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Record W2105765521 · doi:10.1002/rcm.3061

Modeling of ion processes in atmospheric pressure matrix‐assisted laser desorption/ionisation

2007· article· en· W2105765521 on OpenAlexaff
Vadym D. Berkout, Sergey Kryuchkov, Vladimir M. Doroshenko

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2007
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Calgary
FundersNational Center for Research ResourcesNational Institutes of Health
KeywordsChemistryDesorptionAtmospheric pressureIonLaserMatrix (chemical analysis)IonizationSoft laser desorptionAnalytical Chemistry (journal)Environmental chemistryChromatographyMatrix-assisted laser desorption/ionizationOpticsOrganic chemistryMeteorologyAdsorption

Abstract

fetched live from OpenAlex

Atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI) has proven a convenient and rapid method for ion production in the mass spectrometric analysis of biomolecules. This technique, like other atmospheric pressure ionization methods, suffers from ion loss during ion transmission from the atmosphere into the vacuum of the mass spectrometer. In this work, a simple model describing ion formation and ion motion towards the inlet capillary of the mass spectrometer is described. Both the gas flow and electric field near the MALDI plate were numerically calculated using the boundary element method (BEM). The ions were moving along with the gas flow and drifting in the electric field in accordance with their ion mobility properties. The ion signal dependence on an electric field strength obtained in the proposed model correlates well with experimental results.

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.025
GPT teacher head0.304
Teacher spread0.278 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

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