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Record W2146844722 · doi:10.1139/cjc-2014-0565

Design of new aliphatic azido nitro compounds as plasticizer: an initial exploration on AFCTEE (1-azido-formic acid 1,1,1-trinitro-ethyl ester)

2015· article· en· W2146844722 on OpenAlexvenueno aff
Junqing Yang, Xuedong Gong, Guixiang Wang

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsChemistryPlasticizerFormic acidEnergetic materialNitroThermal stabilityThermal decompositionDensity functional theoryDecompositionChemical stabilityEthyl esterOrganic chemistryComputational chemistryExplosive material

Abstract

fetched live from OpenAlex

To explore new high-energy azido nitro compounds as plasticizers for propellants, AFCTEE (1-azido-formic acid 1,1,1-trinitro-ethyl ester) was designed and studied using density functional theory. The predicted density of AFCTEE, 1.90 gcm −3 , is comparable to that of HMX (1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane) and much higher than that of general organic azido compounds. AFCTEE possesses higher energetic properties and chemical stability than the promising azido nitro plasticizer DAMNP (1,3-diazido-2-methyl-2-nitropropane) and the conventional plasticizer NG (nitroglycerine), and it has a moderate thermal stability. The pyrolysis of AFCTEE starts from the rupture of C–NO 2 and then the breakage of N–N 2 via Curtius rearrangement. This work is the initial exploration for AFCTEE, aiming at the energetics, spectra (IR, NMR, and UV), stability, and decomposition mechanism. Compared with DAMNP, the advantages of superior energetic properties and chemical stability suggest AFCTEE is a promising energetic azido nitro compound and is worth further investigation.

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 score0.181
Threshold uncertainty score0.749

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 head0.044
GPT teacher head0.236
Teacher spread0.193 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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