Influence of an inert background gas on bimetallic cross-beam pulsed laser deposition
Bibliographic record
Abstract
A cross-beam pulsed laser deposition (CBPLD) system operated at variable pressure in an inert (He) background atmosphere was used to deposit films from two dissimilar targets (Pt–Ru and Pt–Au). Using this setup, we showed that films with mixed Pt–Au and Pt–Ru composition can be prepared over the whole compositional range, from [Pt] = 0 to 100at.%. Films deposited at He pressure higher than 1.6Torr are fairly homogeneous and the standard deviation of the Pt concentration over the whole area of the deposit is less than 1at.%. Using a diaphragm located at the interaction zone between the two plasmas, a drastic reduction of the normalized droplet density was observed, from about 700×102cm−2nm−1 in conventional PLD to 6×102cm−2nm−1 in CBPLD. The deposition rate increases as the pressure is increased from vacuum to an optimal He pressure. The deposition rate decreases again for higher He pressure. The optimal operating conditions are P(He)=2Torr for Pt–Ru and P(He)=4Torr for Pt–Au. In these conditions, the deposition rates are, respectively, ∼32% and ∼22% of what they would be in conventional PLD. The behavior of the deposition rate with the He pressure is consistent with what can be concluded from a visual observation of the interaction of the plasma plumes at various pressures. A simple model considering the quadratic dependence of the velocity on the flow resistance of heavy particles in the rarefied light ambient particles is developed to understand the role of the background gas in the deposition rate. This model succeeds in predicting a maximum in the deposition rate versus He pressure curve, allowing us to get a better physical understanding of what is going on during the interaction between the two plasma plumes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".