Reproducibility of superhydrophobic and oleophobic polymeric micro surface topographies
Bibliographic record
Abstract
Abstract Micro-structured surfaces can provide useful material properties, such as repellency to water, oil, or alcohols. Multiple polymers were investigated based on micro molding replication fidelity of repellent structures using both advancing and receding contact angle measurements. Five different polymers (Loctite 3525, ST-1060, TC-854, TC-8740, and Teflon AF) were chosen based on a range of durometers, for each of which, a recommended curing process was presented. These polymers were micro-structured via a one-step replica molding to create mushroom shaped fibers with overhanging caps. Teflon AF, a low surface energy polymer, produced innovative superhydrophobic as well as oleophobic Micro Surface Topographies (MSTs). Advancing contact angles (CAs) of these microstructures were 166 ± 4.2, 151 ± 2.9, and 119 ± 2.2 when in contact with a water, ethylene, and olive oil droplet respectively. The highest reproducibility was achieved by using a curing procedure of 100 °C for 2 h. However, even in these conditions, 35% of MSTs were not fully reproduced. ST-1060 was considered a good alternative to Teflon AF since, even after five uses of the same casting mold, the advancing CAs decreased less than 2% when in contact with any liquid tested. Polymeric MSTs resistance to an external force was also examined using a gyratory shaker suggesting that softer materials, such as ST-1060, were required to survive exposure to environmental conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".