Patterning non-equilibrium morphologies in stimuli-responsive gels through topographical confinement
Bibliographic record
Abstract
Stimuli-responsive "smart" polymers have generated significant interest for introducing dynamic control into the properties of antifouling coatings, smart membranes, switchable adhesives and cell manipulation substrates. Switchable surface morphologies formed by confining stimuli-responsive gels to topographically structured substrates have shown potential for a variety of interfacial applications. Beyond patterning the equilibrium swelling behavior of gels, subjecting stimuli-responsive gels to topographical confinement could also introduce spatial gradients in the various timescales associated with gel deformation, giving rise to novel non-equilibrium morphologies. Here we show how by curing poly(N-isopropylacrylamide) (pNIPAAm)-based gel under confinement to a rigid, bumpy substrate, we can not only induce the surface curvature to invert with temperature, but also program the transient, non-equilibrium morphologies that emerge during the inversion process through changing the heating path. Finite element simulations show that the emergence of these transient morphologies is correlated with confinement-induced gradients in polymer concentration and position-dependent hydrostatic pressure within the gel. To illustrate the relevance of such morphologies in interfacial applications, we show how they enable us to control the gravity-induced assembly of colloidal particles and microalgae. Finally, we show how more complex arrangements in particle assembly can be created through controlling the thickness of the temperature-responsive gel over the bumps. Patterning stimuli-responsive gels on topographically-structured surfaces not only enables switching between two invertible topographies, but could also create opportunities for stimuli ramp-dependent control over the local curvature of the surface and emergence of unique transient morphologies. Harnessing these features could have potential in the design of multifunctional, actuatable materials for switchable adhesion, antifouling, cell manipulation, and liquid and particle transport surfaces.
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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".