Development of a proton-to-neutron converter for radioisotope production at ISAC-TRIUMF
Bibliographic record
Abstract
Abstract At ISAC-TRIUMF, a 500 MeV proton beam is impinged upon “thick” targets to induce nuclear reactions to produce reaction products that are delivered as a Radioactive Ion Beam (RIB) to experiments. Uranium carbide is among the most commonly used target materials which produces a vast radionuclide inventory coming from both spallation and fission-events. This can also represent a major limitation for the successful delivery of certain RIBs to experiments since, for a given mass, many isobaric isotopes are to be filtered by the dipole mass separator. These contaminants can exceed the yield of the isotope of interest by orders of magnitude, often causing a significant reduction in the sensitivity of experiments or even making them impossible. The design of a 50 kW proton-to-neutron (p2n) converter-target is ongoing to enhance the production of neutron-rich nuclei while significantly reducing the rate of neutron-deficient contaminants. The converter is made out of a bulk tungsten block which converts proton beams into neutrons through spallation. The neutrons, in turn, induce pure fission in an upstream UCx target. The present target design and the service infra-structure needed for its operation will be discussed in this paper.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".