Ice adhesion on femtosecond laser textured surfaces
Bibliographic record
Abstract
Ice formation and accumulation on external surfaces create dangerous conditions conducive for the failure of systems and infrastructure, as well as the potential for causing bodily harm, and in severe cases, death. One approach towards mitigating the icing problem is to fabricate ice-releasing materials that reduce the adhesion strength of ice to their surface. In this thesis, the viability of femtosecond (fs) laser surface texturing as a surface modification technique for creating ice-phobic surfaces is examined. Since fs-laser surface patterning is a relatively new technique, a systematic means of characterizing the hierarchical surface topographies imparted by fs-laser irradiation is lacking in literature. In addition, the formation processes of laser-induced topographies are still poorly understood. The first part of this thesis responds to these gaps in literature by i) introducing lacunarity analysis to quantitatively characterize hierarchical surface structures, and ii) providing insight into the formation mechanisms of laser-induced structures through detailed chemical, crystallographic, and topographical analyses. With the knowledge gained from the first part of the thesis, the second part of the thesis tackles the ice adhesion problem through the use of superhydrophobic laser-inscribed square pillars, as well as two other materials: stainless steel 316 meshes and multi-walled carbon nanotube (MWCNT)-covered meshes. The findings indicate that surface wettability is a poor predictor of ice adhesion. Instead, the surface topography of the substrate determines the ice adhesion strength by balancing two competing factors: mechanical interlocking and the formation of stress concentrations at the ice-substrate interface. This study highlights the importance of considering interfacial fracture mechanics in determining ice adhesion strength and promotes new strategies towards designing ice- shedding materials by utilizing their surface topography.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; both teacher heads agree on what is shown here.
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".