Flower‐Like Covalent Organic Frameworks for Superior Corrosion Resistance and Durability in Epoxy Coatings
Bibliographic record
Abstract
Abstract Developing high‐performance nanocontainers for loading corrosion inhibitors offers a promising approach to addressing metal corrosion. However, the development of a durable material with high loading capacity, controlled release, and active‐barrier protection, especially in the long term, remains a challenge. To achieve such properties, both the molecular structure and morphology of the nanocontainers need to be optimized. Herein, a porous, crystalline 3D flower‐like covalent organic framework (COF), termed TP‐TA COF, constructed from 2D hexagonal nanosheets linked via imine bonds, is reported. Thereafter, through a one‐step solvothermal process, the COF is gradually grown on molybdenum disulfide (MDS), forming a layered structure with enhanced porosity to improve barrier properties and facilitate the efficient loading of corrosion inhibitors (zinc cations). The novel MDS‐COF‐Zn shows a charge transfer resistance of 72 000 Ω.cm 2 after 24 h and barrier retention for over 3000 h in a 0.6 m saline electrolyte. The MDS‐COF‐Zn ameliorates the mechanical and weathering properties of epoxy coatings. The adhesion strength of 4.80 MPa and the tensile strength of 46.50 MPa are achieved. Moreover, no color change is observed after 120 h of accelerated weathering testing. Overall, the proposal of novel MDS‐COF nanocontainers guides the design for durable hybrid corrosion inhibitors and beyond.
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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".