MétaCan
Menu
Back to cohort
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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueMaterials LettersSame topicCatalytic Processes in Materials ScienceFrench-language works237,207