A Solution-Based Approach for Mo-99 Production: Considerations for Nitrate versus Sulfate Media
Bibliographic record
Abstract
Molybdenum-99 is the parent of Technetium-99m, which is used in nearly 80% of all nuclear medicine procedures. The medical community has been plagued by Mo-99 shortages due to aging reactors, such as the NRU (National Research Universal) reactor in Canada. There are currently no US producers of Mo-99, and NRU is scheduled for shutdown in 2016, which means that another Mo-99 shortage is imminent unless a potential domestic Mo-99 producer fills the void. Argonne National Laboratory is assisting two potential domestic suppliers of Mo-99 by examining the effects of a uranyl nitrate versus a uranyl sulfate target solution configuration on Mo-99 production. Uranyl nitrate solutions are easier to prepare and do not generate detectable amounts of peroxide upon irradiation, but a high radiation field can lead to a large increase in pH, which can lead to the precipitation of fission products and uranyl hydroxides. Uranyl sulfate solutions are more difficult to prepare, and enough peroxide is generated during irradiation to cause precipitation of uranyl peroxide, but this can be prevented by adding a catalyst to the solution. A titania sorbent can be used to recover Mo-99 from a highly concentrated uranyl nitrate or uranyl sulfate solution; however, different approaches must be taken to prevent precipitation during Mo-99 production.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".