Three-Dimensional Co-Printing of Hydrogels and Elastomers for Facile Fabrication of Soft Electronics and Robotics
Bibliographic record
Abstract
Polymeric materials such as hydrogels and elastomers offer intrinsic stretchability and tunable electrical properties, enabling advancements in soft electronics and robotics. However, the lack of a universal fabrication method for integrating hydrogels and elastomers while maintaining good structural integrity between different material layers has limited device integration and manufacturing scalability. Here, we present a fully 3D co-printing process that enables the direct fabrication of hydrogel-elastomer integrated electronic systems using direct ink writing (DIW), a versatile 3D printing technique that supports multi-material deposition and rapid prototyping. This approach achieves seamless integration of multiple polymeric materials through covalent bonding between hydrogel and elastomer layers. We demonstrate the potential of this fabrication method by developing hydrogel-elastomer sensor arrays and integrating them into wearable devices and soft robotics, including a full-scale sensing glove, a wristband, and a soft robotic gripper. Our process highlights the versatility and promise of our co-printing strategy for advanced manufacturing of soft electronics and robotics.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".