MétaCan
Menu
Back to cohort
Record W2052404112 · doi:10.1080/10739140701436520

Planar Drift Tube for Ion Mobility Spectrometry

2007· article· en· W2052404112 on OpenAlexaff
Gary A. Eiceman, H. Schmidt, Jaime E. Rodriguez, Corey R. White, Evgeny V. Krylov, John A. Stone

Bibliographic record

VenueInstrumentation Science & Technology · 2007
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsIon-mobility spectrometryDrift tubeMass spectrometryIonPlanarIon-mobility spectrometry–mass spectrometryMaterials scienceTube (container)ChemistryComputer scienceAnalytical Chemistry (journal)ChromatographyTandem mass spectrometryComputer graphics (images)Selected reaction monitoringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The drift tube in ion mobility spectrometry is the component of central importance, where sample vapors are ionized and where ions are separated on the basis of gaseous mobility in a comparatively weak electric field. Construction of drift tubes is labor intensive and costly when built with precision machined components and an alternative design, fabricated using photolithography methods with planar drift plates, is described. Diagnostics of performance including response toward electric fields, peak shape of mobility spectra, determined values for reduced mobility coefficients, and patterns of response to changing vapor concentrations were consistent with conventional drift tubes. Neither ion losses nor band broadening were unexpectedly large though ultimate measures of performance were limited by the unavailability of drift tube components with rectangular profiles and by an imperfect reaction region design. The cost of making drift tubes is calculated as roughly 10% of that for conventional designs.

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)0.000
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 score0.147
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.315
Teacher spread0.300 · 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

Citations14
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueInstrumentation Science & TechnologySame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207