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Record W1687862233 · doi:10.1063/1.2338829

Hysteresis in acoustic properties of ferroelectric relaxor Pb[(Zn1∕3Nb2∕3)0.955Ti0.045]O3 single crystals studied by Brillouin and dielectric spectroscopies

2006· article· en· W1687862233 on OpenAlexaff
Jae‐Hyeon Ko, Do Han Kim, Seiji Kojima, Wenzhi Chen, Zuo‐Guang Ye

Bibliographic record

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials scienceFerroelectricityCondensed matter physicsDielectricBrillouin zoneBrillouin scatteringPhase transitionRelaxation (psychology)HysteresisBrillouin SpectroscopyOpticsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Acoustic and dielectric properties of Pb[(Zn1∕3Nb2∕3)1−xTix]O3 (PZN-xPT) single crystals with x=0.045 have been studied by the high-resolution micro-Brillouin scattering and dielectric spectroscopy in a wide temperature range. The softening of the Brillouin shift and the increase of dielectric relaxation time upon cooling indicated the formation of polar nanoregions (PNRs) and the slowing down of their dynamics. In contrast to the acoustic properties of typical model relaxors such as lead magnesioniobate, the change in the Brillouin shift near its minimum became sharper on heating compared to the change on cooling, pointing to the clear existence of hysteresis in the dynamics of the diffuse phase transition in PZN-4.5%PT. Since the number of PNRs will increase upon cooling, it may be expected that the kinetics of the phase transition would become slower, the lower the transition temperature resulting in the more sluggish, broad feature of the Brillouin shift observed during cooling. This result may indicate that the number and size of polar nanoregions, which are dependent on temperature, play an important role in the development of the mesoscopic ferroelectric order in PZN-4.5%PT.

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)0.001
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.205
Teacher spread0.193 · 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.

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

Citations28
Published2006
Admission routes1
Has abstractyes

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