Nature‐Inspired Janus Actuators with Structurally Engineered PEDOT:PSS
Bibliographic record
Abstract
Abstract Inspired by natural systems such as pinecones and seedpods, bilayer actuators enable programmable shape transformations through differential responses to external stimuli, making them promising for soft robotics and adaptive devices. However, issues such as interfacial delamination, high driving voltages, and excessive heat generation hinder their practical use. Here, a facile strategy is developed to fabricate a high‐performance poly(3,4‐ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) Janus actuator capable of large, reversible bending deformation in response to electrical current, heat, humidity, and organic vapor. The actuator features a seamless interface between a conductive, hydrophobic‐treated layer and a semiconductive, hydrophilic untreated layer, effectively eliminating delamination common in bilayer structures. This structural engineering is enabled by a novel “dual treatment” approach that combines solvent doping and acid post‐treatment, producing a substantial conductivity contrast (≈2000 S cm −1 ) between the two sides while preserving structural integrity. The resulting actuator exhibits excellent electro‐thermo‐mechanical performance, achieving reversible curvature values of 2.4 to 3.4 cm −1 at low operating voltages (2 to 6 V) with minimal surface heating (≈5 °C). It also demonstrates outstanding durability over 2,400 actuation cycles. This work introduces a scalable and energy‐efficient design platform for next‐generation soft actuators, bio‐inspired adaptive materials, and intelligent sensing systems.
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) | 0.000 | 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".