Bioinspired rubber composites for superior ice traction: enhancing winter safety through innovative laboratory testing in tribology
Bibliographic record
Abstract
Winter conditions significantly increase the risk of slips and falls on ice, especially for the elderly. Improving the ice-gripping performance of rubber materials in shoe outsoles is crucial for enhancing public safety. Biomimetic strategies have led to the development of composites inspired by arctic seal (Arctic Grip), which embed glass fibers in a rubber matrix, and polar animals' claws (ICE-FX), incorporating hard additives into rubber. Laboratory-scale tribometers are essential for evaluating these materials' friction properties early in development, as standard large-scale testing methods are impractical and resource-intensive. This study presents a novel lab-scale tribotest using the Bruker UMT Tribolab for high-throughput ice friction testing of bioinspired rubber composites. Key parameters such as test duration, contact mechanics, and load application were systematically optimized. ICE-FX and Arctic Grip composites were evaluated using this protocol after surface characterization through SEM-EDS. The results indicated that ICE-FX composites exhibited a 161% increase in static friction and a 976% increase in kinetic friction, characterized by broader, lower-amplitude stick-slip oscillations. In contrast, Arctic Grip composites showed a 439% enhancement in static friction and over a 10X increase in kinetic friction, with a shorter, more pronounced stick-slip regime. These findings demonstrate that increasing the density and complexity of surface grip patterns on rubber significantly enhances both static and kinetic friction coefficients, while amplifying stick-slip behavior, which further improves traction on ice. This novel lab-scale tribotest effectively provides high-velocity kinetic friction assessment of rubber composites under ice-sliding conditions. The study emphasizes the critical role of surface texturing in modulating ice friction, offering valuable insights for the advancement of rubber composite design and paving the way for future research in surface engineering to improve winter safety.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".