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Record W4404505743 · doi:10.1021/acsami.4c13603

Microstructural Thermal Zones in Reaction of Nanoenergetics

2024· article· en· W4404505743 on OpenAlexafffund
B.H. Cha, Anqi Wang, Suyong Kim, Jean-Pierre Hickey, Sili Deng, John Z. Wen

Bibliographic record

VenueACS Applied Materials & Interfaces · 2024
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCombustionMaterials scienceReaction rateThermalActivation energyScalingMicrometerNanoparticleParticle (ecology)NanotechnologyChemical engineeringChemical physicsThermodynamicsPhysical chemistryOpticsChemistryCatalysis

Abstract

fetched live from OpenAlex

Despite the potential of nanoenergetics as promising energy sources with high energy densities and fast energy release, our limited ability to predict combustion speeds restricts the utilization of nanoenergetics. Here, we provide a comprehensive analysis of thermal microstructures subject to heterogeneous reactions and propose a new scaling for combustion wave speeds. To control reaction heterogeneity, two different particle interfacial morphologies of physically mixed and core-shell Al/CuO nanoparticles were synthesized. The combustion dynamics and temperature fields were obtained at micrometer-scale resolutions using high-speed and infrared cameras. Experiments showed that the core-shell Al/CuO exhibiting less reaction heterogeneity had a faster wave speed than the physically mixed counterpart, although the measured chemical reaction rates were lower. By employing the thermal structure analysis, we found that the shortened preheat zone and the lengthened reaction zone, attributed to the lower reaction onset temperature and reaction rate, led to the increased wave speed despite the lower reaction rate in the core-shell Al/CuO. From these analyses, we developed a new scaling that describes the combustion wave speeds in nanoenergetics based on intrinsic properties and thermal structures for both morphologies.

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 score0.007
Threshold uncertainty score0.542

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 head0.202
Teacher spread0.196 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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