Theoretical optimal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mstyle/> </mml:mrow> <mml:mrow> <mml:mn>232</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> Th target thicknesses for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mstyle/> </mml:mrow> <mml:mrow> <mml:mn>225</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> Ac production at proton energies of 70–200 MeV
Bibliographic record
Abstract
Abstract Objective. Conventional 232 Th target thicknesses used for 225 Ac production are substantially smaller than the proton ranges, underutilizing the 225 Ac production potential of proton beams. This study explores theoretical optimal thicknesses of 232 Th targets at widely used proton energies, 70–200 MeV. Approach. Yields of 225 Ac and impurity levels of 227 Ac were calculated for proton energies of 70–200 MeV and 232 Th target thicknesses of 0.05–24 mm using Monte Carlo simulations. Ranges of optimal target thicknesses were defined for each proton energy based on the relative rates of change (RROCs) of 225 Ac yields per target thickness of 0.25 mm and on energy-adjusted RROC thresholds. Expected 225 Ac yield gains from using the optimal thicknesses were also estimated. Main results. The ranges of theoretical optimal thicknesses for representative energies, 70, 100, 160, and 200 MeV, were found to be 2–2.25 mm, 4.5–6.25 mm, 7.75–14.5 mm, and 14.25–21.75 mm, respectively. All these thicknesses are markedly larger than those of the conventional thin targets used for medium- to large-scale 225 Ac production, 0.25 mm and 0.5 mm. By using these optimal target thicknesses, 225 Ac yields are expected to increase by factors of up to 8.8 at 70 MeV and 63.8 at 200 MeV. Actinium-227 impurity levels were unaffected by target thickness optimization at all proton energies. Significance. Optimizing 232 Th target thicknesses can provide proton accelerator facilities operating in the 70–200 MeV range with a straightforward means of increasing their
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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".