Programmable stimuli-responsive zwitterionic hydrogels for soft robotic applications
Bibliographic record
Abstract
As a prominent class of actuators, stimuli-responsive hydrogels have attracted significant interest in the \nsoft robotics community for biomedical applications. Key features of hydrogels, including high water \ncontent and similar physicochemical properties to that of tissues, make them excellent materials to be \nused in a wide range of biomedical applications such as drug delivery, and tissue engineering. \nParticularly, hydrogels with stimuli-responsiveness, self-healing, shape-morphing, low cytotoxicity, \nand tunable physiochemical properties can be used as functional building blocks in biomedical devices \nand robots, enabling minimally invasive medical procedures. \n Introducing programmability to the shape-morphing of hydrogels opens up new opportunities, \nespecially, in the fabrication of remotely controllable biomedical robots. In this work, we synthesized \nresponsive hydrogel nanocomposites and bilayers with preprogrammed shape transformations that \nenable desirable robotic functionalities. For this, we used zwitterionic/acrylate chemistries that impart \nself-healing, stimuli-responsiveness, and biocompatibility to our hydrogel system. Introducing \nheterogenous physiochemical properties, at the microscale, and employing a multilayering approach, \nat the macroscale, rendered differential swelling to the hydrogels, which were then employed as a \nprogramming strategy to facilitate 2D-to-3D shape-morphing of the hydrogel upon exposure to \nenvironmental cues. \n As a proof-of-concept, we demonstrated tethered and untethered soft robotic functionalities, such as \nactuation, magnetic locomotion, and targeted transport of soft and light cargo in confined and flooded \nmedia. Our future direction includes developing novel bio-inks from this hydrogel system for extrusion \nadditive manufacturing given their excellent tunability of mechanical properties coupled with the shear thinning rheology of the hydrogel. We believe that the proposed hydrogel formulation will expand the \nportfolio of functional materials for fabricating miniaturized soft actuators for biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".