Liquid-Infused Surfaces: A Review of Theory, Design, and Applications
Bibliographic record
Abstract
plant, a frontier of devices has emerged with unmatched capabilities. Liquid-infused surfaces (LISs), particularly known for their liquid-repelling behavior under low tilting angles (<5°), have demonstrated a plethora of applications in medical, marine, energy, industrial, and environmental materials. This review presents recent developments of LIS technology and its prospective to define the future direction of this technology in solving tomorrow's real-life challenges. First, an introduction to the different models explaining the physical phenomena of these surfaces, their wettability, and viscous-dependent frictional forces is discussed. Then, an outline of different emerging strategies required to fabricate a stable liquid-infused interface is presented, including different substrates, lubricants, surface chemistries, and design parameters which can be tuned depending on the application. Furthermore, applications of LIS coatings in the areas of anticorrosion, antifouling, anti-icing, self-healing, droplet manipulation, and biomedical devices will be presented followed by the limitations and future direction of this technology.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".