Bibliographic record
Abstract
Magnetically driven miniature soft robots exhibit fast and dexterous responses when subjected to external magnetic field. With remote manipulation, controlled navigation of robots can be realized within hard-to-access spaces for potential use in the human body. This thesis introduces a novel approach utilizing multi-layer three-dimensional (3D) printing with digital light processing (DLP) to pattern magnetic nanoparticles (MNPs) within an ultraviolet (UV)-curable polymer matrix, which enables the creation of adaptable 3D magnetic actuators. By programming heterogeneous magnetization within discrete multi-layer robot segments, magnetic torque-induced shape transformations and dynamic motions are achieved through magnetic actuation. These behaviors include gripping, rolling, swimming, and walking, demonstrating versatile capabilities of the printed magnetic robots. Meanwhile, enhanced deformation flexibility and robots’ functionality are observed by integrating multiple materials with distinct mechanical and magnetic properties. In addition, the fabrication method allows for the rapid and accurate 3D printing of complex geometries in a single print, including structures featuring hollow components. A millimeter-scale magnetic structure resembling a pipette rubber bulb with a hollow chamber is created and actuated by magnetic fields to implement sampling, transportation, and controlled release of target liquids. The untethered manipulation of the printed magnetic miniature mechanisms holds significant potential for a spectrum of applications, including liquid biopsy, targeted drug delivery, and precise therapeutic procedures within the confines of biomedical environments.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".