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Record W4221083653 · doi:10.1139/cjc-2021-0223

Thermal kinetics, thermodynamics, decomposition mechanism, and thermal safety performance of typical ammonium perchlorate-based molecular perovskite energetic materials

2022· article· en· W4221083653 on OpenAlexvenueno aff
Erhai An, Shao‐Li Chen, Xiaoxia Li, Yingxin Tan, Xiong Cao, Peng Deng

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
Fundersnot available
KeywordsAmmonium perchlorateChemistryThermodynamicsThermal decompositionEnthalpyDifferential scanning calorimetryPhysical chemistryGibbs free energyEnergetic materialPerovskite (structure)NucleationKineticsCalorimetryDecompositionCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

In this work, we report the thermal kinetics, thermodynamics, and decomposition mechanism of AP-based molecular perovskite energetic materials and estimate their thermal safety performance. Typical AP-based molecular perovskite energetic materials, (H 2 dabco)[NH 4 (ClO 4 ) 3 ] (DAP-4), (H 2 pz)[NH 4 (ClO 4 ) 3 ](PAP-4), (H 2 mpz)[NH 4 (ClO 4 ) 3 ](PAP-M4), and (H 2 hpz)[NH 4 (ClO 4 ) 3 ] (PAP-H4), were synthesized and characterized. These were studied using differential scanning calorimetry (DSC). The results show that all of the obtained AP-based molecular perovskite energetic materials have higher thermal decomposition temperatures, and the peak temperatures are more than 360 °C. All follow random nucleation and growth models. Other thermodynamic parameters, such as the reaction enthalpy (ΔH), entropy change (ΔS), and Gibbs free energy (ΔG), show that they are generally thermodynamically stable. Moreover, their adiabatic induced temperatures were obtained; T D 24 of DAP-4, PAP-4, PAP-M4, and PAP-H4 were 246.6, 201.2, 194.5, and 217.5 °C, respectively. This study offers an important and in-depth understanding of the thermal decomposition characteristics of AP-based molecular perovskite energetic materials and their potential applications.

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 score0.126
Threshold uncertainty score0.882

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)0.0010.000

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 head0.003
GPT teacher head0.159
Teacher spread0.157 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

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