Surface forces and interaction mechanisms of soft thin films under confinement: a short review
Bibliographic record
Abstract
Surface forces of soft thin films under confinement in fluids play an important role in diverse biological and technological applications, such as bio-adhesion, lubrication and micro- and nano-electromechanical systems. Understanding the involved interaction mechanisms underlying the adhesion behaviors and tribological performances (i.e., friction and lubrication) of various confined soft thin films is significant in the development of both fundamental science and practical technologies. In this review, the fundamentals of surface forces are briefly presented. The widely utilized force measurement techniques including surface forces apparatus (SFA), atomic force microscopy (AFM) and spacer layer interferometry tribometer techniques are introduced. The advances in the fundamental understanding of a wide range of adhesion and tribological phenomena have been reviewed, in terms of the intermolecular and surface interaction mechanisms involved. The influences of various factors such as confined film properties, experimental conditions (e.g., normal load, and sliding velocity) and environmental variables (e.g., salts, salinity, additives and pH) on the adhesion, friction or lubrication forces of confined soft thin films are presented. The correlation between adhesion hysteresis and friction/lubrication behaviors has been discussed. Some of the challenging issues remaining and future perspectives are also provided.
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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) | 0.001 | 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) | 0.003 | 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".