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Influence of Coulomb effects on the resolving power of multireflection mass-spectrometer systems

2015· article· en· W2505183297 on OpenAlexaff
M. G. Skoblin, I A Kopaev, Dmitry Greenfield, Alexander Makarov, Mikhail Monastyrskiy, С. С. Алимпиев

Bibliographic record

VenueQuantum Electronics · 2015
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsThermo Fisher Scientific (Canada)
Fundersnot available
KeywordsBunchesReflectronPhysicsSpectrometerComputational physicsCoulombAnalyserMass spectrometryIonCoalescence (physics)Electric fieldAtomic physicsOpticsNuclear physicsBeam (structure)IonizationQuantum mechanicsElectronTime-of-flight mass spectrometry

Abstract

fetched live from OpenAlex

General theoretical approaches to the modelling of Coulomb effects in short ion bunches, developed previously by the authors, are applied in this paper to the calculation of multireflection mass-spectrometer systems. A separate module of the MASIM 3D applied software package is designed. An adaptive computational procedure for calculating the 'mirror potential' induced by an ion bunch on the surface of field-forming electrodes is proposed. The dynamics of ion bunches in a time-of-flight reflectron-type mass analyser is calculated and the limitations on the resolving power, caused by resonant Coulomb effects of self-bunching and coalescence in the groups of particles with close masses, are revealed on the basis of numerical experiments.

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)0.002
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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