Bioinspired Scalable Lubricated Bicontinuous Porous Composites with Self-Recoverability and Exceptional Outdoor Durability
Bibliographic record
Abstract
Lubricant-impregnated surfaces (LIS) are promising as efficient liquid-repellent surfaces, which comprise a surface lubricant layer stabilized by base solid structures. However, the lubricant layer is susceptible to depletion upon exposure to degrading stimuli, leading to the loss of functionality. Lubricant depletion becomes even more pronounced in exposed outdoor conditions, restricting LIS to short-term lab-scale applications. Thus, the development of scalable and long-term stable LIS suitable for practical outdoor applications remains challenging. In this work, we designed "Lubricated Bicontinuous porous Composites" (LuBiCs) by infusing a silicone oil lubricant into a bicontinuous porous composite matrix of tetrapod-shaped zinc oxide microfillers and poly(dimethylsiloxane). LuBiCs are prepared in the meter scale by a facile drop-casting inspired wet process. The bicontinuous porous feature of the LuBiCs enables capillarity-driven spontaneous lubricant transport throughout the surface without any external driving force. Consequently, the LuBiCs can regain liquid-repellent function upon lubricant depletion via capillary replenishment from a small, connected lubricant reservoir, making them tolerant to lubricant-degrading stimuli (e.g., rain shower, surface wiping, and shearing). As a proof-of-concept, we show that the large-scale "LuBiC roof" retains slippery behavior even after more than 9 months of outdoor exposure.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".