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Record W2327311937 · doi:10.1255/ejms.1278

Quadrupolar Ion Excitation for Radiofrequency-Only Mass Filter Operation

2014· article· en· W2327311937 on OpenAlexaff
D. J. Douglas, A.G. Polyakov, N. V. Konenkov

Bibliographic record

VenueEuropean Journal of Mass Spectrometry · 2014
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQuadrupoleAtomic physicsExcitationIonAmplitudePhysicsQuadrupole mass analyzerOscillation (cell signaling)ChemistryOptics

Abstract

fetched live from OpenAlex

Trajectory calculations are used to model a mass filter based on the radiofrequency (rf)-only operation of a linear quadrupole with resonant quadrupole excitation of ions (resonant excitation applied with the same spatial electric field as the main quadrupole rf field). Ions are not trapped, but pass continuously through the quadrupole. Excited ions gain axial kinetic energy in the fringe field at the quadrupole exit, overcome a stopping potential and are transmitted to an external detector. No quadrupole direct current is required, unlike conventional operation at the tip of the first stability diagram. Quadrupole excitation can be applied with amplitude or frequency modulation of the main rf voltage, or with an auxiliary excitation voltage. All three methods give the same mass resolution. The mass resolution, R, is given by R ≈ 0.5q(dβ/dq)n where q is a Mathieu parameter, β(q), determines the frequency of ion oscillation and n is the number of cycles of the rf field experienced by an ion, determined by the flight time through the quadrupole. A disadvantage of this mode of operation is that the flight times of the ions and the excitation amplitudes or modulation depths need to be synchronized.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.014
GPT teacher head0.246
Teacher spread0.233 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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