Dependence of friction on roughness, velocity, and temperature
Bibliographic record
Abstract
We study the dependence of friction on surface roughness, sliding velocity, and temperature. Expanding on the classic treatment of Greenwood and Williamson, we show that the fractal nature of a surface has little influence on the real area of contact and the static friction coefficient. A simple scaling argument shows that the static friction exhibits a weak anomaly mu ~ A(0)(-chi/4), where A0 is the apparent area and chi is the roughness exponent of the surface. We then develop a method to calculate atomic-scale friction between a microscopic asperity, such as the tip of a friction force microscope (FFM) and a solid substrate. This method, based on the thermal activation of the FFM tip, allows a quantitative extraction of all the relevant microscopic parameters and reveals a universal scaling behavior of atomic friction on velocity and temperature. This method is extended to include a soft atomic substrate in order to simulate FFM scans more realistically. The tip is connected with the support of the cantilever by an ideal spring and the substrate is simulated with a ball-spring model. The tip and substrate are coupled with repulsive potentials. Simulations are done at different temperatures and scanning velocities on substrates with different elastic moduli. Stick-slip motion of the tip is observed, and the numerical results of the friction force and distribution of force maxima match the theoretical framework.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".