Macrophage-compatible magnetic achiral nanorobots fabricated by electron beam lithography
Bibliographic record
Abstract
With the development and progress of nanotechnology, the prospect of using nanorobots to achieve targeted drug delivery is becoming possible. Although nanorobots can potentially improve nano-drug delivery systems, there remains a significant challenge to fabricating magnetically controllable nanorobots with a size suitable for drug delivery in complex in vivo environments. Most of the current research focused on the preparation and functionalization of microscale and milliscale robots due to the relative difficulties in fabricating nanoscale robots. To address this problem and move towards in vivo applications, this study uses electron beam lithography to fabricate achiral planar L-shaped nanorobots that are biocompatible with immune cells. Their minimal planar geometry enabled nanolithography to fabricate nanorobots with a minimum feature size down to 400 nm. Using an integrated imaging and control system, the locomotive behavior of the L-shaped nanorobots in a fluidic environment was studied by examining their velocity profiles and trajectories. Furthermore, the nanorobots exhibit excellent cell compatibility with various types of cells, including macrophage cells. Finally, the long-term cell culture medium immersion test demonstrated that the L-shaped nanorobots have robust stability. This work will demonstrate the potential to use these nanorobots to operate in vivo without triggering immune cell responses.
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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 | 0.001 |
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".