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Record W2094871192 · doi:10.1115/imece2010-38935

Modeling of Particle Formation via Emulsion Combustion Spray Method

2010· article· en· W2094871192 on OpenAlexaff
Morteza Eslamian, Mahmoud Ahmed

Bibliographic record

VenueVolume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and B · 2010
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmulsionCombustionMaterials scienceChemical engineeringParticle (ecology)NanoparticleVaporizationEmulsified fuelPyrolysisParticle sizeAgglomerateComposite materialNanotechnologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper, a theoretical model is developed to simulate the process of vaporization and burning of emulsion droplets and the evolution and the formation of micro- and nano-particles via the Emulsion Combustion Method (ECM). In ECM, a precursor solution is mixed and stirred with a fuel to form an emulsion of micro-solution droplets suspended in the oil phase. The emulsion liquid is sprayed into in emulsion droplets that are therefore composed of a fuel and tiny micro solution droplets. Spray droplets are ignited and burn to form final micro- or nanoparticles. In this paper, the principles of the method and the main governing equations of the developed model are discussed. Model equations are solved numerically and the results will be presented. The model predicts that depending on the operating and processing conditions, such as the initial size and concentration of the suspended micro solution droplets in emulsion droplets, the fuel fraction of the emulsion droplets, and the fuel combustion enthalpy, the final particles may be mono-dispersed nanoparticles, or larger agglomerate particles. Due to the similarity of the emulsion combustion method with spray pyrolysis and flame spray pyrolysis, most of the equations presented here are applicable to those methods, as well.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.700
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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.

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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueVolume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and BSame topicCombustion and flame dynamicsFrench-language works237,207