Impact of co‐flow on the spray flame behaviour applied to nanoparticle synthesis
Bibliographic record
Abstract
Abstract Flame spray pyrolysis (FSP) is an established process to synthesize nanoparticles of various metals and metal oxides. Applying open or enclosed configurations of the FSP reactor is an efficient tool to control the fuel‐oxidizer ratio in the reaction zone and, thus, the temperature distribution and the particle formation and growth process. In the present work, geometrical setups representing an open and an enclosed flame reactor are compared and their influence on the temperature, velocity, and particle characteristics is investigated. In addition, several distinct kinetic mechanisms for the combustion reactions are evaluated and their effects on the local reactor temperature and gas composition distribution are analyzed. An Eulerian‐Lagrangian approach is adopted to describe the multiphase turbulent gas‐droplet flow and a monodisperse approach based on the population balance equation (PBE) model is implemented to predict the particle formation and evolution. From the open reactor results, the air entrainment mass flow rate of gas into the flame is calculated. Several numerical experiments are performed with the enclosed setup. Supplying an appropriate co‐flow rate into the enclosed reactor results in similar flame behaviour as found for the open reactor configuration. By reducing the co‐flow gas, strong recirculation zones and particle deposition on the enclosure walls are observed. In this situation, the local temperature increases considerably, resulting in larger primary nanoparticle diameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".