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Record W4413699224 · doi:10.1021/acsnano.5c10174

Combustion Waves and Flame Stability in Nanocomposites

2025· article· en· W4413699224 on OpenAlexafffund
Suyong Kim, Anqi Wang, John Z. Wen, Sili Deng

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsUniversity of Waterloo
FundersMathWorksNatural Sciences and Engineering Research Council of CanadaVolkswagen of America
KeywordsCombustionMaterials scienceNanocompositeStability (learning theory)Composite materialNanotechnologyChemical engineeringChemistryEngineeringComputer sciencePhysical chemistry

Abstract

fetched live from OpenAlex

Combustion in nanocomposites involves intricate coupling between chemical reactions and transport phenomena across multiple scales and phases, complicating the development of unified theories. In this study, we present a theoretical and experimental framework that can serve as the foundation for a unified theory of combustion wave dynamics and instabilities in nanocomposites. Using high-speed microscopic imaging, the flame morphology and combustion wave behavior are characterized across a range of reactivity levels. We find that wave speed correlates more strongly with reactivity than predicted by classical laminar flame theory but also decreases due to combustion instability when reactivity exceeds a certain level. We reveal that this strong correlation is attributed to heterogeneous flame structures altered by nanoparticle sintering. Unstable combustion waves feature highly corrugated flame fronts that are prone to quenching from significant heat loss in sintered nanoparticles. We further validate wave stability analysis for unseen particle morphology using macroscopic observations. These insights lay the groundwork for theory-guided strategies to control combustion wave behavior and enable the design of reactive nanocomposites that move beyond empirical trial-and-error methods.

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 categoriesnone
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 score0.038
Threshold uncertainty score0.236

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)0.0000.000

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 head0.006
GPT teacher head0.197
Teacher spread0.192 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes2
Has abstractyes

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