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Record W2004006672 · doi:10.1615/atomizspr.v20.i5.40

AEROSOL CHARACTERIZATION OF CONCENTRIC PNEUMATIC NEBULIZER USED IN INDUCTIVELY COUPLED PLASMAMASS SPECTROMETRY (ICP-MS)

2010· article· en· W2004006672 on OpenAlexaff
Arash Kashani, J. Mostaghimi

Bibliographic record

VenueAtomization and Sprays · 2010
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNebulizerInductively coupled plasma mass spectrometryAerosolMass spectrometryInductively coupled plasmaMaterials scienceNozzleAnalytical Chemistry (journal)Volumetric flow rateChromatographyPlasmaChemistryMechanicsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

A type-C all-glass concentric pneumatic nebulizer (CPN) widely used for sample introduction in inductively coupled plasma mass spectrometry is characterized. Although the fundamentals of the spray formation in CPN and (ICP-MS nebulizers in general) is similar to mechanical engineering nozzles, the specific design, the geometry, dimension, and nominal flow regimes of the former require separate characterization. As investigated in this paper, the well-known Nukiyama-Tanasawa (NT) correlation leads to erroneous size prediction. In this study, an optimization to the NT model together with characterization for spray velocity is presented. The efficiency of the nebulizer under different gas and liquid flow rates is determined, and finally the characterization results of size and velocity are employed into a maximum entropy principle model to predict the velocity and size distributions of the sprayed aerosol.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

Explore more

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