MétaCan
Menu
Back to cohort
Record W2007633878 · doi:10.1002/rcm.2511

Ion sampling effects under conditions of total solvent consumption

2006· article· en· W2007633878 on OpenAlexaff
Bradley B. Schneider, Hassan Javaheri, Thomas R. Covey

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2006
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsOnex (Canada)
Fundersnot available
KeywordsChemistryVolumetric flow rateAnalytical Chemistry (journal)IonSolventAnalyteSampling (signal processing)ElectrosprayIon sourceMass spectrometryChromatographyThermodynamicsOrganic chemistryDetector

Abstract

fetched live from OpenAlex

The motivation of this work was to study some of the properties of nanoelectrospray operation under conditions where the entire sprayed liquid is vaporized and inhaled into the vacuum system. Under these conditions the desolvation requirements, sampling efficiency, concentration versus mass sensitivity, and molar response characteristics of various compounds were studied. The combined efficiency of ion production from solution and transfer into the vacuum system, referred to as sampling efficiency, is presented under various inlet conditions including different flow rates, solution compositions, and compound types. Under ideal solvent conditions the results for favorable compounds show sampling efficiencies of 70-85% at flows in the range of 50-500 nL/min. Efficiencies were lower for aqueous samples and compounds of different structures gave different molar response factors under these high sampling efficiency conditions. The relative molar response factors are presented in terms of those observed with higher flow rate sources which operate at significantly lower sampling efficiencies. In all cases, operating in this flow regime, the ion count rate was directly proportional to the absolute mass of analyte molecules entering the source. The experimental source used to carry out these studies included gas nebulization to stabilize the electrospray process, a heated laminar flow chamber to enhance desolvation and ion production, and various atmosphere-to-vacuum aperture diameters to maximize ion transfer.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.311
Teacher spread0.284 · 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.

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

Citations47
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueRapid Communications in Mass SpectrometrySame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207