Hybrid analytical finite element method for dielectric response of PE/TiO<sub>2</sub> nanodielectric materials
Bibliographic record
Abstract
Abstract An accurate three-dimensional (3D) model was developed using hybrid analytical finite element method (H-FEM) for the simulation of frequency-domain dielectric response of low-density polyethylene (PE) filled with titanium dioxide (TiO 2 ) nanoparticles. The input values of dielectric permittivity of nanoparticle and interphase were calculated analytically using the mixture model and adjusted by an optimization procedure. The effective permittivity of PE/TiO 2 nanocomposites was then modelled by COMSOL Multiphysics. The model output results agreement with the experimental values indicate that the developed H-FEM 3D model is suitable for use in solving dielectric response problems of different nanodielectric materials in frequency domain. Furthermore, the simulation results also offer further understanding into the effect of the nanoparticle interphase on the final dielectric properties of the nanodielectric materials.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Finite element modeling of dielectric response of polyethylene/TiO2 nanocomposites; the object is a material property.
The work develops a finite-element model of nanodielectric materials, not a study of research.
Materials modeling of nanodielectric permittivity; engineering physics, not research studies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".