Insights into dynamic sliding contacts from conductive atomic force microscopy
Bibliographic record
Abstract
Friction in nanoscale contacts is determined by the size and structure of the interface that is hidden between the contacting bodies. One approach to investigating the origins of friction is to measure electrical conductivity as a proxy for contact size and structure. However, the relationships between contact, friction and conductivity are not fully understood, limiting the usefulness of such measurements for interpreting dynamic sliding properties. Here, atomic force microscopy (AFM) was used to simultaneously acquire lattice resolution images of the lateral force and current flow through the tip-sample contact formed between a highly oriented pyrolytic graphite (HOPG) sample and a conductive diamond AFM probe to explore the underlying mechanisms and correlations between friction and conductivity. Both current and lateral force exhibited fluctuations corresponding to the periodicity of the HOPG lattice. Unexpectedly, while lateral force increased during stick events of atomic stick-slip, the current decreased exponentially. Molecular dynamics (MD) simulations of a simple model system reproduced these trends and showed that the origin of the inverse correlation between current and lateral force during atomic stick-slip was atom-atom distance across the contact. The simulations further demonstrated transitions between crystallographic orientation during slip events were reflected in both lateral force and current. These results confirm that the correlation between conduction and atom-atom distance previously proposed for stationary contacts can be extended to sliding contacts in the stick-slip regime.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".