Effect of process parameters on the mechanical properties of carbon nitride thin films synthesized by plasma assisted pulsed laser deposition
Bibliographic record
Abstract
We present an investigation of the effect of the process parameters, namely deposition pressure and laser intensity, on the growth and mechanical properties of carbon nitride (CNx) thin films synthesized by plasma assisted pulsed laser deposition. Deposition at high remote plasma pressure (200 mTorr) enhances both growth rate and nitrogen incorporation (up to 40 at. %), but nano-indentation measurements indicate that these films are very soft and have poor mechanical properties. At low remote plasma pressure (0.5 mTorr), the nitrogen content varies from 24 to 16 at. % with increasing laser intensity as the films become much harder and more elastic, with hardness and Young’s modulus values reaching 24 GPa and 230 GPa, respectively. These effects are explained in terms of a thermalization of the laser plasma at 200 mTorr and indicate that plasma activation of nitrogen does not provide any particular benefit to the film properties when deposition is performed at high pressure. However, at low pressure, the benefit of plasma activation is evidenced through enhanced nitrogen incorporation in the films while preserving the highly energetic species in the ablation plume. Such conditions lead to the synthesis, at room temperature, of hard and elastic films having properties close to those of fullerene-like CNx.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".