<i>In Situ</i> Reproducible Sharp Tips for Atomic Force Microscopy
Bibliographic record
Abstract
Atomically sharp tips are a requirement for scanning-probe microscopy, such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM). Compared with STM, AFM imaging is more sensitive to the sharpness of tip apices because long-range forces act as a background signal on the high-resolution AFM images originating from short-range forces. Here we report the investigation of in situ reproducible sharp tips for AFM. We make an ${\mathrm{Ag}}_{2}\mathrm{S}$ crystal, a mixed ionic and electronic conductor, on a conventional $\mathrm{Si}$ cantilever, and controllably grow and shrink the $\mathrm{Ag}$ nanoprotrusion by changing the polarity of the bias voltage between the tip and the sample. We are able to reduce the contribution of long-range forces by growing a $\mathrm{Ag}$ nanoprotrusion on the ${\mathrm{Ag}}_{2}\mathrm{S}$ tip, and obtain atomic-resolution AFM images. We also confirm that the ${\mathrm{Ag}}_{2}\mathrm{S}$ tip with a $\mathrm{Ag}$ nanoprotrusion, the end of which presumably terminates in $\mathrm{Si}$ atoms, is capable of simultaneous AFM and STM measurements.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".