Covalent organic framework (COF)-enhanced carbon hollow sphere (CS): a novel nano-porous material for robust epoxy composite coating intelligent corrosion protection
Bibliographic record
Abstract
In this research, carbon hollow spheres (CSs) were synthesized by carbonizing polysaccharide shells deposited on silica spheres as hard templates to obtain graphenic carbon shells. Melamine-based covalent organic framework (COF) 3D nanostructures (NSs) were synthesized onto the surface of CSs to enhance their compatibility with the epoxy matrix and their capacity for encapsulating zinc cations and green L-glutamate (ZG) as corrosion inhibitors. Corrosion protection studies of the CS@ZG-COF extract showed an increase in the total impedance of immersed steel from 2298.5 to 14,502.2 Ω cm2 after 24 h, compared to the control solution. The potentiodynamic polarization (PP) test revealed an inhibition efficiency of 82.43% for the CS@ZG-COF extract. The EP/CS@ZG-COF coating demonstrated exceptional self-healing ability, corrosion protection, and barrier properties, forming a robust and uniform protective layer due to the release of encapsulated ZG complexes, which enhanced its electrochemical performance compared to other tested coatings. Furthermore, the incorporation of CS@ZG-COF NSs increased both dry and wet adhesion strength of the epoxy coatings, while the cathodic delamination radius was reduced from 7.6 to 4.4 mm in their presence.
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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".