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Record W4411995606 · doi:10.1021/acs.jpca.5c02241

Molecular Dynamics Study on the Decomposition of IHEM-1 under Impact Loading: Comparison with Traditional Energetic Materials

2025· article· en· W4411995606 on OpenAlexaff
Xifeng Liang, Jiaqiang Wang, Zhaijun Lu, Lichun Bai

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2025
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Hunan Province
KeywordsTATBDetonationExplosive materialDecompositionMolecular dynamicsDetonation velocityMaterials scienceEnergetic materialChemical physicsMoleculeSensitivity (control systems)Energy densityNanotechnologyChemistryComputational chemistryEngineering physicsPhysicsOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

IHEM-1 is a type of novel insensitive high-energy molecule that is crucial for offering high energy density while minimizing sensitivity. Currently, a thorough understanding of its physicochemical properties under extreme impact conditions has rarely been reported, raising a significant challenge for its wide applications. In this study, the impact responses and decomposition behaviors of IHEM-1 are investigated by using molecular dynamics simulations. Unlike conventional high explosives such as CL-20, HMX, TATB, and TNT, the decomposition of IHEM-1 is driven by the cleavage of its N–OH bond, rather than the typical X–NO 2 bond. This distinct “trigger bond” initiates the formation of H 2 O as the primary product, which leads to water production under varying impact velocities. Interestingly, the correlation between the gas production and detonation performance suggests that k gas can serve as a reliable predictor of detonation characteristics ( D v and P ). These findings provide insights into the decomposition mechanisms of IHEM-1 and offer valuable guidance for designing safer high-energy-density materials.

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 score0.340
Threshold uncertainty score0.232

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 routes1
Has abstractyes

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