Effects of oxygen concentration on nanoparticle formation during combustion of iron powders
Bibliographic record
Abstract
• This research examined the formation of products from the combustion of powdered iron. • Products were magnetite/hematite micro-particles and agglomerated hematite nano-particles. • Micro-particles were spherical and bigger than their iron particle precursors. • Nano-particles were 30–100 nm spherules accounting for up to 7.4% of the product mass, increasing with [O 2 ] in the gas. • Iron evaporation and nano-particle formation mechanisms were evaluated with a heuristic model. Powdered iron is being investigated for its potential use as a carbon-free fuel due to its ability to burn heterogeneously and produce oxide particles, which can be collected, reduced back to iron and burned again. However, high temperature oxidation of iron particles can induce partial vaporization/decomposition and evolution of nanometric iron oxide particles. To investigate the formation process of nanoparticles in iron combustion, iron powders (consisting of spheroidal 45–53 µm particles) were injected in an electrically-heated drop tube furnace, operated at a maximum gas temperature of 1375 K, where they experienced high heating rates (10 4 K/s). The particles reacted with oxygen at concentrations of 15, 21, 35, 50 and 100 % by volume in nitrogen diluent gas. Particles ignited and burned brightly, with peak temperatures reaching 2344–2884 K, depending on the oxygen concentration. The observed distribution of the combustion products of iron was bimodal in size and composition, containing (a) dark gray spherical micrometric particles bigger than their iron particle precursors composed of both magnetite and hematite, and (b) highly agglomerated orange-reddish nanometric particles composed of hematite. The mass fraction of nanometric particles accounted for up to 1.7–7.4 % of the collected products, increasing with the oxygen partial pressure. The nanometric particles were spherules, 30–100 nm in diameter. However, they were highly agglomerated with aggregate aerodynamic diameters peaking at 180–560 nm. The yield of nanoparticles increased with increasing oxygen concentration in the furnace. A heuristic model was used to investigate the impact and sensitivity of various strategies for modeling evaporation, aiming to identify key mechanisms that limit the evaporation rate. This study highlights that understanding the type of liquid at the particle surface is crucial, as evaporation can increase significantly with a homogeneous liquid Fe-O particle compared to a core–shell morphology. Additionally, the analysis suggests that evaporation likely occurs in an intermediate regime where gaseous Fe-containing species oxidize in the boundary layer. Understanding these boundary layer processes is essential for accurately modeling the evaporation rate while maintaining computational efficiency.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".