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Development of a proton-to-neutron converter for radioisotope production at ISAC-TRIUMF

2018· article· en· W2894962860 on OpenAlexafffund
Luca Egoriti, Ricardo Augusto, P. Bricault, T. Day Goodacre, M. Delonca, D. Houngbo, J.P. Ramos, S. Rothe, T. Stora, A. Gottberg

Bibliographic record

VenueJournal of Physics Conference Series · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of VictoriaUniversity of British ColumbiaTRIUMF
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMF
KeywordsNuclear physicsSpallationRadiochemistryIsotopeNeutronFissionFission product yieldProtonNuclear engineeringChemistryPhysics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.242
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes2
Has abstractyes

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