Contrast mechanisms on nanoscale subsurface imaging in ultrasonic AFM: scattering of ultrasonic waves and contact stiffness of the tip–sample
Bibliographic record
Abstract
Ultrasonic atomic force microscopy (AFM) and its associated derivatives are nondestructive techniques that can elucidate subsurface nanoscale structures and properties. Despite the usefulness of these techniques, the physical contrast mechanisms responsible for the reported subsurface features observed in ultrasonic AFM are not well defined. In this study, we present a comprehensive model combining ultrasonic wave scattering and tip-sample contact stiffness to better reproduce the experimentally measured phase variations over subsurface features in two model systems. These model systems represent the two extreme sample types typically imaged by ultrasonic AFM, one being a hard material and the other a soft polymeric material. The theoretical analysis presented and associated comparisons with experimental results suggest that the image contrast depends on the combination of two contrast mechanisms: the perturbation of the scattered ultrasonic waves and the local variation of the contact stiffness at the tip-sample contact. The results of this study open up a new door for the depth estimation of buried nanoscale features into hard (engineering structures) and soft (polymers and biological structures) materials, and eventually lead to non-invasive, high-resolution 3D nano-tomography by ultrasonic AFM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".