Interaction Stress Measurement Using Atomic Force Microscopy: A Stepwise Discretization Method
Bibliographic record
Abstract
Atomic force microscopy (AFM) is one of the most common techniques for interaction measurements. However, there are severe problems in attaining and interpreting the current interaction measurements obtained from AFM experiments. The existing procedures do not provide a clear understanding of the interaction mechanism and use misleading and ineffective evaluating criteria. Furthermore, ineffective experimental procedures neglect to use the full range of the AFM force curves for interaction measurement. To overcome the drawbacks of the currently used methods, the current work proposes a new interaction measurement parameter, called interaction stress. From the interaction stress, all other interaction properties, such as interaction force, interaction energy, and internal stress, can be calculated. In order to obtain the interaction stress from the AFM measurements, the details of a new method, a stepwise discretization method, are explained. Finally, a set of AFM experiments are designed and performed, and the results are presented in terms of interaction stress. The validity of the captured results is examined by using well established Hamaker constants. The good agreement between the results of the current work and the literature demonstrates the ability of the stepwise discretization method in capturing the interaction stress properly.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".