Effect of Graphene Incorporation on Erosion-Corrosion Performance of Electroless Ni-P Coatings
Bibliographic record
Abstract
Abstract Erosion-corrosion is a dominant degradation in plant assets and influenced by various attributes of erodent such as velocity, corrosivity, shape, size, density, angle of attack and hardness, etc. Although Ni-P electroless coatings are famous for anti-corrosion and anti-wear properties; there is still room to improve these attributes that will pave its way towards various industrial applications. In this research work, various compositions of ternary Ni-P-graphene coatings were produced via the addition of different concentrations of graphene (30 mg/L, 60 mg/L, and 100 mg/L) into the electroless plating bath. Microstructural characterizations of coatings were conducted along with surface topography. Erosion-corrosion behaviors of the produced coatings were characterized using slurry pot erosion-corrosion testing employing AFS 50-70 Silica sand and 3.5 wt.% NaCl solution. The dominant wear mechanisms were identified using the SEM examination of surfaces after the erosion-corrosion test. High hardness, impermeability, and electrical properties of graphene concurrently improved the pure erosion, and erosion-corrosion attributes.
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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".