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Record W4292513068 · doi:10.3390/thermo2030016

Group Combustion of Dispersed Spherical Core–Shell Nanothermite Particles

2022· article· en· W4292513068 on OpenAlexaff
Mustafa Mutiur Rahman, Ahmed Saieed, Muhammad Fasahat Khan, Jean-Pierre Hickey

Bibliographic record

VenueThermo · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCombustionExothermic reactionCharParticle (ecology)DiffusionShell (structure)Materials scienceChemical engineeringChemistryThermodynamicsComposite materialOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The group combustion characteristics of core–shell nanothermite particles differ from other dispersed solid or liquid fuels. In a core–shell structure, each discrete nanothermite particle can undergo an exothermic reaction as the oxygen atoms in the metal oxide shell undergo a solid state diffusion to oxidize the metal core. This feature allows the spherical core–shell nanothermites to react in the absence of gaseous oxygen, thus modifying their group combustion characteristics compared to char or liquid fuels. Using a number of simplifying assumptions, a theoretical framework was established—based on existing group combustion theory—to examine the characteristics of mass and heat diffusion in nanothermite combustion. First, a model for the quasi-steady state single-particle combustion, in quiescent air, was established. The isolated particle combustion theory serves as the basis for the combustion interaction and mass transfer in a spherical cloud of dispersed nanothermite particles. The type of group combustion is strongly dependent on the diffusion of vapour products, i.e., the interaction is more pronounced when the diffusion of vapour products is higher. The group combustion regimes in dispersed nanothermites were identified and delineated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.191
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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