Cellulose microfibers (CMFs) reinforced smart self-healing polymeric composite coatings for corrosion protection of steel
Bibliographic record
Abstract
The use of organic coating for the metals has been widely being used to protect the surface against corrosion. Polymeric coating incorporated with Nanocontainers loaded with inhibitor and self-healing provides better corrosion resistance. Cellulose microfibers (CMFs) used as smart carriers were synthesized and loaded with dodecylamine (DOC)-inhibitor and polyethyleneimine (PEI)-both inhibitor and self-healing agents. Smart polymeric coatings were developed by mixing CMF/DOC and CMFs/PEI into the epoxy matrix. Reference coatings (that has only CMFs) were also prepared for a compersion. Scanning electron microscope (SEM), X-ray diffraction spectroscopy (XRD), Fourier transform infrared spectroscopy (FTIR) and thermal gravitational analysis (TGA) were used to confirm the loading of DOC and PEI onto the CMFs. UV-vis analysis indicates that the self-release of inhibitor from CMFs is sensitive to pH of the solution and the immersion time. Recovery of controlled surface damage confirms the decent self-healing ability of the prepared smart coatings is due to the efficient release of inhibitor (DOC) and self-healing agent (PEI) in the damaged area leading to the formation of a protective film. Electrochemical impedance spectroscopy (EIS) results demonstrate that corrosion resistance of the smart coating increases with an increase in immersion time which is due to the progressive release of inhibitors from CMFs in response to the pH change. Therefore, smart coatings demonstrate superior properties as compared to the reference coatings. The study reveals the polymeric composite coatings have potential to inhibit the corrosion of steel for oil and gas industry.
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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".