Study of laser plasmas in straight magnetic fields for thin film deposition
Bibliographic record
Abstract
Summary form only given. Pulsed Laser Deposition is a versatile technique for depositing a variety of materials in thin films form. One complication with pulsed laser deposition is the inclusion of micron and submicron size debris particles from the source materials. These particles arise from the release of solid material and liquid droplets from the shock wave produced by the laser produced plasma. An additional issue arising in the deposition process is the rapid fall off in coating flux with distance from the plasma source point. Thus if high coating rates are desire, the substrate must be located close to the plasma source. In this paper the guiding of plasmas produced by nanosecond ultraviolet laser pulses using straight solenoidal magnetic fields has been studied as a mean of giving a low-debris, controlled deposition source for the production of thin films. Experimental study on the guiding efficiency of the coating flux at various magnetic field strength, using Langmuir probes and quartz crystal monitor has been carried out. The transport of the ionization portion of the laser plasma was measured by arrays of Langmuir probes and the total coating flux including neutrals was measured by a quartz crystal monitor. In addition to the magnetic field strengths, the orientation of the target and the repetition rate of the laser also affect the transport efficiency. These results were presented and compared to predictions of plasma guiding using numerical plasma simulation models.
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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) | 0.002 | 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".