Current Developments in the Industry Production Process of 99Mo for Medical Use
Bibliographic record
Abstract
The aging of global research reactors has led to a serious shortage of target irradiation capacity for industrial production of 99Mo, as well as restrictions on the civilian use of high enriched uranium under the Nuclear Non Proliferation Treaty. This has forced the exploration of new production processes for 99Mo and the conversion of existing high enriched uranium targets to low enriched uranium targets. In the process of exploring new technologies and target conversion, it has caused a global shortage of medical 99Mo supply, endangering people’s health.The United States, Canada, and Europe are intensifying their exploration of new process technologies for the commercialization and stable supply of medical 99Mo, such as exploring irradiation targets with as accelerator particle beams, subcritical devices, small reactors, molten salt stacks, and solution stacks, using new materials and processes to prepare targets, and applying supercritical, ionic liquids, and new chromatographic columns to the preparation of 99Mo. The production of low enriched uranium targets is gradually replacing high enriched uranium targets. The photonuclear reaction process has matured, and processes such as low-power research reactors, subcritical devices, and particle beams can meet the needs of some local radioactive drugs and nuclear medicine. The active research on accelerator production technology is the development direction of future medical 99Mo 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".