Radial P32 ion implantation using a coaxial plasma reactor: Activity imaging and numerical integration
Bibliographic record
Abstract
Beta-emitting biomedical implants are currently employed in angioplasty, in the treatment of certain types of cancers, and in the embolization of aneurysms with platinum coils. Radioisotopes such as P32 can be implanted using plasma-based ion implantation (PBII). In this article, we describe a reactor that was developed to implant radioisotopes into cylindrical metallic objects. The plasma first ionizes radioisotopes sputtered from a target, and then acts as the source of particles to be implanted into the biased biomedical device. The plasma therefore plays a major role in the ionization/implantation process. Following a sequence of implantation tests, the liners protecting the interior walls of the reactor were changed and the radioactivity on them measured. This study demonstrates that the radioactive deposits on these protective liners, adequately imaged by radiography, can indicate the distribution of the radioisotopes that are not implanted. The resulting maps give unique information about the activity distribution, which is influenced by the sputtering of the P32-containing fragments, their ionization in the plasma, and also by the subsequent ion transport mechanisms. Such information can be interpreted and used to significantly improve the efficiency of the implantation procedure. Using a surface barrier detector, a comparative study established a relationship between the gray scale of radiographs of the liners, and activity measurements. An integration process allows the quantification of the activities on the walls and components of the reactor. Finally, the resulting integral of the P32 activity is correlated to the sum of the radioactivity amounts that were sputtered from radioactive targets inside the implanter before the dismantling procedure. This balance addresses the issue of security regarding PBII technology and confirms the confinement of the radioactivity inside the chamber.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".