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Record W2951199792 · doi:10.1007/s13361-019-02252-0

Structure, Anion, and Solvent Effects on Cation Response in ESI-MS

2019· article· en· W2951199792 on OpenAlexafffund
Isaac Omari, Parmissa Randhawa, Jaiya Randhawa, Jenny Yu, J. Scott McIndoe

Bibliographic record

VenueJournal of the American Society for Mass Spectrometry · 2019
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryAcetonitrilePhosphoniumSolventProtonationDichloromethaneIonElectrosprayElectrospray ionizationInorganic chemistrySolvent effectsReactivity (psychology)Organic chemistry

Abstract

fetched live from OpenAlex

The abundance of an ion in an electrospray ionization mass spectrum is dependent on many factors beyond just solution concentration. Even in cases where the analytes of interest are permanently charged (under study here are ammonium and phosphonium ions) and do not rely on protonation or other chemical processes to acquire the necessary charge, factors such as cation structure, molecular weight, solvent, and the identity of the anion can affect results. Screening of a variety of combinations of cations, anions, and solvents provided insight into some of the more important factors. Rigid cations and anions that conferred high conductivity tended to provide the highest responses. The solvent that most closely reflected actual solution composition was acetonitrile, while methanol, acetonitrile/water, and dichloromethane produced a higher degree of discrimination between different ions. Functional groups that had affinity for the solvent tended to depress response. These observations will provide predictive power when accounting for analytes that for reasons of high reactivity can not be isolated.

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 score0.171
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.256
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

Citations33
Published2019
Admission routes2
Has abstractyes

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