Suppression of spurious vibration of cantilever in atomic force microscopy by enhancement of bending rigidity of cantilever chip substrate
Bibliographic record
Abstract
To improve the precision of dynamic atomic force microscopy (AFM) using cantilever vibration spectra, a simple but effective method for suppressing spurious response (SR) was developed. The dominant origin of SR was identified to be the bending vibration of the cantilever substrate, by the analysis of the frequency of SR. Although a rigid cover pressing the whole surface of the substrate suppressed SR, the utility was insufficient. Then, a method of enhancing the bending rigidity of the substrate by gluing a rigid plate (clamping plate, CP) to the substrate was developed. This chip can be used with an ordinary cantilever holder, so that the reproducibility of SR suppression when attaching and detaching the cantilever chip to the holder was improved. To verify its utility, the evaluation of a microdevice electrode was performed by ultrasonic atomic force microscopy. The delamination at a submicron depth was visualized and the detailed variation of the delamination was evaluated for the first time using clear resonance spectra. The CP method will particularly contribute to improving dynamic-mode AFM, in which resonance spectra with a low quality factor are used, such as noncontact mode AFM in liquid or contact resonance mode AFM. The effect of the CP can be achieved by fabricating a substrate with a thick plate beforehand.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".