Bibliographic record
Abstract
Oil and gas pipelines are subject to various forms of damage and degradation during their operation. Electroless Nickel (Ni-P) coatings are widely employed as protective coatings due to their ease of application and unique properties, including high wear and corrosion resistance. However, they are not ideal for protecting pipelines due to their brittleness and low toughness. Composite coatings of higher toughness can be developed through the co-deposition of second-phase particles into the Ni-P matrix. Tribaloy (CoMoCrSi) alloy possesses excellent mechanical and tribological properties making it a potential candidate for a high-toughness composite coating. In this study, Ni-P-Tribaloy composite coating consisting of 15.7 vol.% Tribaloy was successfully deposited on low-carbon steel substrates. Both the monolithic and the composite coatings were studied to evaluate the effect of the addition of Tribaloy particles. The micro-hardness of the composite coating was measured to be 6.00 GPa, 12% greater than that of the monolithic coating. Hertzian-type indentation testing was carried out to investigate the coating's fracture toughness and toughening mechanisms. The 15.7 vol.% Tribaloy coating exhibited remarkably less severe cracking and higher toughness. The following toughening mechanisms were observed: micro-cracking, crack bridging, crack arrest, and crack deflection. The addition of the Tribaloy particles was also estimated to quadruple the fracture toughness. Scratch testing was performed to evaluate the sliding wear resistance under a constant load and a varying number of passes. The Ni-P-Tribaloy coating exhibited more ductile behavior and higher toughness, as the dominant wear mechanism was identified as material removal, as opposed to brittle fracture in the Ni-P coating.
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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) | 0.001 | 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 | 0.001 | 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".