Nanoparticle-Free 3D-Printed Hydrophobic Surfaces for Ice Mitigation Applications
Bibliographic record
Abstract
Ice accumulation on exposed surfaces presents substantial economic and safety challenges across various industries. To overcome limitations associated with traditional anti-icing methods, such as the use of nanoparticles, this study introduces a novel and facile approach for fabricating superhydrophobic and anti-icing microstructures using cost-effective LCD 3D printing technology. The influence of diverse pillar geometries, including square, cylindrical, hexagonal, and truncated conical forms, was analyzed to assess their effects on the hydrophobic and anti-icing/icephobic performance in terms of wettability, ice adhesion strength, and icing delay time. The role of microstructure topography was further investigated through cylindrical patterns with varying geometric parameters to identify optimal designs for enhancing hydrophobic and icephobic characteristics. Furthermore, the effectiveness of surface functionalization using a low surface energy material was evaluated. Our findings demonstrate that the synergistic combination of tailored microscale geometries and surface functionalization significantly enhances anti-icing performance with reliable repeatability, achieving ice adhesion of 13.9 and 17.9 kPa for square and cylindrical pillars, respectively. Critically, this nanoparticle-free 3D printing and low surface energy treatment method offers a scalable and efficient route for producing high-performance hydrophobic/icephobic surfaces, opening promising avenues for applications in sectors where robust anti-icing capabilities are crucial, such as renewable energy and transportation.
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".