Anisotropic wetting surfaces machined by diamond tool with tips microstructured by focused ion beam
Bibliographic record
Abstract
In recent years, there has been an increasing interest in methods to fabricate hydrophobic surfaces. Hydrophobic surfaces have been used in multiple applications in microfluidic devices to control fluid flow, as self-cleaning surfaces and also in de-icing or for drag reduction. Conventionally, hydrophobic surfaces were created by laser processing, self-assembly and other chemical processing methods. However, in many of these methods, hydrophobicity of the surface cannot be maintained for an extended time or restricted to limited set of materials. In some applications, creation of anisotropy in the hydrophobic property and the ability to pattern it, is important. Here, a low-cost, high-throughput method to generate highly hydrophobic and anisotropic surface has been developed. This method uses Computer Numerical Control machining employing diamond tools whose tips have been micro-structured using Focused Ion Beam that enables parallelization and achieves at least four times higher machining speed compared with other methods. The versatility of this method has been demonstrated by machining both metal and polymeric materials. Significant anisotropic wetting has been observed on the machined surface with the anisotropy in directional contact angle of up to 71.6°. Highly-hydrophobic surfaces with contact angle of 163.1° on 6061 Aluminum Alloy and 155.7° on polymethyl methacrylate surface were created.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".