MétaCan
Menu
Back to cohort
Record W2270054988 · doi:10.1021/acs.jpcc.5b08595

Polymerization of Nitrogen in Ammonium Azide at High Pressures

2015· article· en· W2270054988 on OpenAlexaff
Hongyu Yu, Defang Duan, Fubo Tian, Hanyu Liu, Da Li, Xiaoli Huang, Yunxian Liu, Bingbing Liu, Tian Cui

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
FundersChina Postdoctoral Science FoundationMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsNitrogenPolymerizationAzideAmmoniumChemistryMolecular dynamicsPolymer chemistryAnnealing (glass)Liquid nitrogenMaterials scienceComputational chemistryOrganic chemistryPolymerComposite material

Abstract

fetched live from OpenAlex

By ab initio molecular dynamics simulations, ammonium azide (AA, NH 4 N 3 ) is predicted to be an effective precursor to form polynitrogen. Our simulations at 60 and 90 GPa show that the critical temperatures for nitrogen polymerization are about 2200 and 1600 K, respectively. Compared with molecular nitrogen (110 GPa and 2000 K), the synthesis pressure of polymeric nitrogen in AA significantly lowers. In the obtained polymeric nitrogen compounds, there are kinds of nitrogen backbones: one-dimensional chains, branched chains, and five-membered rings. By annealing simulations at 90 GPa, a one-dimensional pure nitrogen periodic chain is formed. Our finding might open a way for the practical application of polymeric nitrogen compounds as further depressurization simulations at 300 K confirm that both hydrogen-passivated polymeric networks and five-membered rings can be preserved at ambient conditions.

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)0.001
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 score0.002
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicDiamond and Carbon-based Materials ResearchFrench-language works237,207