Hybrid Hydrogel‐Magnet Actuators with pH‐Responsive Hydrogels for Gastrointestinal Microrobots
Bibliographic record
Abstract
Limited space on millimeter‐scale devices for biomedical applications makes it challenging to incorporate bulky actuators and power for onboard mechanical actuation. Stimuli‐responsive hydrogels, such as pH‐responsive hydrogels, provide a solution to automatically sense and actuate in the gastrointestinal tract. However, hydrogels are often nonload bearing and slow in actuation. To overcome these challenges, a new type of hybrid actuator is developed which utilizes a pH‐responsive hydrogel with magnets to trigger magnetic springs (i.e., permanent magnets with repulsive, spring‐like forces) to quickly initiate rotational and translational movements at pH > 6. The agar‐poly(acrylic acid) hydrogel undergoes a large volume transition at pH > 6 and exhibits large nominal blocking stress of 610–819 kPa for a 3–4 mm diameter cylinder hydrogel. Moreover, the scaling of hydrogel force and response times are experimentally confirmed. Based on the hydrogel properties, an analytical hydrogel model is developed to predict hydrogel force and displacement under varying magnetic loads and wall constraints in simulated gastric fluid (SGF, pH 1.2) and simulated intestinal fluid (SIF, pH 6.8), and the experimental data validate the model. Finally, an innovative hybrid hydrogel‐magnet actuator that triggers rotational and translational motion without external activation is demonstrated.
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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".