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Record W6966706366 · doi:10.48448/588k-fw82

Negative Ion Source Development for Accelerator Mass Spectrometry

2021· other· en· W6966706366 on OpenAlexaff

Bibliographic record

VenueUnderline Science Inc. · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAccelerator mass spectrometryIon sourceIonMass spectrometryIon beam depositionThermal emittanceSputteringIon beam

Abstract

fetched live from OpenAlex

Negative Ion Source Development for Accelerator Mass Spectrometry CJ Tiessen, WE Kieser, and XL Zhao Accelerator mass spectrometry (AMS) is a highly sensitive technique used for the analysis of long-lived radioisotopes. While carbon-14 dating is the most well known application, AMS can be used to measure other isotopes such as beryllium-10, aluminum-26, iodine-129, and uranium-236 which are useful in geology, archeology, environmental tracer and chronology studies, nuclear waste monitoring, and nuclear forensics. The technique uses a combination of electrostatic analyzers, mass-separation magnets, electrostatic lenses, as well as a tandem accelerator. In the accelerator, an electron stripping gas canal is used to convert incoming negative ions to positive ions while simultaneously disintegrating molecular isobars. Ions from the samples are injected into the accelerator using a cesium sputter negative-ion source. The focus of this work is to model the electrodynamics within the ion source, including the effects of the more intense positive cesium ion beam and the sputtered sample negative-ion beam. Simulations using Integrated Engineering Software’s Lorentz 2E ion optics software will guide the design of a new ion source with the goal of increasing the emitted sample ion current while also improving the emittance of this beam. Following a short overview of the AMS system, details of the ion source, including the mutual space-charge interaction of the two beams, will be presented.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.311
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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