Characterization of Dielectric Nanocomposites with Electrostatic Force Microscopy
Bibliographic record
Abstract
Nanocomposites physical properties unexplainable by general mixture laws are usually supposed to be related to interphases, highly present at the nanoscale. The intrinsic dielectric constant of the interphase and its volume need to be considered in the prediction of the effective permittivity of nanodielectrics, for example. The electrostatic force microscope (EFM) constitutes a promising technique to probe interphases locally. This work reports theoretical finite-elements simulations and experimental measurements to interpret EFM signals in front of nanocomposites with the aim of detecting and characterizing interphases. According to simulations, we designed and synthesized appropriate samples to verify experimentally the ability of EFM to characterize a nanoshell covering nanoparticles, for different shell thicknesses. This type of samples constitutes a simplified electrostatic model of a nanodielectric. Experiments were conducted using either DC or AC-EFM polarization, with force gradient detection method. A comparison between our numerical model and experimental results was performed in order to validate our predictions for general EFM-interphase interactions.
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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".