Dielectric Properties of Paper Made from Pulps Loaded with Ferroelectric Particles
Bibliographic record
Abstract
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 (BaTiO3and SrTiO3) 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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".