Challenges and Strategies for Optimizing Corrosion and Biodegradation Stability of Biomedical Micro‐ and Nanoswimmers: A Review
Bibliographic record
Abstract
Abstract The last two decades have witnessed the emergence of micro‐ and nanoswimmers (MNSs). Researchers have invested significant efforts in engineering motile micro‐ and nanodevices to address current limitations in minimally invasive medicine. MNSs can move through complex fluid media by using chemical fuels or external energy sources such as magnetic fields, ultrasound, or light. Despite significant advancements in their locomotion and functionalities, the gradual deterioration of MNSs in human physiological media is often overlooked. Corrosion and biodegradation caused by chemical reactions with surrounding medium and the activity of biological agents can significantly affect their chemical stability and functional properties during their lifetime performance. It is therefore essential to understand the degradation mechanisms and factors that influence them to design ideal biomedical MNSs that are affordable, highly efficient, and sufficiently resistant to degradation (at least during their service time). This review summarizes recent studies that delve into the physicochemical characteristics and complex environmental factors affecting the corrosion and biodegradation of MNSs, with a focus on metal‐based devices. Additionally, different strategies are discussed to enhance and/or optimize their stability. Conversely, controlled degradation of non‐toxic MNSs can be highly advantageous for numerous biomedical applications, allowing for less invasive, safer, and more efficient treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".