High-speed imaging and statistics of puffing and micro-exploding droplets in spray-flame synthesis
Why this work is in the frame
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Bibliographic record
Abstract
Thermally-induced breakup of metal-precursor-laden droplets in spray-flame synthesis occurs via a rapid and disruptive disintegration, i.e., “puffing” and “micro-explosion”. To assess the temporal evolution and statistics of droplet disruption, LED-illuminated droplet shadowgraphs were imaged with a microscope onto a high-speed camera and morphological image analysis was applied. The atomized liquid was a mixture of 35 vol.-% ethanol and 65 vol.-% 2-ethylhexanoic acid mixed with iron(III) nitrate nonahydrate (INN) as a precursor. Droplet evaporation and disruption were also simulated with a population balance model. The model finds solid precipitates forming in the droplets because of the decomposition of the precursor intermediate iron(III) 2-ethylhexanoate. The precipitates form a particle shell, which favors the superheating of the droplets’ interior, and they facilitate heterogeneous bubble nucleation. Imaging experiments and modelling find that per 10 µs lifetime of a droplet, the probability for disruption increases from 5 to 13% and 5 to 19%, respectively, when increasing the INN concentration from 0.05 to 0.5 mole/l. The probability of disruption suggests that throughout their lifetime in the spray flame, nearly all droplets will undergo disruption and many of them multiple times. In the experiment, droplets before disruption are 15% smaller than regular, non-disrupting droplets. Once disrupted, the droplets have a 45% smaller mean diameter than regular droplets. Under all conditions, disrupting and disrupted droplets are slower than regular droplets while the disruption does not significantly accelerate disrupted droplets.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it