Macro-Textured Fabric Reduces Water Droplet Impact Contact Time
Bibliographic record
Abstract
Reducing fabric wetting by minimizing the contact time of impacting droplets is beneficial in many textile applications, such as rainwear and personal protective equipment. Macro-texturing of rigid superhydrophobic surfaces is a technique to reduce droplet impact contact time that has not previously been applied to textiles. This study examines utilizing inherently macro-textured superhydrophobic fabrics to reduce the contact time of impacting water droplets, adjusting the fabric topography through stretching and gathering to optimize the macro-texture. Fabrics were finished with either a commercial superhydrophobic finish or a developed superhydrophobic formulation utilizing fumed silica nanoparticles and poly(dimethylsiloxane) (PDMS) brushes. Various droplet splitting impact behaviors were observed and depended on the impact site and geometry of the macro-textured fabric. A 2:1 gathering ratio on woven seersucker fabric provided a 52% decrease from the theoretical minimum contact time for droplet impact on a ridge site. Rigid 3D-printed superhydrophobic surfaces that mimicked the fabric topography provide a useful analogue to the fabric surface for fine-tuning fabric designs.
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) | 0.000 | 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) | 0.007 | 0.004 |
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".