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Record W2761917312 · doi:10.1002/elps.201700364

Nonaqueous capillary electrophoresis mass spectrometry method for determining highly hydrophobic peptides

2017· article· en· W2761917312 on OpenAlexafffund
Jianhui Cheng, David D. Y. Chen

Bibliographic record

VenueElectrophoresis · 2017
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaSCIEX
KeywordsChemistryAmmonium formateMass spectrometryChromatographyFormic acidCapillary electrophoresis–mass spectrometryCapillary electrophoresisAcetonitrileFormateAnalytical Chemistry (journal)Triple quadrupole mass spectrometerSelected reaction monitoringElectrospray ionizationTandem mass spectrometryOrganic chemistry

Abstract

fetched live from OpenAlex

A nonaqueous capillary electrophoresis mass spectrometry (NACE-MS) method was developed to separate and determine highly hydrophobic temporin peptides. The nonaqueous background electrolyte solution was a mixture of 20% acetonitrile, 78% methanol and 2% formic acid, with 20 mM ammonium formate. The separation of six peptides was completed within 12 min. The CE system was connected to a triple quadrupole mass spectrometer operating in MRM mode using a chemical modifier solution of 2 mM ammonium formate in ethanol with the flow through microvial interface. The mass spectrometer offered a second dimension of separation for peptides having identical migration times but different structures. The new method represents the first system capable of reliably determining hydrophobic peptides without using reversed phase liquid chromatography mass spectrometry.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations17
Published2017
Admission routes2
Has abstractyes

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