Erosion–Corrosion of Novel Electroless Ni-P-NiTi Composite Coating
Bibliographic record
Abstract
The lifespan of low-carbon steel petroleum pipelines can often be shortened by the erosion–corrosion damage caused by their service conditions. Applying electroless Ni-P coating is a promising option to protect the steel from the environment due to its high hardness and corrosion resistance. However, electroless Ni-P has a low toughness but can be increased by the addition of NiTi ductile particles. This work produced electroless Ni-P and Ni-P-NiTi coatings of different thicknesses on AISI 1018 substrates and compared their erosion, corrosion, and erosion–corrosion behaviors. The methodology involved conducting slurry pot erosion–corrosion tests on AISI 1018 steel substrate, the monolithic Ni-P coatings, and the composite Ni-P-NiTi coatings. Erosion resistance was highly influenced by coating thickness, presumably because of the relationship between the erosion-induced compressive stresses and the coating’s as-plated internal stresses. The NiTi nanoparticle addition was highly effective at improving the erosion–corrosion resistance of the coating. Pitting corrosion and cracking were present after erosion–corrosion on the monolithic Ni-P coatings. However, the Ni-P-NiTi composite coating had a relatively uniform material loss. Overall, the AISI 1018 steel substrate had the worst erosion–corrosion resistance and 25 μm thick Ni-P-NiTi coating had the best.
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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) | 0.000 | 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".