Modeling of Particle Formation via Emulsion Combustion Spray Method
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".