Influence of Unzipped Multiwalled Carbon Nanotube Oxides‐Epoxy Paint on the Corrosion Rate of Mild Steel in Marine Environment
Bibliographic record
Abstract
Abstract In recent years, numerous innovative approaches have emerged to enhance the corrosion resistance of materials. This study investigates the effect of enhancing mild steel corrosion through the incorporation of unzipped multiwalled carbon nanotube oxides (UMCNO) into epoxy resin. Additionally, the effect of various operating parameters, such as temperature, UMCNO concentration, salt concentration, duration of exposure, and coating thickness, have also been considered in the study. The Box–Behnken method was used for experimental design and correlation of corrosion rate with various operating parameters, followed by analysis of variance of both five‐ and three‐parameter models. Notably, despite variations in temperature and salt concentration, the corrosion rate remained negligible, confirming its suitability in various marine conditions. Furthermore, it was observed that the corrosion rate of mild steel coated with epoxy decreased with the addition of UMCNO. A corrosion rate of 0.182 mpy was observed for epoxy resin incorporated with 0.5 % UMCNO over a 14‐day period, which is lower compared to other conditions. Electrochemical impedance spectroscopy and potentiodynamic polarization analysis showed higher corrosion‐resistant properties in epoxy coating incorporated with UMCNO. In addition, it was evident from the contact angle measurement that the corrosion rate of mild steel was highly dependent on the concentration of UMCNO.
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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) | 0.000 | 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".