Investigation of Zinc- and Carbon-Nanoparticle-Based Nanocomposite Coatings
Bibliographic record
Abstract
High performance coatings of pipelines should possess properties such as high adhesion to the substrate, low gas permeability, high corrosion resistance, etc. In this study, novel nanocomposite materials are studied for coating of steel plates. The coatings consist of two-layer composites with different nanoparticulates. Zinc particles, multi-walled carbon nanotubes (MWCNT), and graphene nanoplatelets (GNP) are used for the base layer bonded to the steel. Zinc particles are used as a filler and act as sacrificial anode against corrosion (cathodic protection). Hexagonal boron nitride (hBN) is added to the matrix for the second layers. Adhesion of the coatings is studied through pull-off tests. To examine the corrosion protection capabilities, cathodic disbondment tests are conducted on the coated steel plates. The gas permeability of the coatings is evaluated through a standard testing technique. Results show that addition of Zinc particulates could enhance corrosion protection. The addition of GNP and hBN nanoparticulates resulted in lower gas penetration. Results of this research will contribute to the development of advanced pipeline coatings.
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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".