Effects of oxygen concentration on nanoparticle formation during combustion of iron powders
Notice bibliographique
Résumé
• 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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».