Silver Nanowire/MnO<sub>2</sub> Nanowire Hybrid Polymer Nanocomposites: Materials with High Dielectric Permittivity and Low Dielectric Loss
Bibliographic record
Abstract
This study reports the fabrication of hybrid nanocomposites based on silver nanowire/manganese dioxide nanowire/poly(methyl methacrylate) (AgNW/MnO 2 NW/PMMA), using a solution casting technique, with outstanding dielectric permittivity and low dielectric loss. AgNW was synthesized using the hard-template technique, and MnO 2 NW was synthesized employing a hydrothermal method. The prepared AgNW:MnO 2 NW (2.0:1.0 vol %) hybrid nanocomposite showed a high dielectric permittivity (64 at 8.2 GHz) and low dielectric loss (0.31 at 8.2 GHz), which are among the best reported values in the literature in the X-band frequency range (8.2–12.4 GHz). The superior dielectric properties of the hybrid nanocomposites were attributed to (i) dimensionality match between the nanofillers, which increased their synergy, (ii) better dispersion state of AgNW in the presence of MnO 2 NW, (iii) positioning of ferroelectric MnO 2 NW in between AgNWs, which increased the dielectric permittivity of nanodielectrics, thereby increasing dielectric permittivity of the hybrid nanocomposites, (iv) barrier role of MnO 2 NW, i.e., cutting off the contact spots of AgNWs and leading to lower dielectric loss, and (v) AgNW aligned structure, which increased the effective surface area of AgNWs, as nanoelectrodes. Comparison of the dielectric properties of the developed hybrid nanocomposites with the literature highlights their great potential for flexible capacitors.
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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".