MétaCan
Menu
Back to cohort
Record W1892003524 · doi:10.1088/0964-1726/20/4/045005

Study of electrical properties and the magnetoelectric effect in Ni<sub>0.2</sub>Co<sub>0.8</sub>Fe<sub>2</sub>O<sub>4</sub>+ PbZr<sub>0.8</sub>Ti<sub>0.2</sub>O<sub>3</sub>particulate composites

2011· article· en· W1892003524 on OpenAlexfundno aff
B. K. Bammannavar, Lohit Naik, R.K. Kotnala

Bibliographic record

VenueSmart Materials and Structures · 2011
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsMaterials scienceLead zirconate titanateElectrical resistivity and conductivityDielectricScanning electron microscopeDissipation factorFerrite (magnet)Analytical Chemistry (journal)Magnetoelectric effectComposite materialDiffractionFerroelectricityMultiferroicsElectrical engineeringChemistryOpticsPhysics

Abstract

fetched live from OpenAlex

The electric properties and magnetoelectric effect in magnetoelectric (ME) composites with composition (x) Ni0.2Co0.8Fe2O4 + (1 − x)PbZr0.8Ti0.2O3 (PZT—lead zirconate titanate) were studied. The presence of single- (x = 0 and 1) and bi-phases (x = 0.15, 0.30 and 0.45) in the composites was confirmed by x-ray diffraction (XRD) measurements. A structural analysis of the composites was carried out by scanning electron microscopy (SEM) measurements. Electric resistivity measurement as a function of temperature shows the semiconducting nature of the samples. The dielectric constant (ε') and loss tangent (tanδ) decrease rapidly at lower frequencies and remain constant at higher frequencies for all the samples. An AC conductivity measurement was used to discuss the conduction mechanism in all the samples. The ME voltage coefficient (dE/dH)H measured as a function of DC magnetic field gives the maximum ME output of 778 µV cm−1 Oe−1 for 15% of ferrite in the composites.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueSmart Materials and StructuresSame topicMultiferroics and related materialsFrench-language works237,207