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Record W2158757550 · doi:10.1002/open.201402002

An Improved Rapid Mixing Device for Time‐Resolved Electrospray Mass Spectrometry Measurements

2014· article· en· W2158757550 on OpenAlexaff
Nicholas W. Zinck, Ann-Kathrin Stark, Derek J. Wilson, Michal Sharon

Bibliographic record

VenueChemistryOpen · 2014
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsYork University
FundersIsrael Science Foundation
KeywordsMass spectrometryMixing (physics)ChemistryAnalytical Chemistry (journal)Electrospray ionizationElectrosprayChromatographyPhysics

Abstract

fetched live from OpenAlex

Time series data can provide valuable insight into the complexity of biological reactions. Such information can be obtained by mass-spectrometry-based approaches that measure pre-steady-state kinetics. These methods are based on a mixing device that rapidly mixes the reactants prior to the on-line mass measurement of the transient intermediate steps. Here, we describe an improved continuous-flow mixing apparatus for real-time electrospray mass spectrometry measurements. Our setup was designed to minimize metal-solution interfaces and provide a sheath flow of nitrogen gas for generating stable and continuous spray that consequently enhances the signal-to-noise ratio. Moreover, the device was planned to enable easy mounting onto a mass spectrometer replacing the commercial electrospray ionization source. We demonstrate the performance of our apparatus by monitoring the unfolding reaction of cytochrome C, yielding improved signal-to-noise ratio and reduced experimental repeat errors.

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)0.003
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.020
GPT teacher head0.276
Teacher spread0.257 · 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
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

Citations18
Published2014
Admission routes1
Has abstractyes

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