Effect of Graphene Enrichment on Solid Particle Erosion Performance of Electroless Ni-P Composite Coatings
Bibliographic record
Abstract
ABSTRACT Solid particle erosion (SPE) and dents (from contact loads) are among numerous surface degradations in the hydrocarbon industry that can in turn compromise the longevity of protective coatings. Both these degradation mechanisms can induce cracks that allow the corrosive solutions to seep through those cracks and corrode the underlying metal, thereby defeating the purpose of surface protection. Nickel-phosphorus (Ni-P) coatings have been known for decades for their corrosion resistance, but their applications in hydrocarbon industries are impeded by their tribological limitations, namely low wear resistance. In the current research work, graphene nanoplatelets were introduced to an Ni-P electroless plating bath in various concentrations (30 mg/L, 60 mg/L, and 100 mg/L) to achieve three different compositions of ternary Ni-P–graphene coatings, namely Ni-P-30 mg G, Ni-P-60 mg G, and Ni-P-100 mg G, respectively. Surface roughness was characterized via topography employing a laser confocal microscope. Coating hardness was characterized using Vickers hardness and the composition analyses were carried out via energy dispersive spectroscopy. SPE was conducted via Tungsten carbide (WC) erodent ball at three different impact angles and two different particle velocities. Finally, Hertzian indentation was performed under two different loads to characterize the denting behavior of coatings. Eroded and dented coatings were further visualized via an optical microscope. The highest concentration of graphene (by 18 vol.%) in Ni-P-30 mg G coating improved the hardness, leading to the smallest size of indents during both SPE and Hertzian indentation. Also, Ni-P-30 mg G exhibited no evidence of cracking under normal impact angle and particle velocities of 35 ms−1 and 52 ms−1.
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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".