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Record W2415248383 · doi:10.1139/cjc-2016-0174

Searching for a new family of high energy explosives by introducing N atoms, <i>N</i>-oxides, and NO<sub>2</sub> into a cage adamantane

2016· article· en· W2415248383 on OpenAlexvenueno aff
Dong Xiang, Hao Chen, Weihua Zhu, Heming Xiao

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsDetonationExplosive materialAdamantaneChemistryCageNitroDiamondoidDensity functional theoryStandard enthalpy of formationOxideSensitivity (control systems)Computational chemistryCrystallographyPhysical chemistryOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

A design strategy that including N atoms, N-oxides, and nitro groups into a cage azaadamantane at the same time was used to design 10 polyazaoxyadamantanes (PAOAs) and eight polynitroazaoxyadamantanes (PNTAOAs). First, four stable azaadamantanes were built by replacing the tertiary C atoms of an adamantane with N atoms. Then, 10 PAOAs were designed by introducing one to four N-oxides into the four azaadamantanes. After that, eight PNTAOAs were formed when the H atoms of four N-oxide-substituted azaadamantanes were replaced with different numbers of nitro groups. Finally, their heats of formation, densities, detonation properties, and impact sensitivity were estimated by using density functional theory. Among the eight PNTAOAs, seven compounds had better detonation performances than CL-20, the outstanding, novel, high-energy, and relatively insensitive cage explosive. Two compounds had higher detonation performance and lower sensitivity than CL-20 and HMX, suggesting that their overall performances are outstanding and they may be considered as the potential candidate of high-energy explosives.

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 score0.014
Threshold uncertainty score0.390

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 head0.004
GPT teacher head0.161
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

Citations10
Published2016
Admission routes1
Has abstractyes

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