MétaCan
Menu
Back to cohort
Record W2562606271 · doi:10.1139/cjc-2016-0355

New concept for the design of zero-hydrogen energetic materials with high energy and low sensitivity: achieving a good balance among parent compounds, nitro groups, and <i>N</i>-oxides

2016· article· en· W2562606271 on OpenAlexvenueno aff
Qiong Wu, Linghua Tan, Zusheng Hang, Weihua Zhu

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryDetonationTetrazineNitroDensity functional theoryHydrogen bondEnergetic materialStandard enthalpy of formationSensitivity (control systems)HydrogenDecompositionThermal decompositionComputational chemistryRing (chemistry)ThermodynamicsPhysical chemistryOrganic chemistryExplosive materialMolecule

Abstract

fetched live from OpenAlex

A new powerful zero-hydrogen energetic compound DNDOBTT (2,7-dinitro-4N,9N-dioxide-bis[1,2,4]-triazolo)[1,5-b:1′,5′e][1,2,4,5] tetrazine) was produced by a new design concept of achieving a balance among the parent compound, nitro groups, and N-oxides. Its structure and properties was studied by the density functional theory. The breaking of N–N bond in the tetrazine ring is an initial decomposition step of DNDOBTT, and the energy barrier was predicted to be 175 kJ·mol −1 . DNDOBTT has comparable detonation performance with some CHNO energetic compounds, including the most powerful ONC (octanitrocubane), whereas its sensitivity and thermal stability are obviously lower and better than those of ONC, respectively, indicating that DNDOBTT has both the high energy and reduced sensitivity and may be a valuable candidate for experiments. Therefore, a new novel energetic material DNDOBTT with good overall performance has been obtained successfully by the new design concept, and it may be applied to design and develop other novel improved zero-hydrogen energetic 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 score0.064
Threshold uncertainty score0.334

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 head0.005
GPT teacher head0.153
Teacher spread0.149 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicEnergetic Materials and CombustionFrench-language works237,207