Dynamic heterodyned polarization imaging: a scanning probe technique for studying polarization dynamics in materials
Bibliographic record
Abstract
Electric fields in dielectric materials produce dipoles related to the polarizability of the material. In this paper we will present a technique that measures the polarization near a surface. The polarization is induced by a modulated signal applied to the conducting probe used for detection. The polarization dipoles in the surface layers of the material generate an electrostatic attraction between the between the probe and the dielectric material. Using techniques common in non-contact force microscopy these forces can easily be sensed. Remarkably, this measurement technique can be extended to frequencies well above the mechanical resonant frequency of the probe cantilever by utilizing amplitude modulation heterodyning. By rastering the probe over the surface an image of the dielectric properties of the surface can be produced. We expect this technique to be useable up to frequencies of at least 20 GHz and time resolution of less than 100 ps. We present calculations of the forces generated assuming simple probe geometries and also thermal noise that compare favourably with experimental results. The technique has been used in the stroboscopic imaging of an operating 434 MHz surface acoustic wave device. The experiments already completed demonstrate that this technique may be employed to produce images that display the local polarizability of materials at a given frequency. In more detailed studies, regions of interest can be imaged repeatedly, with different frequencies used to produce each image.
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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 | 0.001 |
| Open science | 0.001 | 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".