Study of Solid Particle Erosion & Electrochemical Behaviors of Graphene Enriched Ni-P Coatings
Bibliographic record
Abstract
Abstract Surface erosion and erosion-corrosion are among major degradations in the hydrocarbon industries and are driven by the attributes of process streams and materials of construction for process equipment(s)/piping. Despite significant corrosion and wear resistance, the performance of Ni-P coatings is inhibited due to fracture and cracking by the impact of erosive particles in the process streams. This study involves the preparation of graphene incorporated Ni-P coatings by adding various concentrations of graphene in Ni-P plating bath under stirring conditions. The solid particle erosion behavior of Ni-P coatings was studied under two different erodent velocities of 35 ms-1 and 52 ms-1 and three different incident angles as 45°, 60°, and 90°. Indent size and morphologies were characterized using the microscopic examination. The electrochemical behavior of ternary Ni-P coatings was studied using Potentiodynamic Polarization testing against 3.5 wt.% NaCl solution. Graphene addition improved the electrochemical and wear behavior by the reduction of porosities and improved hardness, respectively.
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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".