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Record W2551352395 · doi:10.1139/cjc-2016-0512

Acidic ionic liquids and green and recyclable catalysts in the clean nitration of TAIW to CL-20 using HNO<sub>3</sub> electrolyte

2016· article· en· W2551352395 on OpenAlexvenueno aff
Kai Wang, Bo Dong, Chao-fei Yang, Hua Qian

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsnot available
FundersNanjing UniversityNanjing University of Science and TechnologyNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsChemistryNitrationIonic liquidElectrolyteCatalysisYield (engineering)Inorganic chemistryGreen chemistryOrganic chemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

A novel method for the synthesis of CL-20 by nitration of TAIW was investigated. HNO 3 electrolyte, containing generated dinitrogen pentoxide and unreacted dinitrogen tetraoxide, was directly used as a nitrating agent and the result was encouraging. A series of SO 3 H-functionalized ionic liquids were utilized to further improve the result. The satisfactory yield of CL-20 (94%) makes it a useful method for the green and clean synthesis of CL-20.

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 score0.007
Threshold uncertainty score0.315

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 head0.204
Teacher spread0.194 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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