Tuning the Dielectric Response in a Nanocomposite Material through Nanoparticle Morphology
Bibliographic record
Abstract
The introduction of metal cluster dopants and molecular-scale inclusions in metal oxide matrices provides an opportunity for exploring new high-k solid-state dielectrics with tunable response. The quantum properties of molecular nanoparticles depend strongly on their size and shape, a characteristic that can be exploited in changing the response properties of a material, while the small nanoparticle size can help limit the usual issues of conduction and leakage. Here, we model the polarization of molecular-scale silver inclusions in magnesium oxide, using the Modern Theory of Polarization and Car-Parinello Molecular Dynamics (CPMD). Several trends are considered, including nanoparticle size, shape and orientation relative to the applied field. Dielectric permittivity enhancements of 30-100% were observed with inclusion sizes varying from 8 to 32 atoms, considering both rod-like and disk-like inclusions, with alignment either parallel or perpendicular to the external field. Currents calculated using the modern theory of polarization with periodic boundary conditions can experience box edge jumps due to the distortion of the matrix during the simulations - an approach for addressing these issues in CPMD calculations is outlined within.
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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".