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Record W2163021585 · doi:10.1186/1556-276x-8-184

Characterization of thermochemical properties of Al nanoparticle and NiO nanowire composites

2013· article· en· W2163021585 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanoscale Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsDefence Research and Development CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermiteMaterials scienceNon-blocking I/ONanowireNanoparticleComposite numberMicrostructureComposite materialNanochemistryChemical engineeringHydrothermal circulationNanotechnologyAluminiumCatalysis

Abstract

fetched live from OpenAlex

Thermochemical properties and microstructures of the composite of Al nanoparticles and NiO nanowires were characterized. The nanowires were synthesized using a hydrothermal method and were mixed with these nanoparticles by sonication. Electron microscopic images of these composites showed dispersed NiO nanowires decorated with Al nanoparticles. Thermal analysis suggests the influence of NiO mass ratio was insignificant with regard to the onset temperature of the observed thermite reaction, although energy release values changed dramatically with varying NiO ratios. Reaction products from the fuel-rich composites were found to include elemental Al and Ni, Al2O3, and AlNi. The production of the AlNi phase, confirmed by an ab initio molecular dynamics simulation, was associated with the formation of some metallic liquid spheres from the thermite reaction.

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 score0.139
Threshold uncertainty score0.609

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.261
Teacher spread0.226 · 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