Topographical Design and Thermal-Induced Organization of Interfacial Water Structure to Regulate the Wetting State of Surfaces
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Smart surfaces with superhydrophobic/superhydrophilic characteristics can be controlled by external stimuli, such as temperature. These transitions are attributed to the molecular-level conformation of the grafted polymer chains due to the varied interactions at the interface. Here, tunable surfaces were prepared by grafting two well-known thermo-responsive polymers, poly( N -isopropylacrylamide) (PNIPAM) and poly(oligoethylene glycol)methyl ether acrylate (POEGMA 188 ) onto micro-pollen particles of uniform morphology and roughness. Direct Raman spectra and thermodynamic analyses revealed that above the lower critical solution temperature, the bonded and free water at the interface partially transformed to intermediate water that disrupted the “water cage” surrounding the hydrophobic groups. The increased amounts of intermediate water produced hydrogen bonding networks that were less ordered around the polymer grafted microparticles, inducing a weaker binding interaction at the interface and a lower tendency to wet the surface. Combining the roughness factor, the bulk surface assembled by distinct polymer-grafted-pollen microparticles (PNIPAM or POEGMA 188 ) could undergo a different wettability transition for liquid under air, water, and oil. This work identifies new perspectives on the interfacial water structure variation at a multiple length scale, which contributed to the temperature-dependent surface wettability transition. It offers inspiration for the application of thermo-responsive surface to liquid-gated multiphase separation, water purification and harvesting, biomedical devices, and printing.
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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 | 0.000 | 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 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".