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Record W2056265001 · doi:10.1115/imece2005-81896

The Effect of Atomization Method on the Morphology of Spray Dried Particles

2005· article· en· W2056265001 on OpenAlexaff
Morteza Eslamian, Nasser Ashgriz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Toronto
FundersNational Science Council
KeywordsSplashNozzleMistNebulizerParticle sizeRange (aeronautics)Materials scienceSpray characteristicsSpray nozzleParticle (ecology)Aqueous solutionAnalytical Chemistry (journal)Composite materialMechanicsChemistryChromatographyThermodynamicsMeteorologyPhysics

Abstract

fetched live from OpenAlex

Effect of various atomization methods as well as solution concentration on the morphology of MgSO4 particles produced by spray drying of aqueous solutions of MgSO4 is investigated. Four types of atomizers are tested: (1) A mesh vibrating nebulizer, which generates low velocity droplets of 0.2 m/s with a size range of 1μm to 7 μm. (2) A splash plate nozzle, which generates droplets with an average velocity of 21 m/s and a size range of 40 μm to 500 μm. (3) An air mist atomizer, which generates droplets with an average velocity of 22 m/s and a size range of 20 μm to 150 μm. (4) A pressure atomizer, which generates droplets with an average velocity of 20 m/s and a size range of 30 μm to 1000 μm. Several types of particle morphologies are identified in this research. For the vibrating mesh nebulizer most of the particles are non-disrupted thick wall shells. For the splash plate and the air mist atomizer most of the particles are deformed spherical shells due to contraction and some are uniformly or non-uniformly disrupted. Higher initial solute concentration thickens the shell wall and prevents the particles to burst. It is found to be hard to obtain fully filled particles even for nearly saturated solutions at room temperature.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.137

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 head0.217
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2005
Admission routes1
Has abstractyes

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