Design of Nacre-Inspired 2D-MoS<sub>2</sub> Nanosheets Assembled with Mesoporous Covalent Organic Frameworks (COFs) for Smart Coatings
Bibliographic record
Abstract
High loading capacity and smart release of inhibitors are the first and foremost characteristics of nanocontainers, which play a pivotal role in metal active corrosion protection. The present work explores the development of novel protective nanocontainers based on recently emerged covalent organic frameworks (COFs). These highly porous frameworks with large surface area, outstanding thermomechanical properties, low density, and ease of functionalization are used as nanocontainers. On the other hand, molybdenum disulfide (MoS 2 ), a state-of-the-art 2D layered compound with a sheetlike structure, was utilized thanks to its excellent barrier properties. However, these lamellar structures suffer a high agglomeration tendency in polymeric matrices. Therefore, we developed a novel hybrid nanocontainer, inspired by natural nacre, by an in situ growth of COF on MoS 2 to improve the stability and provide a high inhibitor loading capacity. The porous and nitrogen-rich structure of COF made it a good carrier to adsorb europium cations as inorganic inhibitors and release them on demand by pH changes to suppress the electrochemical reactions. The as-synthesized nanoplatforms were used as pH-responsive fillers in the epoxy resin. The nanocomposite coatings showed almost 50 kΩ cm 2 total resistance and high impedance values (10 11 Ω cm 2 ) even after 77 days of immersion. Moreover, salt spray analysis depicted the smallest amount of rust and corrosion product after 31 days in the filled nanocomposite coating. Cathodic delamination and pull-off outcomes denoted that the filled coatings with the as-synthesized nanofiller showed the smallest cathodic delamination radius (3.41 mm) and lowest adhesion loss (24%) compared to the neat epoxy (7.55 mm and 46.7%). As such, the highly porous modified MoS 2 nanosheets are considered promising alternatives in a wide range of applications with anticorrosion properties.
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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".