A rolled-up-based fabrication method of 3D helical microrobots
Bibliographic record
Abstract
While the potential of using helical microrobots for biomedical applications, such as cargo transport, drug delivery, and micromanipulation, had been demonstrated, the viability to use them for practical applications is hindered by the cost, speed, and repeatability of current fabrication techniques. Hence, this paper introduces a simple, low-cost, high-throughput manufacturing process for single nickel layer helical microrobots with consistent dimensions. Photolithography and electron-beam (e-beam) evaporation were used to fabricate 2D parallelogram patterns that were sequentially rolled up into helical microstructures through the swelling effect of a photoresist sacrificial layer. Helical parameters were controlled by adjusting the geometric parameters of parallelogram patterns. To validate the fabrication process and characterize the microrobots' mobility, we characterized the structures and surface morphology of the microrobots using a scanning electron microscope and tested their steerability using feedback control, respectively. Finally, we conducted a benchmark comparison to demonstrate that the fabrication method can produce helical microrobots with swimming properties comparable to previously reported microrobots.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".