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Record W3007301006 · doi:10.1002/prep.202000005

Preparation of Azido Polycarbonates via Bulk Polymerization of Halogenated Diols

2020· article· en· W3007301006 on OpenAlexafffund
Jean‐Christophe St‐Charles, Charles Dubois

Bibliographic record

VenuePropellants Explosives Pyrotechnics · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerPolyesterPolymerizationPropellantMaterials sciencePolymer chemistryTrimethylene carbonateCyclohexanoneCarbonatePolystyreneChemical engineeringCatalysisChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Azido polymers find use as energetic binders in a variety of composite explosive and propellant applications, but few azido polyesters have previously been reported: a method is introduced for the preparation of two azido polycarbonates, poly(2,2’‐bisazidomethyl‐1,3‐propyl carbonate) and poly(3‐azido‐1,2‐propyl carbonate), possible binder candidates for energetics applications. The preparation method for these polymers involves a two‐step synthesis starting from the bulk polymerization of the commercially sourced diols with diphenyl carbonate in the presence of lanthanum (III) acetylacetonate as a neutral catalyst, and subsequent azidation in cyclohexanone. The physical and thermal characteristics of each are reported, indicating properties similar to other azido‐polymers. The thermal and mechanical properties of cured azido polyester resin mixtures are the subject of ongoing research.

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 score0.002
Threshold uncertainty score0.008

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.217
Teacher spread0.204 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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