Bibliographic record
Abstract
In this research, a low-cost template-based process has been developed to structure the surfaces of polymeric materials rendering them superhydrophobic. This biology-inspired approach was developed using results from the first part of this thesis: the first known detailed study of superhydrophobic aspen leaf surfaces. Aspen leaves, similar to lotus leaves, possess a dual-scale hierarchical surface structure consisting of micro-scale papillae covered by nano-scale wax crystals, and this surface structure was used as a blueprint in the structuring of templates. These distinctive surface features coupled with a hydrophobic surface chemistry is responsible for these leaves’ extreme non-wetting property. Non-wetting is further augmented by the unique high aspect ratio aspen leafstalk geometry. The slender leafstalks offer very little resistance to twisting and bending, which results in significant leaf movement in the slightest breeze, facilitating water droplet roll-off. The structured template surfaces, produced by sand blasting and chemical etching of electrodeposited nanocrystalline nickel sheets, resemble the negative of the superhydrophobic aspen leaf surfaces. Re-usable templates were subsequently employed in a hot embossing technique where they were pressed against softened polymers (polyethylene, polypropylene and polytetrafluoroethylene) thereby transferring their surface structures. The resulting pressed polymer surfaces exhibited features very similar to aspen leaf surfaces. This process increased the water contact angle for all pressed polymers to values above 150 degrees. Additionally, after pressing the water roll-off angle for all polymer surfaces dropped below 5 degrees. The effects of water surfactant concentration, water drop size and temperature on the wetting characteristics of the structured polymers were studied to indicate in which applications these functional surfaces could be most beneficial. Coupling this attractive superhydrophobic surface property with mechanical motion (shaking, bending, or vibrating) could result in superhydrophobic surfaces with superior non-wetting properties suitable for a wide range of applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".