MétaCan
Menu
Back to cohort
Record W2133266663 · doi:10.5539/jmsr.v1n4p98

Particle Size of Mechanically Alloyed La0.5Sr0.5Fe0.5Mn0.25Ti0.25O3 Powders Prepared with the Assistance of Ultrasonic Irradiation

2012· article· en· W2133266663 on OpenAlexvenueno aff
Azwar Manaf, Mas Ayu Elita Hafizah

Bibliographic record

VenueJournal of Materials Science Research · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsnot available
FundersUniversitas Indonesia
KeywordsMaterials scienceParticle sizeScanning electron microscopeParticle (ecology)SinteringParticle-size distributionSonicationComposite materialMetallurgyNanoparticleDiffractionUltrasonic sensorChemical engineeringNanotechnologyOptics

Abstract

fetched live from OpenAlex

This paper reports the particle size characterisation of mechanically alloyed La0,5Sr0,5Fe0,5Mn0,25Ti0,25O3 prepared with the assistance of a high-power ultrasonic treatment. After a solid-state reaction on quasi-crystalline powders at 1000°C for 3 hour, the presence of a single phase was confirmed by X-ray Diffraction (XRD). It was found that powder materials derived from mechanical alloying and successive sintering have several disadvantages, namely, that the particle morphology is seldom controllable and results in large variations in the particle size and distribution. A significant improvement in both particle size and distribution was obtained upon subjecting the mechanically milled powder materials to an ultrasonication treatment for a relatively short period of time. As determined by a particle size analyser, the mean particle size gradually decreased from the original size of 6.23 to 1.13mm. A narrow size variation was also observed by Scanning Electron Microscope (SEM). The line broadening analysis by XRD revealed that the particles consist of nanocrystallites with an average size of ~ 22-26 nm. These results indicate that the sintering of mechanically milled particles followed by a high-power ultrasonication treatment promotes the formation of particles containing nanocrystals.

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)0.006
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 score0.013
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.311
Teacher spread0.285 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Materials Science ResearchSame topicAdvanced materials and compositesFrench-language works237,207