Phase Structure, Wear Resistance and Antimicrobial Response of Austenitic Stainless Steels 316L by Sputtering Cu during Plasma Nitriding and PECVD of Silicon Nitride
Bibliographic record
Abstract
The surface of stainless steel 316L was plasma nitrided and subsequently deposited with silicon nitride from tetraethylorthosilicate (TEOS):H2:N2 gas mixtures by plasma enhanced chemical vapor deposition (PECVD). A copper mesh was employed to sputter copper atoms onto the surface during the two processes to consider its effect on the microstructure, tribology and antibacterial response of hard surface layers. The surface layers were characterized using XRD, optical and SEM microscopy, EDX analysis, microhardness test, pin-on-disc wear tests and microbial viability test. α-Si3N4 was found on the top surfaces of two steps processed stainless steel 316 L. Fe2–3N, Fe4N and CrN were identified in the compound layers. The overall thickness of the surface layers were more than 60 µm. The two step treatments improved the hardness up to 1600 HV0.1. The combination of plasma nitriding (with Cu sputtering) and PECVD of silicon nitride compound (with Cu sputtering) of SS 316 L resulted in superior high hardness, 3 times lower friction and 10 times higher wear resistance of treated surfaces if compared to those of conventional plasma nitrided surfaces. Cu addition to single plasma nitriding resulted in an effective reduction of 100% of Escherichia coli (E. coli) within 2 to 3 h. However the bacteria viability after the two step processes with Cu addition diminished to zero in 3.5 to 4 h. The antimicrobial response of the surfaces depends mainly on the Cu action and does not interfere with the wear resistance of the 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".