Investigating the Effects of Lubricant Infusion Methods on Polymer SLIPS
Bibliographic record
Abstract
Slippery lubricant infused porous surfaces (SLIPSs) are promising bioinspired surfaces with self-healing and droplet wetting properties, among many others, that are desirable due to their range of applications. Recently, there have been many developments in the SLIPS field regarding the creation of textured surfaces and lubricant selection. However, there is a lack of knowledge regarding the method of lubricant infusion. In this study, we aim to fill this void by investigating different infusion methods that impose external forces on the lubricant. We developed our SLIPS by hot embossing nanostructures onto polypropylene by using molds that were laser micromachined. These textured surfaces were then infused with silicone oil using three different infusion methods: ultrasonication, vacuum, and hydrostatic pressure. We analyzed the wettability and slipperiness of the SLIPS by evaluating the critical tilt angle and comparing the sliding velocities of water droplets on each sample at a tilt angle of 20°. Additionally, the durability of the SLIPS was tested by dropping 50 successive water drops onto the samples and evaluating the droplet-surface interactions throughout. The sonicated infusion method yielded SLIPS that performed the best with a contact angle hysteresis of 13°, a critical tilt angle of 18.3°, a sliding velocity of 1.66 mm/s, and the least accumulation of droplets over time with use. These values are greatly improved when compared to the control sample where lubricant was simply dripped on, which resulted in a contact angle hysteresis of 20°, a critical tilt angle of 26.3°, and a sliding velocity of 0.23 mm/s. The sonicated and drip infusion methods were also compared with different materials (stainless steel) and different textures (microstructures). It was found that the improvement in slipperiness using the sonicated infusion method is prominent for nanoscale textures on both stainless steel and polypropylene. In this study, we discuss the challenges with oil depletion in SLIPS (cloaking and wetting ridges) and with the selection of contact angle measurement methods. While further investigation as to why certain applied forces during infusion yield better SLIPS is warranted, these forces greatly affect the outcome. This work suggests that researchers should consider using sonication or other methods of lubricant infusion that apply external forces as infusion techniques to yield better SLIPS on the nanoscale.
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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 | 0.000 | 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 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".