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Record W2077294548 · doi:10.1088/0022-3727/37/4/007

Numerical investigation of interface region flows in mass spectrometers: ion transport

2004· article· en· W2077294548 on OpenAlexafffund
Manish Jugroot, C. P. T. Groth, Bruce A. Thomson, Vladimir Baranov, B. A. Collings

Bibliographic record

VenueJournal of Physics D Applied Physics · 2004
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Innovation Trust
KeywordsInterface (matter)Mass transportIonMass spectrometryAnalytical Chemistry (journal)MechanicsChemistryMaterials sciencePhysicsEngineering physicsChromatography

Abstract

fetched live from OpenAlex

The transport of free ions through highly under-expanded jet flows of neutral gases and in the presence of applied electric fields is investigated by continuum-based numerical simulations. In particular, numerical results are described that are relevant to ion flows occurring in the interface region of mass spectrometer systems. A five-moment mathematical model and parallel multi-block numerical solution procedure are developed for predicting the ion transport. The model incorporates the effects of ion–neutral collision processes and is used in conjunction with a Navier–Stokes model and flow solver for the neutral gas to examine the key features of the ion motion. The influences of the neutral gas flow, electric field, and flow field geometry on ion mobility are all carefully assessed. Several ions of varying mass and charge are considered, and the relative importance of competing effects (i.e. electric field and ion–neutral collision effects) is discussed. The capability of controlling the charged particle motions through a combination of directed neutral flow and applied electric field is demonstrated for high-speed, hypersonic jet flows.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.216
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations22
Published2004
Admission routes2
Has abstractyes

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