 Steel in a 3.5% NaCl Solution
Bibliographic record
Abstract
In the present work dwarf palm leaf extract (DPLE) has been prepared to be used as a corrosion inhibitor on carbon steel in a saline medium.The inhibitory efficiency of DPLE on the corrosion rate of carbon XC70 steel (CS) in a 3.5% NaCl solution has been studied by means of weight loss measurements, potentiodynamic polarization curves, electrochemical impedance spectroscopy, SEM and AFM microscopies.The results showed that the corrosion inhibition rate of carbon XC70 steel in the NaCl solution increases with the concentration of DPLE, and reaches up to 90% at 2.0•10 -4 g.L -1 as the optimum concentration of DPLE.The inhibiting performance against corrosion was attributed to the formation a DPLE barrier that reduces the contact area between the carbon XC70 steel and the corrosive solution.The EIS analysis revealed that the presence of DPLE was found to decrease the double layer capacitance, with an increasing charge transfer resistance.The morphological analysis showed that upon adding DPLE in saline solution, the surface morphology of the metal becomes smoother due to the formation a protective layer adsorbed on the metal surface.This study showed that dwarf palm leaf extract acts as an efficient and eco-friendly inhibitor on carbon steel in saline medium.
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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".