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Record W2764037458 · doi:10.1021/acs.cgd.7b00603

Microwave-Assisted Hydrothermal Synthesis of BiFe<sub><i>x</i></sub>Cr<sub>1–<i>x</i></sub>O<sub>3</sub> Ferroelectric Thin Films

2017· article· en· W2764037458 on OpenAlexafffund
Gitanjali Kolhatkar, Reji Thomas, Andreas Ruëdiger

Bibliographic record

VenueCrystal Growth & Design · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsFerroelectricityX-ray photoelectron spectroscopyMaterials sciencePiezoresponse force microscopyRaman spectroscopyHydrothermal synthesisHydrothermal circulationThin filmStoichiometryAnnealing (glass)Analytical Chemistry (journal)MineralogyChemical engineeringNanotechnologyDielectricChemistryPhysical chemistryOpticsOptoelectronicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

We synthesize ferroelectric BiFe x Cr 1– x O 3 thin films by microwave-assisted hydrothermal synthesis on (111) oriented Nb-doped SrTiO 3 substrates. Bi(NO 3 ) 3, Fe(NO 3 ) 3, Cr(NO 3 ) 3, and KOH are used in the precursor solution, along with deionized water. Combination of Raman and X-ray photoelectron spectroscopies confirmed the incorporation of chromium into the rhombohedral structure of BiFeO 3 . The effect of deposition time on the surface morphology and the ferroelectric properties of the films are analyzed. This reveals that if the deposition goes on beyond completion of the reaction (10 min at 120 W), the ferroelectric properties of the film deteriorate due to stoichiometry changes. Through piezoresponse force microscopy, we show that Cr significantly deteriorates the film retention, which cannot be recovered by thermal annealing.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2017
Admission routes2
Has abstractyes

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