Evaluation of the Inhibition Efficiency of a Green Inhibitor on Corrosion of Cu-Ni Alloys in the Marine Application
Bibliographic record
Abstract
The aim of this article describes the application of green tea aqueous extract as an eco-friendly corrosion inhibitor for two Cu-Ni alloys in 3.5% NaCl solution. This ability has been studied by using electrochemical techniques (i.e. PDP, CT and EIS), IR spectroscopy measurements and the surface analysis technique (i.e. SEM/EDX). This ability was compared with it's of a commercial cooling water (green water). The results show that tested extract exhibited a good ability to decrease the corrosion rate of alloys in 3.5% NaCl solution.The inhibition efficiency of the green water and green tea extract inhibitors increased with increasing the concentration and decreased with increasing the temperature. The inhibition efficiency of two Cu-Ni alloys which reaching ̴ 91.5% and ̴ 93.9% with 50 % green tea aqueous extract for Cu-10 Ni and Cu-30 Ni alloy, respectively. Electrochemical impedance showed that the change in charge transfer resistance (R ct ) and double layer capacity (C dl ) which adsorbed on the alloy surface. Adsorption of the inhibitors gives a good fit to Langmuir isotherm model. Some thermodynamic parameters of activation and adsorption processes were also determined and discussed. Surface examination studies by SEM and EDX confirm the presence of protective film on the alloy surface. In the present study, we investigated the corrosion of the Cu-Ni (cupronickel) alloys in 3.5 % NaCl environment to simulate the seawater desalination plants conditions. Therefore, the future studies can be focused on the development of polymeric compounds used as self- healing or production of new natural corrosion inhibitors especially recommended the waste product of green tea.
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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".