Development of nanocomposite coatings with improved mechanical, thermal, and corrosion protection properties
Bibliographic record
Abstract
In this study, new composite coatings are fabricated and investigated for their applications as the metal coating. The studied coatings consist of two-layered composites with various nanoparticulates as fillers in a polymeric matrix (styrene acrylic). The first layer bonded to the steel plate uses a combination of zinc particles, multi-walled carbon nanotubes, and graphene nanoplatelets. For the second layer, hexagonal boron nitride with high electrical insulation properties is added to the matrix. The morphology of the nanoparticulates is conducted using a scanning electron microscope. The coefficient of thermal expansion, cathodic disbondment resistance, gas penetration, and scratch resistance of the coatings are evaluated. The corroded area on the cathodic disbondment test specimens reduced down up to 90% for the composite with zinc (20 wt%), multi-walled carbon nanotubes (2 wt%), and graphene nanoplatelets (2 wt%), compared to a specimen coated with a pure polymer. It is seen that the presence of nanoparticulates decreased gas permeation and thermal expansion of the matrix by 75% and 65%, respectively. The addition of nanoparticulates also enhanced scratch resistance of the coating composites.
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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".