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Record W2282754025 · doi:10.1504/ijnp.2015.073731

Investigations on structural, optical, morphological and electrical properties of nickel oxide nanoparticles

2015· article· en· W2282754025 on OpenAlexaff
Suresh Sagadevan, Jiban Podder

Bibliographic record

VenueInternational Journal of Nanoparticles · 2015
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNon-blocking I/ONickel oxideMaterials scienceScanning electron microscopeNanoparticleTransmission electron microscopyAnalytical Chemistry (journal)Band gapDielectricOxideNanotechnologyChemistryOptoelectronicsMetallurgyComposite material

Abstract

fetched live from OpenAlex

Nickel oxide (NiO) nanoparticles have been prepared by chemical co-precipitation method. The synthesised nanoparticles were investigated by X-ray diffraction analysis (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV-visible spectroscopy and dielectric studies. The formation of NiO nanoparticles was confirmed by X-ray diffraction (XRD). The morphology and size of the NiO nanoparticles were characterised using scanning and transmission electron microscopy (SEM and TEM). The optical properties were studied by the UV-visible absorption spectrum. The dielectric properties of NiO nanoparticles were studied in the frequency range of 50 Hz5 MHz at different temperatures. Further, electronic properties, such as valence electron plasma energy, average energy gap or Penn gap, Fermi energy and electronic polarisability of the NiO nanoparticles were calculated. The AC conductivity of the NiO nanoparticles increases with increase in temperature and frequency. The activation energy was calculated from AC conductivity studies.

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 score0.002

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 head0.062
GPT teacher head0.286
Teacher spread0.224 · 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

Citations84
Published2015
Admission routes1
Has abstractyes

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