Probing the intriguing frictional behavior of hydrogels during alternative sliding velocity cycles
Bibliographic record
Abstract
Abstract Understanding the friction behavior of hydrogels is critical for the long-term stability of hydrogel-related bioengineering applications. Instead of maintaining a constant sliding velocity, the actual motion of bio-components (e.g., articular cartilage and cornea) often changes abruptly. Therefore, it is important to study the frictional properties of hydrogels serving under various sliding velocities. In this work, an unexpected low friction regime (friction coefficient μ < 10 −4 at 1.05×10 −3 rad/s) was observed when the polyacrylamide hydrogel was rotated against a glass substrate under alternative sliding velocity cycles. Interestingly, compared with the friction coefficients under constant sliding velocities, the measured μ decreased significantly when the sliding velocity changed abruptly from high speeds (e.g., 105 rad/s) to low speeds (e.g., 1.05×10 −3 rad/s). In addition, μ exhibited a downswing trend at low speeds after experiencing more alternative sliding velocity cycles: the measured μ at 1.05 rad/s decreased from 2×10 −2 to 3×10 −3 after 10 friction cycles. It is found that the combined effect of hydration film and polymer network deformation determines the lubrication and drag reduction of hydrogels when the sliding velocity changes abruptly. The observed extremely low friction during alternative sliding velocity cycles can be applied to reduce friction at contacted interfaces. This work provides new insights into the fundamental understanding of the lubrication behaviors and mechanisms of hydrogels, with useful implications for the hydration lubrication related engineering applications such as artificial cartilage.
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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) | 0.001 | 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".