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Record W2123946020 · doi:10.1002/prep.200600055

Characterization of Aluminum Powders: II. Aluminum Nanopowders Passivated by Non‐Inert Coatings

2006· article· en· W2123946020 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 · 2006
Typearticle
Languageen
FieldMaterials Science
TopicChemical and Physical Properties of Materials
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsMaterials scienceInertReagentInert gasAluminiumStearic acidChemical engineeringPassivationMetallurgyChemistryNanotechnologyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Results of DTA‐TG investigation and chemical analysis of electro‐exploded aluminum nanopowders, passivated and/or coated with the non‐inert reagents: nitrocellulose (NC), oleic acid (C 17 H 33 COOH) and stearic acid (C 17 H 35 COOH), which were suspended in kerosene and ethanol, amorphous boron, nickel, fluoropolymer, ethanol and air (for comparison), are discussed. Surface protection of aluminum nanopowders by coatings of different origin results in significant advantages in the energetic properties of the powders. Aluminum nanopowders with a protecting surface show increased stability to oxidation in nitrogen, air and in water during storage period. On the basis of the experimental results, a diagram of the formation and stabilization of the coatings is proposed. The kinetics of the interaction of aluminum nanopowders with nitrogen, air and water is discussed. Recommendations concerning the efficiency of non‐inert reagent passivation are proposed on the basis of comprehensive analysis of the experimental data.

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 categoriesMeta-epidemiology (narrow)
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 score0.001
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.202
Teacher spread0.192 · 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