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Record W2346700674 · doi:10.1155/2016/3982572

Dielectric Properties of Paper Made from Pulps Loaded with Ferroelectric Particles

2016· article· en· W2346700674 on OpenAlexafffundabout
Hind Omari, Adel Zyane, Ahmed Belfkira, Moha Taourirte, François Brouillette

Bibliographic record

VenueJournal of Nanomaterials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialDielectricPulp (tooth)

Abstract

fetched live from OpenAlex

Due to its physical properties and its ease of manufacture, paper is widely used in various engineering applications such as electrical insulation materials for components in high voltage technology. In this study, paper loaded with ferroelectric nanoparticles (BaTiO 3 and SrTiO 3 ) was made with fibers obtained from plants growing on the Moroccan soil [Halfa ( Stipa tenacissima ), Agave ( Agave americana ), Pennisetum ( Pennisetum alopecuroides ), Typha ( Typha latifolia ), and Junc ( Juncus effusus )] and two commercial pulps (bleached softwood Kraft and newsprint grade thermomechanical pulps). A retention aid, cation polyacrylamide (Percol 292), was necessary to retain ferroelectric particles in the fibrous network and improve the dispersion of strontium titanate particles. The different pulp and handsheets used were characterized according to standard methods (Pulp and Paper Technical Association of Canada, PAPTAC). It is well known that annual and perennial plants contain high percentages of fines (length < 0.2 mm) and short fibers. The results show that there is a strong interdependence between the dielectric properties of the loaded paper and surface finish, porosity, dispersion level of ceramic particles, fines content, shape, conformability, and sheet formation. The single dielectric relaxation detected towards low frequencies is attributed to hydroxyl groups present on fiber surfaces, in ceramic particles and adsorbed water.

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 score0.002
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.249
Teacher spread0.222 · 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

Citations13
Published2016
Admission routes3
Has abstractyes

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