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Cooling of highly-charged, short-lived ions for precision mass spectrometry at TRIUMF's Ion Trap for Atomic and Nuclear Science

2013· article· en· W1968170531 on OpenAlexafffund
B. E. Schultz, U. Chowdhury, V. V. Simon, C. Andreoiu, A. Chaudhuri, A. T. Gallant, A. A. Kwiatkowski, T. D. Macdonald, M. C. Simon, J. Dilling, G. Gwinner

Bibliographic record

VenuePhysica Scripta · 2013
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityUniversity of ManitobaTRIUMF
FundersNatural Sciences and Engineering Research Council of CanadaStudienstiftung des Deutschen VolkesTRIUMF
KeywordsPenning trapNuclideIon trapIonAtomic physicsElectron beam ion trapPhysicsElectronAtomic massTrap (plumbing)IsotopeMass spectrometryElectron captureNuclear physicsCyclotronCathode ray

Abstract

fetched live from OpenAlex

At TRIUMF's Ion Trap for Atomic and Nuclear Science (TITAN), masses of short-lived nuclides are measured accurately and precisely using Penning trap mass spectrometry. The achievable precision is primarily limited by the radioactive lifetime of the nuclides. To boost the precision TITAN has demonstrated that short-lived isotopes can be charge-bred to higher charge states within 10–100 s of ms using an electron beam ion trap. The charge breeding process increases the energy spread of the ions, which in turn affects the precision and the efficiency. A novel cooler Penning trap (CPET) has been developed to trap and cool highly-charged ions using electrons prior to the precision measurement. A discussion of electron cooling and the current status of CPET will be given.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.023
GPT teacher head0.274
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2013
Admission routes2
Has abstractyes

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