Effect of Surface Patterning on the Adhesive Friction
Bibliographic record
Abstract
Tribological properties play an important role in many applications that require low adhesion or non sticking surfaces; therefore, understanding the effect of the surface morphology on adhesion can allow for improved surfaces to be created. In the last 10 years, researchers have paid attention to the impact of the surface roughness on the tribological behaviour. As a result the idea of surface pattering or texturing has emerged as a mean of controlling the friction and adhesion between contacting surfaces. In this study, the effect of the different surface patterns with specifically selected parameters, such as the pattern size, and pattern density on the adhesion force which is measured by Atomic Force Microscope (AFM) is thoroughly investigated. First, micro laser dimples of different diameters’ (D’s) of 5, 10 and 20 μm are fabricated on air hardened tool steel samples using High quality–high power CuBr vapour laser. The distance (L), between the centers of two neighbouring circular dimples, were set to different values of 5, 10, 20, 40 and 80 μm. The AFM tip is modified so that the effect of the patterning on the adhesive force can be captured. A customized micro fabricated polystyrene particle of 120 μm in diameter is used as a tip attachment to the end of calibrated silicon nitride cantilever. The pull-off force versus displacement curves are recorded and used to estimate the average adhesion force for each surface pattern. It has been observed that selected surface patterns significantly decrease the adhesive forces compared to a flat surface. The ratio D/L, which represents the pattern density or the complement of the contact area establishes a well-defined trend of decrease of the adhesion force as D/L increases.
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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 | 0.000 | 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) | 0.002 | 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".