Co-axial ECR plasma system for radioactive ion implantation
Bibliographic record
Abstract
A pulsed, co-axial electron cyclotron resonance (ECR, 2.45 GHz) plasma reactor was designed and tested to demonstrate the feasibility of plasma-based radioactive ion implantation ( 32 P radioisotope). The geometry of the reactor was designed to produce an efficient implantation of cylindrical implants. Therefore, the reactor is cylindrical in shape, and is equipped with a cylindrical grid in a co-axial geometry. The plasma is created between the wall and the grid; the plasma surrounds the implant, allowing for a radial implantation. A 1 ms microwave pulse creates a plasma in argon, which sputters material from a radioactive cathode. A fraction of the radioisotopes is then ionized, and the ions are implanted into negatively biased metal samples. The plasma was characterized by means of electrostatic probes, giving spatial evaluations of the electron temperature, plasma potential and electron density. Titanium samples were implanted with 32 P during a study that aimed at optimizing the position of the radioactive sputter cathode in the plasma. From an analysis of the distribution of the radioactive fragments, we deduce that the plasma potential has a marked effect on the ion trajectories. In particular, it provides a more uniform implantation distribution than one would otherwise expect. For plasma densities ∼8 × 10 10 cm −3 , implantation efficiencies as high as 1% are measured; this is about 100 times higher than conventional beam-line ion implantation.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".