Reactively sputtered coatings for the protection of a nickel-based alloy against heavy oil corrosion fouling
Bibliographic record
Abstract
Critical components in the petrochemical industry require materials resistant to wear, corrosion, and fouling to withstand hydrocracking conditions. This work evaluates the fouling resistance of nitride coatings, sputter-deposited on Inconel 718 substrates by RF and pulsed DC magnetron sputtering, followed by processing heavy oil at high temperature (450 °C) and high pressure (>11 MPa). The nitride coatings protected the surface against diffusion, oxidation, and sulfidation. Specifically, the amorphous nitride film (am-Coating) exhibited better anticoking properties, while the polycrystalline nitrides (pc-Coating) offered superior mechanical performance. Contact angle measurements indicated that the pc-Coating was hydrophobic, with a surface energy dispersive component 41% higher than the am-Coating, potentially increasing its affinity for organic fouling and consequently poorer anticoking performance. The am-Coating exhibited a hardness of 17.5 GPa, with a cohesive failure (LC1) at 3.99 N and an adhesive failure (LC2) at 7.28 N. Incorporating a silicon (Si) interlayer improved LC1 and LC2 to 4.94 and 8.27 N, respectively, without significantly altering the hardness. The pc-Coating demonstrated a higher hardness of 27.0 GPa, with LC1 at 6.12 N, and no adhesive failure observed up to 20 N. The mechanical properties may have also contributed to the difference in foulant adhesion between the amorphous and polycrystalline coatings. Boriding of the Inconel 718 alloy was explored as an alternative to create a gradient of elasto-plastic properties from the substrate bulk toward the coating surface. However, standard borided samples performed poorly against fouling before and after the nitride depositions.
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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".