On the adhesion of diamond‐like carbon coatings deposited by low‐pressure plasma on 316L stainless steel
Bibliographic record
Abstract
Diamond‐like carbon (DLC) thin films constitute proven protective coatings due to their outstanding mechanical and tribological properties, combined with a relative chemical inertness and long‐term stability. These make them particularly attractive to protect metallic medical implants from corrosion and erosion. However, lack of adhesion between DLC and metallic surfaces is a recurrent problem due to poor interactions with the native oxide layer. An effective strategy to overcome these adhesion issues consists in building interfacial layers. In this context, in this work, the use of a plasma treatment to generate shallow metallic carbide layers was investigated, to promote DLC adhesion directly on the surface of 316L stainless steel (SS). The metallic carbides presence stabilizes and promotes DLC thin film deposition. The highest adhesion was obtained on samples carburized by methane during 20 min with a bias of −700 V. Furthermore, this led to interface amorphization. In conclusion, this study shows that plasma can provide new insights for overcoming the lack of adhesion of DLC thin films on SS metallic surfaces.
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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".