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Record W2889875927 · doi:10.1002/cplu.201800414

Synthesis and Investigation of 2,3,5,6‐Tetra‐(1<i>H</i>‐tetrazol‐5‐yl)pyrazine Based Energetic Materials

2018· article· en· W2889875927 on OpenAlexaff
Tomasz G. Witkowski, Paul Richardson, Bulat Gabidullin, Anguang Hu, Muralee Murugesu

Bibliographic record

VenueChemPlusChem · 2018
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsDefence Research and Development CanadaUniversity of Ottawa
Fundersnot available
KeywordsChemistryDetonationAlkali metalInorganic chemistryRubidiumCaesiumDetonation velocityEnergetic materialIonic bondingPyrazineThermal stabilityMetalInfrared spectroscopyPhysical chemistryPotassiumIonOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The structures and properties of several energetic compounds based on a high‐nitrogen‐content anion, namely 2,3,5,6‐tetra(1H‐tetrazol‐5‐yl)pyrazine (H4TTP) are reported here for the first time. These energetic salts were synthesized by reacting H4TTP with various alkali metal hydroxides (sodium, potassium, rubidium, caesium) and N‐based (ammonia, hydrazine, hydroxylamine, guanidine carbonate, aminoguanidine bicarbonate). The resulting materials were comprehensively characterized by multinuclear (1H, 13C) NMR spectroscopy, infrared spectroscopy, elemental analysis, DSC, as well as low‐temperature single‐crystal X‐ray diffraction. Heats of formation for the metal‐free species as well as detonation parameters were calculated. The presented energetic materials (EMs) show high thermal stability (207 °C≤Tdec≤300 °C), while the metal‐free ionic derivatives exhibit desirable properties such as detonation velocity (6873 m s−1≤VC‐J≤8364 m s−1), detonation pressure (14.3 GPa≤pC‐J≤24.9 GPa), and specific impulse (141.4≤Isp≤192.5 s).

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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 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

Citations13
Published2018
Admission routes1
Has abstractyes

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