Exploring the tactile and thermal behaviour of rose petals for bio-inspired material design
Bibliographic record
Abstract
Rose petals have been recognised for their superhydrophobicity and strong adhesion, a phenomenon known as the petal effect. This phenomenon has been leveraged to engineer surfaces through biomimetics. However, there is limited knowledge regarding the tactile qualities of rose petals. This research explores the thermo-haptic properties of rose petals using a combination of material testing methods, including the measurement of roughness, tactile perception, texture, compliance, and thermal properties. Except for compliance and thermal percepts, tactile properties varied between the adaxial (front) and abaxial (back) sides of the petals. The abaxial side of rose petals was rougher, coarser, stickier, and had higher friction and more heterogeneous micro/macro texture compared to the adaxial side. Low compliance values (for both sides) point to low compressibility. The thermal percepts and thermal effusivity both suggest a cooler tactile sensation compared to most dry fabrics. These data provide valuable insights into the micro and macro-scale texture, compliance, and thermal behaviour of the rose petal, shedding light on its haptic qualities. The research findings contribute to improving our understanding of the biomimicry of natural materials in terms of tactile and thermal properties. It has a huge potential to aid in improving the hand feel sensation in next-to-fit body garments, sports clothing, everyday use garments, etc. It further offers applications for various other fields, including materials science, cosmetics, and product design.
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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".