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Record W1597031427 · doi:10.1002/9780470027318.a9483

Current Electrospray Mass Spectrometry: An Overview. Part<scp>B</scp>. Analyte Charging

2014· other· en· W1597031427 on OpenAlexaff
Udo H. Verkerk

Bibliographic record

VenueEncyclopedia of Analytical Chemistry · 2014
Typeother
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsYork University
Fundersnot available
KeywordsAnalyteElectrospray ionizationChemistryMass spectrometryIonizationAnalytical Chemistry (journal)ElectrosprayIon suppression in liquid chromatography–mass spectrometryIonExtractive electrospray ionizationChromatographySample preparation in mass spectrometryTandem mass spectrometryOrganic chemistry

Abstract

fetched live from OpenAlex

Electrospray ionization (ESI) is an atomization and ionization method through which a solution‐phase analyte can be transferred into the gas‐phase as an ion via minute charged droplets. The second part of this two‐part review on electrospray mass spectrometry reviews the final part of the charged droplet trajectory and covers transfer into the gas‐phase and ionization of the analyte. The now customary separation into the ion evaporation model (for small analytes) and the charged residue model (for large analytes) is followed and reviewed. These models yield insight into the charging process, but observations made with newer atomization techniques and quantitation limitations imposed by ionization suppression (matrix effect) indicate that a complete understanding is not available yet. Deviations from expected charged residue or ion evaporation behavior will be indicated. Chemical and charge‐driven surfactancy is discussed in relation to small analyte ionization and quantitation, whereas hydration and kinetic trapping of protein conformers are treated as part of the charged residue model.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0360.065

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 head0.290
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2014
Admission routes1
Has abstractyes

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