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Record W2968894637 · doi:10.1139/cjc-2019-0141

Mechanisms and kinetics of initial pyrolysis and combustion reactions of 1,1-diamino-2,2-dinitroethylene from density functional tight-binding molecular dynamics simulations

2019· article· en· W2968894637 on OpenAlexvenueno aff
Dong Xiang, Weihua Zhu

Bibliographic record

VenueCanadian Journal of Chemistry · 2019
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
FundersScience Challenge ProjectNational Natural Science Foundation of China
KeywordsPyrolysisCombustionChemistryActivation energyKineticsKinetic energyMolecular dynamicsThermal decompositionChemical kineticsComputational chemistryThermodynamicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The density functional tight-binding molecular dynamics approach was used to study the mechanisms and kinetics of initial pyrolysis and combustion reactions of isolated and multi-molecular FOX-7. Based on the thermal cleavage of bridge bonds, the pyrolysis process of FOX-7 can be divided into three stages. However, the combustion process can be divided into five decomposition stages, which is much more complex than the pyrolysis reactions. The vibrations in the mean temperature contain nodes signifying the formation of new products and thereby the transitions between the various stages in the pyrolysis and combustion processes. Activation energy and pre-exponential factor for the pyrolysis and combustion reactions of FOX-7 were obtained from the kinetic analysis. It is found that the activation energy of its pyrolysis and combustion reactions are very low, making both take place fast. Our simulations provide the first atomic-level look at the full dynamics of the complicated pyrolysis and combustion process of FOX-7.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.183
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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