Degradable and Biocompatible Magnesium Zinc Structures for Nanomedicine: Magnetically Actuated Liposome Microcarriers with Tunable Release
Bibliographic record
Abstract
Abstract Inorganic therapeutic carriers and implants should not only be biocompatible, but should also degrade under physiological conditions. Ideally, the time of the degradation can be controlled, and ideally the degradation products are fully biocompatible and metabolized by the body. This proves a challenge for carriers used in nanomedicine, including microswimmers and nanorobotic systems destined for targeted delivery, as these generally require inorganic materials to enable coupling to external fields. Taking inspiration from macroscopic orthopedic implants that are made from magnesium (Mg) and zinc (Zn) and that are fully biocompatible and degradable, the growth of complex microstructures is demonstrated, including micropropellers, containing Mg and Zn. By varying the content of Mg, the corrosion time of the microstructures can be tuned from hours to over a month. Incorporation of biocompatible hard‐magnetic iron (Fe)‐platinum (Pt) permits the controlled motion of the micropropellers. The surface of the MgZn structures can be functionalized with liposomes, rendering the structures microcarriers that allow for a time‐dependent release of their cargo as a results of their degradation in aqueous environments. This suggests a powerful platform for targeted drug or gene delivery, that can be integrated with established systems for magnetic actuation and transfection.
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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".