A composite target of a radium salt and a soft metal matrix for production of Ac-225 with a proton or electron accelerator
Bibliographic record
Abstract
The production of 225Ac using either a proton or an electron accelerator requires a target of 226Ra. Radium metal is difficult to work with and so a radium salt, such as radium carbonate, is preferred as a target material. Normally available as a powder, the average density of the powder is low, and the thermal conductivity is poor, thus limiting the beam power that can be dissipated in the target. This work proposes the creation of a solid mixture of a radium salt powder with a metal matrix. Although aluminum powder has been used in similar applications, we suggest that indium powder is a good choice for the metal matrix in the case of radioactive radium salts. The target can be formed with low die pressure, thus simplifying the hot cell equipment needed for target preparation. We describe how the solid mixture can be formed, measure its thermal conductivity, and compare the value to model estimates. We calculate the yield of 225Ac under different scenarios. Calculations show that the radioactive isotopes of indium should not produce significant handling challenges. Thermal modeling indicates that the target temperature will be below the melting point of indium. The Fluka Monte Carlo code predicts that a target with 1/2 g of radium will produce 80 GBq for a 10-day irradiation with 3 kW of 24 MeV protons and 47 GBq of 225Ra for a 25 MeV, 20 kW electron beam. A target of one gram of radium will produce about 80 GBq with electrons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".