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Record W1779203281 · doi:10.1139/cjp-2012-0263

The Fe<sub>3</sub>O<sub>4</sub> nanoparticle doping effect in liquid crystal on electrical and dielectric properties

2013· article· en· W1779203281 on OpenAlexvenueno aff
Oğuz Köysal, Muharrem Gökçen, Mert Yıldırım

Bibliographic record

VenueCanadian Journal of Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsnot available
Fundersnot available
KeywordsDopingDielectricPhysicsCondensed matter physicsBiasingNanoparticleLiquid crystalVoltageAnalytical Chemistry (journal)Dielectric spectroscopyPolarization (electrochemistry)Materials scienceOptoelectronicsElectrodeChemistry

Abstract

fetched live from OpenAlex

Fe 3 O 4 nanoparticles were doped into E63 coded liquid crystal (LC) with 0.1 wt% to investigate the effect of Fe 3 O 4 on the electrical and dielectric properties of LC in a wide range of frequency, bias voltage, and illumination. The frequency, bias voltage, and illumination level dependence of the electrical and dielectric properties of pure E63 and doped mixture (E63–Fe 3 O 4 ) has been investigated using the current–voltage (I–V) and admittance spectroscopy (C–V and G/ω–V) data in the frequency range of 10 kHz – 1 MHz at room temperature. Results show that the increment in current with Fe 3 O 4 doping is due to the metallic nature of the Fe 3 O 4 nanoparticles. The current switching voltage of reorientation is shifted to lower voltage values as a result of Fe 3 O 4 doping. Same behavior is also observed with increasing illumination level. The polarization contribution of Fe 3 O 4 doping is evident considering the values of [Formula: see text] at low frequencies. Moreover, depending on the existence of Fe 3 O 4 doping agent, there is a split in σ ac –f plots where σ ac values increase with frequency, especially at high frequencies.

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)0.001
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 score0.018
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.227
Teacher spread0.214 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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