MétaCan
Menu
Back to cohort
Record W2178745903 · doi:10.1080/10426914.2015.1004707

Modeling and Experimental Validation of Deagglomeration of Ultrafine Nanoparticles in Liquid Al During Noncontact Ultrasonic Casting of Al–Al<sub>2</sub>O<sub>3</sub> Nanocomposite

2015· article· en· W2178745903 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

VenueMaterials and Manufacturing Processes · 2015
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials scienceNanocompositeUltrasonic sensorCastingMoldLiquid metalDissolutionComposite materialNanoparticleUltrasoundMetallurgyNanotechnologyAcousticsChemical engineering

Abstract

fetched live from OpenAlex

Noncontact ultrasonic casting of nanocomposite has advantages over the contact method. Some of the advantages are (a) relatively uniform intensity of ultrasonic wave within the mold and (b) no dissolution of metal from the probe into the liquid metal. It also has disadvantages over the contact method. Since the ultrasonic action and cooling cum solidification occur simultaneously one needs to ensure completion of deagglomeration before the initiation of solidification. In the current study mathematical models of mold cooling cum solidification and deagglomeration have been developed to identify correct conditions for the noncontact ultrasonic casting. Using this approach a combination of casting parameters that will ensure complete deagglomeration of nanodispersoid was identified and Al–Al2O3 nanocomposite, in which Al2O3 nanoparticles are separated from each other, was successfully cast using noncontact ultrasonic casting.

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 score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.213
Teacher spread0.200 · 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