Aligned Carbon Nanotube–Based Flexible Gel Substrates for Engineering Biohybrid Tissue Actuators
Bibliographic record
Abstract
Muscle-based biohybrid actuators have generated significant interest as the future of biorobotics but so far they move without having much control over their actuation behavior. Integration of microelectrodes into the backbone of these systems may enable guidance during their motion and allow precise control over these actuators with specific activation patterns. Here, we addressed this challenge by developing aligned CNT forest microelectrode arrays and incorporated them into scaffolds for stimulating the cells. Aligned CNTs were successfully embedded into flexible and biocompatible hydrogel exhibiting excellent anisotropic electrical conductivity. Bioactuators were then engineered by culturing cardiomyocytes on the CNT microelectrode-integrated hydrogel constructs. The resulting cardiac tissue showed homogeneous cell organization with improved cell-to-cell coupling and maturation, which was directly related to the contractile force of muscle tissue. This centimeter-scale bioactuator has excellent mechanical integrity, embedded microelectrodes and is capable of spontaneous actuation behavior. Furthermore, we demonstrated that a biohybrid machine can be controlled by an external electrical field provided by the integrated CNT microelectrode arrays. In addition, due to the anisotropic electrical conductivity of the electrodes provided from aligned CNTs, significantly different excitation thresholds were observed in different configurations such as the ones in parallel vs. perpendicular direction to the CNT alignment.
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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".