Enhanced sensitivity of nanoscale subsurface imaging by photothermal excitation in atomic force microscopy
Bibliographic record
Abstract
Photothermal excitation of the cantilever for use in subsurface imaging with atomic force microscopy was compared against traditional piezoelectric excitation. Photothermal excitation alleviates issues commonly found in traditional piezoelectrics such as spurious resonances by producing clean resonance peaks through direct cantilever excitation. A calibration specimen consisting of a 3 × 3 array of holes ranging from 200 to 30 nm etched into silicon and covered by graphite was used to compare these two drive mechanisms. Photothermal excitation exhibited a signal-to-noise ratio as high as four times when compared to piezoelectric excitation, utilizing higher eigenmodes for subsurface imaging. The cleaner and sharper resonance peaks obtained using photothermal excitation revealed all subsurface holes down to 30 nm through 135 nm of graphite. In addition, we demonstrated the ability of using photothermal excitation to detect the contact quality variation and evolution at graphite-polymer interfaces, which is critical in graphene-based nanocomposites, flexible electronics, and functional coatings.
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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 | 0.001 |
| 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 | 0.001 | 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".