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Record W4416386265 · doi:10.1016/j.matlet.2025.139802

A novel approach to synthesize mixed-metal oxides via direct metal combustion

2025· article· en· W4416386265 on OpenAlexafffund
Kartik Mangalvedhe, Zachary A. Chanoi, Ethan Anderson, Victoria Reyes, Eric McCalla, Evgeny Shafirovich, Samuel Goroshin, Jeffrey M. Bergthorson

Bibliographic record

VenueMaterials Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationFoundation for Angelman Syndrome TherapeuticsCanadian Space AgencyNational Science Foundation
KeywordsSpinelCombustionLaminar flowNanoparticlePorosityAluminiumSpecific surface area

Abstract

fetched live from OpenAlex

A high-surface-area, magnetic iron‑aluminum spinel has been synthesized via direct combustion of iron and aluminum powders in a laminar dust burner. Characterization of the combustion products by SEM, EDAX, XRD, and BET revealed spherical nanoparticles comprised of hercynite (FeAl 2 O 4 ) and gamma-alumina ( γ -Al 2 O 3 ), formed due to high flame temperatures. The products exhibit a high specific surface areas (800 m 2 /g) and high porosity, indicating strong catalytic potential. • A laminar metal-air flame is used to synthesize spinel nanoparticles. • The obtained nanoparticles are porous and have a very high specific surface area. • The proposed method is a one-step, clean process.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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