MétaCan
Menu
Back to cohort
Record W2019776741 · doi:10.1063/1.4790326

Structure and multiferroic properties of Bi(1-x)DyxFe0.90Mg0.05Ti0.05O3 solid solution

2013· article· en· W2019776741 on OpenAlexaff
Nan Li, Jiangtao Wu, Yaqi Jiang, Zhaoxiong Xie, Lan‐Sun Zheng, Zuo‐Guang Ye

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsSimon Fraser University
FundersNational Natural Science Foundation of China
KeywordsMultiferroicsSolid solutionMaterials scienceCrystallographyMetallurgyChemistryFerroelectricityDielectricOptoelectronics

Abstract

fetched live from OpenAlex

Chemical modification is proven to be an effective way to improve the properties of perovskite BiFeO3 (BFO). In this paper, we studied the effects of the A-site Dy3+ ion substitution on the structure and multiferroic properties of BFO in which the Fe3+ ion on the B-site is partially co-substituted for by Mg2+ and Ti4+ ions. The solid solution compounds of Bi(1-x)DyxFe0.90Mg0.05Ti0.05O3 (x = 0 – 1) were synthesized by a tartaric acid-assisted solution process, followed by thermal treatments at 850 °C. The structural transition and the ferroelectric and magnetic properties as a function of composition were investigated. It is found that the structure transforms from a rhombohedral (R3c) to an orthorhombic phase (Pn21a) when x is increased to ≥ 0.15. The ferroelectric measurements indicate that the sample with x = 0.15 possesses the best ferroelectric property, and the remnant polarization of the samples with orthorhombic symmetry decreases with the increase of Dy3+ concentration. The compounds of Bi(1-x)DyxFe0.90Mg0.05Ti0.05O3 (x = 0.05 – 1.00) exhibit weakly ferromagnetic properties at low temperatures, but antiferromagnetic behavior at high temperatures. The magnetization of the solid solution increases linearly with the increase of the Dy3+ amount.

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)0.000
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 score0.006
Threshold uncertainty score0.523

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 communication0.0000.000
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 head0.018
GPT teacher head0.230
Teacher spread0.213 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicMultiferroics and related materialsFrench-language works237,207