Preparation & Tribological Characterization of Graphene Enriched Ni-P Coatings on X70 Pipelne Steel
Bibliographic record
Abstract
Abstract Cracking of electroless Ni-P coatings due to lower toughness hinder its application in various service conditions, and in turn demands improved toughness and corrosion resistance using a ternary coating matrix. On the other hand, graphene is known for its higher hardness, impermeability; so, was selected as a candidate for ternary coating systems. In this research, 1 wt. % graphene suspension after Raman Spectroscopic checks was added into electroless Ni-P plating bath in various concentrations to produce three different compositions of Ni-P-G (graphene) coatings. Micro-structural and surface attributes were studied using scanning electron microscope and laser confocal microscope, respectively. Scratch and indentation behaviors of substrate and coatings were investigated using UMT Scratch tester and Hertzian type Indenter. Corrosion resistance results from potentiodynamic testing of ternary coatings were compared with that for substrate steel and as-plated Ni-P coating. Graphene incorporation in Ni-P coating matrix improved the Vickers micro-hardness as well as wear resistance. Also, graphene induced Ni-P coating exhibited increased corrosion resistance and toughness.
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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".