Fluctuating Flame from Suspending Ferrimagnetic Core/Shell Al@Fe<sub>3</sub>O<sub>4</sub> Nanoparticles in a Magnetic Field
Bibliographic record
Abstract
Abstract Core–shell fuel@oxidizer nanocomposite can combust in oxygen‐starved environment. A ferrimagnetic reactive particle, being successfully engineered, allows for manipulation using an external magnetic field and facilitates target heat or gas production. This research reports on interesting flame dynamics of newly synthesized core–shell Al@Fe3O4 nanoparticles under the influence of a magnetic field. Serving as an oxidizer and a functional ferrimagnetic component, iron oxide nanoparticles (IONPs) are grown in situ on nano‐sized Al (n‐Al) particles. Electron microscopic images demonstrate nearly monodispersed IONPs ≈7 nm decorating the surface of n‐Al. X‐ray diffractogram and X‐ray photoelectron spectroscopy confirm the formation of Fe3O4. Thermal analysis suggests the as‐prepared core–shell particles predominantly go through a solid‐state reaction mechanism that exhibits 30% lower activation energy compared to physically‐mixed nanocomposite (215.0 vs 310.8 kJ mol−1). The core/shell particles can be ignited and combust under laser irradiation with or without the effect of a magnetic field. When suspended in the middle of the tube by the magnetic field, an interesting combustion process is observed, highlighting a grow‐shrink‐grow flame resulted from the interactions between the combustion products and the external magnetic field, as well as backfiring at the bottom of the sample without major changes in burning rate and ignition delay.
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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".