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Record W2967728882 · doi:10.1021/acs.analchem.9b02681

Mass Spectrometry Based Approach for Organic Synthesis Monitoring

2019· article· en· W2967728882 on OpenAlexafffund
Veronica Termopoli, Elena Torrisi, Giorgio Famiglini, P. Palma, Giovanni Zappia, Achille Cappiello, Gregory W. Vandergrift, Misha Zvekic, Erik T. Krogh, Chris G. Gill

Bibliographic record

VenueAnalytical Chemistry · 2019
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsSimon Fraser UniversityUniversity of VictoriaVancouver Island University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMass spectrometryElectrospray ionizationSelected reaction monitoringAnalyteElectron ionizationSemipermeable membraneMembraneIonizationDirect electron ionization liquid chromatography–mass spectrometry interfaceChemical ionizationAnalytical Chemistry (journal)Tandem mass spectrometryChromatographyOrganic chemistryIon

Abstract

fetched live from OpenAlex

Current mass spectrometry-based methodologies for synthetic organic reaction monitoring largely use electrospray ionization (ESI), or other related atmospheric pressure ionization-based approaches. Monitoring of complex, heterogeneous systems may be problematic because of sampling hardware limitations, and many relevant analytes (neutrals) exhibit poor ESI performance. An alternative monitoring strategy addressing this significant impasse is condensed phase membrane introduction mass spectrometry using liquid electron ionization (CP-MIMS-LEI). In CP-MIMS, a semipermeable silicone membrane selects hydrophobic neutral analytes, rejecting particulates and charged chemical components. Analytes partition through the membrane, and are then transported to the LEI interface for sequential nebulization, vaporization, and ionization. CP-MIMS and LEI are both ideal for continuous monitoring applications of hydrophobic neutral molecules. We demonstrate quantitative reaction monitoring of harsh, complex reaction mixtures (alkaline, acidic, heterogeneous) in protic and aprotic organic solvents. Also presented are solvent-membrane compatibility investigations and, in situ quantitative monitoring of catalytic oxidation and alkylation reactions.

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: none
Teacher disagreement score0.755
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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