Catalytic Growth of High-Performance Graphite-like Carbon Films on a Nitrided Substrate: Experimental Study and First-Principles Calculations
Bibliographic record
Abstract
This study reports a novel process for the fabrication of high-performance graphite-like carbon (GLC) films on nitrided substrates through successive steps in a plasma nitriding system. Unlike films fabricated via conventional dual treatments (nitriding + film deposition), here-fabricated GLC films were grown on the surface of nitrided steel via a catalytic reaction. A transition zone having a nanocrystalline and amorphous structure was observed at the interface between the nitrided substrate and GLC films, which increased the durability of GLC films because this structure enhanced the adhesion of GLC films on the nitrided substrate, improving resistance to spallation and wear. Experimental study and first-principles calculations showed that the Fe 3 N phase had a stronger catalytic effect on GLC films than the Fe 4 N phase, and GLC films grown on the nitrided layer dominated by Fe 3 N were thicker with stronger adhesion and excellent frictional and wear properties compared with GLC films grown on the nitrided layer dominated by Fe 4 N because of the beneficial structure formed at the interface. This study reports a simple and inexpensive method to fabricate a dual layer containing high-performance GLC films via a catalytic growth and interface matching mechanism.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".