Lubrication with Soft and Hard Two-Dimensional Colloidal Arrays
Bibliographic record
Abstract
Normal and friction forces between immobilized two-dimensional (2D) homogeneous non-close-packed colloidal arrays made of different particles are compared in aqueous media. Soft pH-responsive (microgels) and nonresponsive hard (silica) particles of different sizes were used to create the 2D arrays. The results show that the friction of soft responsive structured layers can be successfully modulated by varying the pH, with a friction coefficient varying by nearly 3 orders of magnitude (10 –2 to 1). This important change in lubricating properties is mainly correlated with the particle swelling behavior, i.e., the friction coefficient decreasing exponentially with an increase in the swelling ratio regardless of the size, surface coverage, and degree of ionization of the particles. In addition, the robustly attached microgel particles were able to sustain high pressure (up to 200 atm) without significant surface damage. The 2D arrays of nonresponsive hard particles also gave rise to a very low friction coefficient (μ ≈ 10 –3 ) under similar experimental conditions and could sustain a larger pressure without damage (≤600 atm). The low friction dissipation observed between the hard arrays resulted from a rolling mechanism. Even though rolling requires nonimmobilized particles on the substrates, the results show the importance of attaching a certain proportion of particles on the surfaces to reduce friction.
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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 | 0.000 |
| 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 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".