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Record W4386727217 · doi:10.18280/rcma.330407

Enhanced Dielectric Characteristics of Cr2O3 Nanoparticles Doped PVA/PEG for Electrical Applications

2023· article· fr· W4386727217 on OpenAlexvenueno aff
Mohammed H. Abbas, Aseel Hadi, Bahaa H. Rabee, Majeed Ali Habeeb, Musaab Khudhur Mohammed, Ahmed Hashim

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2023
Typearticle
Languagefr
FieldMaterials Science
TopicPolymer Nanocomposite Synthesis and Irradiation
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDielectricDopingNanoparticlePEG ratioNanotechnologyChemical engineeringOptoelectronicsBusinessEngineering

Abstract

fetched live from OpenAlex

This study examines the dielectric properties of polyvinyl alcohol (PVA)/polyethylene glycol (PEG) doped with chromium oxide (Cr2O3) nanoparticles, with the aim of leveraging these properties in electronic and electric nanodevices.The effect of Cr2O3 nanoparticle concentration on the dielectric constant, dielectric loss, and AC electrical conductivity of the composites was systematically investigated.The results demonstrate that both the dielectric constant and dielectric loss decrease with increasing frequency, but increase with the concentration of Cr2O3 nanoparticles.Conversely, AC electrical conductivity was found to increase with both frequency and Cr2O3 nanoparticle concentration.The enhanced dielectric properties of the PVA/PEG/Cr2O3 nanocomposites make them suitable for various applications in the field of electronics and energy storage.The study provides new insights into the design of materials for electrical and electronics applications.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.049
GPT teacher head0.299
Teacher spread0.251 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicPolymer Nanocomposite Synthesis and IrradiationFrench-language works237,207