Programmable Humidity-Responsive Actuation of Polymer Films Enabled by Combining Shape Memory Property and Surface-Tunable Hygroscopicity
Bibliographic record
Abstract
Most humidity-responsive polymeric actuators can only exhibit shape transformations between a planar shape in the dry state and a bended three-dimensional (3D) shape when exposed to moisture, and it is challenging to design and prepare hygroscopic actuators with programmable actuating behaviors displayed from sophisticated 3D structures. Herein, we demonstrate that the integration of shape memory property and surface treatment enabled hygromorphic responsivity endows a single-component polymer film with programmable moisture-driven actuating behaviors. The solvent-processed polyethylene- co -acrylic acid (EAA) copolymer film is soft and stretchable at room temperature, and has a good thermal-responsive shape memory property. By surface treatment using base/acid solutions, the reversible gradient conversion between carboxyl groups and carboxylate salts along the thickness direction enables the film to exhibit designed hygroscopic actuations. The shape memory property and moisture-driven actuating behaviors can be combined to realize 3D-3D morphing by first programming the films into 3D shapes and then conducting the surface treatments. Both shape programming and surface treatment processes can be reprogrammed to make the actuation behavior readily tunable. We also show that the created surface patterns can act as moisture-sensitive conducting paths to detect human breathes, and the combination of shape memory, moisture-responsive morphing and conductivity change leads to some interesting applications such as smart switch in conducting circuit. This work provides a new and general strategy for the design of advanced humidity-responsive actuators.
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 | 0.000 | 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".