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Record W4317914373 · doi:10.53063/synsint.2022.24135

Synthesis and characterization of aluminum-yttrium perovskite powder using a co-precipitation technique

2022· article· en· W4317914373 on OpenAlexvenueno aff
Sara Ahmadi

Bibliographic record

VenueSynthesis and Sintering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsCalcinationMaterials scienceYttriumOrthorhombic crystal systemPerovskite (structure)PrecipitationScanning electron microscopeChemical engineeringCoprecipitationPhase (matter)Powder diffractionMineralogyNuclear chemistryAnalytical Chemistry (journal)CrystallographyMetallurgyCrystal structureOxideCatalysisChemistryComposite materialChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

In this research the solid solution YAlO3 perovskite powder (YAP) was successfully synthesized by co-precipitation route. Co-precipitation synthesis is simple and inexpensive method which capable to produced more homogeneous powders. In the first step, effect of various mineralizers on YAlO3 formation has been investigated, which resulted in decrease of the formation temperature down to 1200 °C. In the next step, effect of the pH in synthesis procedure and also calcination time and temperature has been studied. Later, the optimum condition for synthesis of single-phase YAP was determined. The obtained powders have been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) as well as ICP analysis. Results show that the most appropriate mineralizer system for the formation of YAlO3 perovskite was NaF:MgF2:Li2CO3 (3:2:1 by weight). Additionally, orthorhombic YAP powders were successfully synthesized in pH=9. Calcination at 1200°C for 4 h was the best condition for preparation single phase Aluminum-Yttrium perovskite crystals.

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 science0.0000.000
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 head0.019
GPT teacher head0.260
Teacher spread0.241 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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