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Record W2188998847

Raman Scattering Spectra and Dielectric Relaxation Behavior of Pzt-Pzn-Pmnn Ceramics

2014· article· en· W2188998847 on OpenAlexvenueno aff
Le Dai Vuong, Phan Dinh Gio, Vo Thi Thanh Kieu

Bibliographic record

VenueInternational Journal of Chemistry · 2014
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsnot available
Fundersnot available
KeywordsSinteringDielectricRaman spectroscopyRaman scatteringCeramicFull width at half maximumRelaxation (psychology)Analytical Chemistry (journal)ChemistryCalcinationFerroelectricityMaterials scienceMineralogyComposite materialOpticsOptoelectronicsChromatography
DOInot available

Abstract

fetched live from OpenAlex

The 0.8Pb(Zr0.48Ti0.52)O3–0.125Pb(Zn1/3Nb2/3)O3–0.075Pb(Mn1/3Nb2/3)O3 + 0.7 wt% Li2CO3 + 0.3 wt% Fe2O3ceramics (PZT-PZN-PMnN) have been prepared by two-stage calcination method. The influences of sintering temperature on dielectric properties and Raman scattering in the PZT-PZN-PMnN relaxor ferroelectric ceramics have been investigated in detail. The dielectric studies showed that the degree of diffuseness (γ) increased with the increase of the sintering temperatures from 900 to 1000 °C, indicating that the dielectric relaxation behavior was increased; while at higher sintering temperature above 1000 °C, the γ decreased. The dielectric measurement results are in good agreement with the Raman scattering spectra analysis of the samples. It was found that the value of full wide of half maximum (FWHM) of silent B1+E mode reached the maximum value at the sintering temperature of 1000 oC, which showed similar trends to γ. At sintering temperature of 1000 oC, the highest dielectric constant (Emax) of 23600 and the γ value of 1.84.

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)0.0010.000

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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