MétaCan
Menu
Back to cohort
Record W2010687265 · doi:10.1063/1.3686273

Shock initiation of powder mixtures of aluminum with dense metal oxides

2012· article· en· W2010687265 on OpenAlexaff
F. X. Jetté, Samuel Goroshin, David L. Frost, Fan Zhang

Bibliographic record

VenueAIP conference proceedings · 2012
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsDefence Research and Development CanadaMcGill University
Fundersnot available
KeywordsMaterials scienceOxideAluminiumMetalMelting pointPorosityMixing (physics)Reactive materialSofteningStoichiometryMetal foamPorous mediumChemical engineeringComposite materialMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Strong and dense structural reactive materials may be produced by mixing aluminum powders with heavy metal-oxide powders (such as Bi2O3, PbO, Pb3O4, I2O5, etc.). The addition of certain additives to such mixtures, such as V2O5 and B2O3, can lower the softening point of the oxide mixture below the melting point of aluminum. This could lead to the fabrication of dense and nonporous aluminum-metal oxide structural materials. The shock sensitivity of aluminum-metal oxide mixtures was investigated in this work. The minimum shock initiating pressure was obtained for various porous and non-porous aluminum-metal oxide mixtures using the shock recovery technique. Since most reactions of Al in metal oxide mixtures produce little pressure and material velocity changes but large increases in temperatures, thermocouples were used to observe the bulk reaction onset, which relates to the overall reaction rate, in those mixtures. The mixtures tested were found to be very sensitive to shock initiation and their reaction rates were found to be very fast, compared to other types of reactive powder mixtures. Finally, the addition V2O5 and B2O3 additives or the addition of liquid heptane (to fill the pores) did not significantly lower the sensitivity or reaction rates of the mixtures investigated.

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 score0.014
Threshold uncertainty score0.327

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicEnergetic Materials and CombustionFrench-language works237,207