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Record W2112255827 · doi:10.1039/c2an35249d

Characterization of microstructured fibre emitters: in pursuit of improved nano electrospray ionization performance

2012· article· en· W2112255827 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Analyst · 2012
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrohydrodynamicsElectrosprayCommon emitterElectrospray ionizationComputational fluid dynamicsNano-Materials scienceCapillary actionVolumetric flow rateDielectricIonizationAnalytical Chemistry (journal)NanotechnologyMechanicsOptoelectronicsMass spectrometryChemistryComposite materialChromatographyIonPhysics

Abstract

fetched live from OpenAlex

Full-dimensional computational fluid dynamics (CFD) simulations are presented for nano electrospray ionization (ESI) with various emitter designs. Our CFD electrohydrodynamic simulations are based on the Taylor-Melcher leaky-dielectric model, and the volume of fluid technique for tracking the fast-changing liquid-gas interface. The numerical method is first validated for a conventional 20 μm inner diameter capillary emitter. The impact of ESI voltage, flow rate, emitter tapering, surface hydrophobicity, and fluid conductivity on the nano-ESI behavior are thoroughly investigated and compared with experiments. Multi-electrospray is further simulated with 2-hole and 3-hole emitters with the latter having a linear or triangular hole arrangement. The simulations predict multi-electrospray behavior in good agreement with laboratory observations.

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.

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 score0.531
Threshold uncertainty score0.284

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 head0.003
GPT teacher head0.174
Teacher spread0.171 · 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