Use of biaxially oriented polypropylene film for evaluating and cleaning contaminated atomic force microscopy probe tips: An application to blind tip reconstruction
Bibliographic record
Abstract
An atomic force microscopy (AFM) image of a surface is basically a convolution of the probe tip geometry and the surface features; it is important to know this tip effect to ensure that an image truly reflects the surface features. We have found that a biaxially oriented polypropylene (BOPP) film is suitable for checking tip performance and for cleaning contaminated tips, thus making it possible to collect images of the same area of a BOPP film surface before and after the tip was cleaned. Therefore, the difference between the two different images is solely due to the contamination of the tip. We took advantage of our ability to collect AFM images of the same area using the same tip, in one instance, contaminated and, in the other, after being cleaned. First we used blind reconstruction on the image collected using the contaminated tip. Blind tip reconstruction allows one to extract the geometry of the tip from a given image. Once we had estimated the geometry of the contaminated tip, we used it to simulate the tip effect using the image collected using the cleaned tip. By comparing the simulation result with the image collected using the contaminated tip we showed that the blind reconstruction routine works well. Prior to this, there was no de facto method for testing blind reconstruction algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".