Quantitative Friction-Force Measurements by Longitudinal Atomic Force Microscope Imaging
Bibliographic record
Abstract
Since the first lateral force measurements by atomic force microscopy, one of the main obstacles to quantitative friction-force measurements has been the difficulty in measuring the torsional response of the probes. The influence of friction on images acquired in the usual longitudinal scanning direction has also long been recognized. However, in part due to its less favorable geometry, the longitudinal mode is not typically exploited for friction-force measurements. We show here that quantitative frictional-force measurements are possible in longitudinal imaging and provide several advantages over lateral-force imaging: for instance, topology and frictional effects are coupled in a well-defined way, and there is no need to estimate the torsional spring constant. More importantly, following frictional-force measurements by longitudinal imaging with traditional lateral-force imaging allows a convenient calibration that does not require additional equipment, cantilever preparation, or special samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".