MétaCan
Menu
Back to cohort
Record W2006626012 · doi:10.1021/ac061276d

Atmospheric Pressure Photoionization for Ionization of Both Polar and Nonpolar Compounds in Reversed-Phase LC/MS

2006· article· en· W2006626012 on OpenAlexaff
Damon B. Robb, Michael W. Blades

Bibliographic record

VenueAnalytical Chemistry · 2006
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChemistryPhotoionizationIonizationPolarAtmospheric pressureAtmospheric-pressure chemical ionizationPhase (matter)Chemical ionizationChromatographyAnalytical Chemistry (journal)IonOrganic chemistryMeteorology

Abstract

fetched live from OpenAlex

Atmospheric pressure photoionization can provide high ionization efficiency simultaneously to both polar and nonpolar compounds delivered in reversed-phase solvent. The method to achieve this utilizes toluene as a dopant and simply requires that the solvent flow be limited so that reactions between toluene photoions and the organic component of the solvent are not driven to completion. Under these conditions, toluene photoions remain in the source for ionizing nonpolar compounds via charge exchange (electron transfer), while protonated solvent ions are available for proton-transfer reactions with polar molecules. The reagent ion mixture is then suitable for ionizing a wide range of both polar and nonpolar compounds. The critical effect of solvent flow rate is demonstrated here with results for a test analyte, 9-methylanthracene, which may be ionized by either charge exchange or proton transfer. For a solvent of 50:50 methanol/water (v/v), lowering the flow from 200 to 50 microL min-1 results in a 10x increase in charge exchange ionization efficiency--further flow reductions provide even greater enhancements. This method is compatible with sample delivery by direct infusion and micro- and narrow-bore LC, as well as conventional LC using a flow splitter.

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 categoriesnone
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 score0.162
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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.

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

Citations34
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207