MétaCan
Menu
Back to cohort

Creating negative ion beams from neutral gases using a negative hydrogen ion source

2024· article· en· W4399213287 on OpenAlexaff
Arjun Paul, Stéphane Melanson, Tobias Junginger, Morgan Dehnel

Bibliographic record

VenueJournal of Instrumentation · 2024
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFaraday cupIonIon gunBeam (structure)Ion beamAtomic physicsIon sourceVapoursMass spectrometryIonizationFlammable liquidIon beam depositionHydrogenAlkali metalMaterials scienceChemistryPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract A typical method to produce negative ion beams uses alkali vapour as a medium for a double charge exchange to convert incident positive (1+) beams to negative (1-) beams. Alkali vapours pose a problem in ion production as they are flammable, explosive and cause vacuum surface contamination. Thus, it is advantageous to use non-metallic vapours for charge exchange as it will prevent hazards and the contamination of vacuum surfaces and targets in which the negative ion beams are incident upon. In this paper we will describe and demonstrate the process of creating negative ion beams by impinging a 15 keV to 30 keV beam from D-Pace's TRIUMF licenced H - volume-cusp ion source (1 mA to 15 mA) onto a volume of non-metallic neutral gas (X) resulting in a single or two-step charge exchange: H - + X → H + X - or H - + X → H + X + e → H + X - . The newly created X - ion beams will be accelerated by a (1 to 20) kV electrostatic accelerator and passed through a mass spectrometer system to separate the primary H - beam from the X - beam. The two gases studied are He and H 2 and we will present the magnitude of the resultant beam currents after being separated from the incident H - beam by the mass spectrometer system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.479

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 communication0.0000.001
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 head0.017
GPT teacher head0.259
Teacher spread0.243 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of InstrumentationSame topicParticle accelerators and beam dynamicsFrench-language works237,207