Soft Bio‐Microrobots: Toward Biomedical Applications
Bibliographic record
Abstract
Soft bio‐microrobots have the potential to execute complex tasks in unexpected and harsh environments of the human body due to their dexterity and flexibility. The architectural designs of soft bio‐microrobots either replicate the motion of natural creatures or capitalize on their motility. Based on this design principle, biologically inspired microrobots that imitate the movements and functions of biological systems, such as starfish, bacteria, and sperm cells, as well as biohybrid microrobots that combine motile micro‐organisms or cells with functional components have been developed. Herein, an overview of the design principles, energy sources, and biomedical applications of existing soft bio‐microrobots is presented. It is shown that the incorporation of externally responsive material enables biologically inspired microrobots to change their shapes and imitate the motion of living organisms under external stimuli, and it is interpreted how biohybrid microrobots are guided through the tactic behavior of microorganisms or cells. Finally, perspectives on key challenges that soft bio‐microrobots must overcome to achieve in vivo biomedical applications are given.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".