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Record W2022885323 · doi:10.1002/prep.200300007

Polymer Nanocomposites from Energetic Thermoplastic Elastomers and Alex®

2003· article· en· W2022885323 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePropellants Explosives Pyrotechnics · 2003
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsUniversité LavalDefence Research and Development Canada
Fundersnot available
KeywordsMaterials scienceThermoplastic elastomerComposite materialNanocompositePropellantElastomerThermoplasticEnergetic materialPolymerExplosive materialComposite numberCopolymerAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Polymer Nanocomposites (PNs) obtained from linear energetic copolyurethane thermoplastic elastomers (ETPEs) based on GAP and a commercially available nanometric aluminum (Alex) were characterized. Two methods were performed to prepare the PNs: in‐situ and by solvent evaporation. The thermal and mechanical properties of the pure ETPEs, of the composite ETPE/Al (micrometric) and of the nanocomposite ETPE/Alex were studied. The percentage of Alex was adjusted to obtain the optimum mechanical properties. The beneficial effects of the nanopowder on the material properties are highlighted. The introduction of nanoaluminum improves the elasticity and strength of the original ETPE and, consequently, makes it easier to use, to handle, and to process. It indicates that PNs can be considered for future applications in energetic material, such as in gun propellants, rocket propellants and insensitive melt‐cast explosive formulations.

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.

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 score0.009
Threshold uncertainty score0.835

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 head0.006
GPT teacher head0.163
Teacher spread0.158 · 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