Laser-induced emission from iron oxide nanoparticles in spray-flame synthesis: in situ high-speed microscopy
Bibliographic record
Abstract
Abstract Spray-flame synthesis uses low-cost precursors dissolved in organic solvents to produce functional metaloxide nanoparticles. In the spray flame, the precursor-laden droplets show frequent and intense thermally-induced disruption, so-called puffing and micro-explosion. This process is often correlated with high uniformity of particle sizes. Whether puffing and micro-explosion are also directly associated with the formation or release of iron oxide nanoparticles is not clear. Also, the spatiotemporal evolution of nanoparticles in the turbulent flow field of the flame is largely unknown from experiments. We performed simultaneous high-speed microscopic imaging of droplet shadowgraphs at 360 kHz as well as elastic light scattering (ELS) and laser-induced emission (LIE) of nanoparticles at 40 kHz. Comparing ELS and LIE images allows distinguishing signals from droplets, flame, and nanoparticles, as only the nanoparticles will appear in images from both methods. ELS and LIE show nanoparticles as thin narrow filaments, presumably following the local flow. Nanoparticle filaments are found at a height of 50 mm and more above the burner in the spray flame. The filaments show increasing LIE signal and higher confinement with increasing height above the burner. The appearance of LIE and thus nanoparticles does not directly correlate with the presence of droplets or their disruption.
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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".