Visualization and image analysis of droplet puffing and micro-explosion in spray-flame synthesis of iron oxide nanoparticles
Bibliographic record
Abstract
Abstract Combusting metal precursor-laden droplets, required in spray-flame synthesis of nanomaterials, are known to undergo a rapid and disruptive disintegration, i.e., puffing and micro-explosion. In this work, imaging with high spatiotemporal resolution and image-analysis routines were developed to investigate droplet disruption in spray-flame synthesis of metal oxides. Droplet shadowgraphs were imaged on a high-speed camera. The solvent was a mixture of 35 vol% ethanol and 65 vol% 2-ethylhexanoic acid which (in some cases) was mixed with a 0.2 mol/l iron(III) nitrate nonahydrate precursor. Photometric and morphological processing identified in-focus features, estimated their size, velocity, and circularity, and discriminated regular, spherical droplets from disrupting ones. While solely regular droplets were found in the spray flame of pure solvent, with the precursor/solvent mixture, disrupting droplets were found in addition to the regular droplets. Disruption events were phenomenologically classified into puffing, comprising droplet deformation and local eruption, and micro-explosion, the violent disintegration of the droplet into multiple fragments. Puffing was found to occur much more frequently than micro-explosions. Disrupting droplets had a 32% smaller Sauter mean diameter than regular droplets, indicating that disruptions are beneficial for rapid spray evaporation. At 40 and 50 mm heights above the burner, about 8 and 6%, respectively, of the in-focus droplets are disrupting per millimeter axial distance. Thus, throughout their lifetime in the spray flame, all precursor-laden droplets are expected to experience disruption. Graphical abstract
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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 | 0.000 |
| 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 | 0.000 | 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".