Two-Dimensional Nanomaterials beyond Graphene for Biomedical Applications
Bibliographic record
Abstract
Two-dimensional (2D) nanomaterials (e.g., graphene) have shown to have a high potential in future biomedical applications due to their unique physicochemical properties such as unusual electrical conductivity, high biocompatibility, large surface area, and extraordinary thermal and mechanical properties. Although the potential of graphene as the most common 2D nanomaterials in biomedical applications has been extensively investigated, the practical use of other nanoengineered 2D materials beyond graphene such as transition metal dichalcogenides (TMDs), topological insulators (TIs), phosphorene, antimonene, bismuthene, metal-organic frameworks (MOFs) and MXenes for biomedical applications have not been appreciated so far. This review highlights not only the unique opportunities of 2D nanomaterials beyond graphene in various biomedical research areas such as bioelectronics, imaging, drug delivery, tissue engineering, and regenerative medicine but also addresses the risk factors and challenges ahead from the medical perspective and clinical translation of nanoengineered 2D materials. In conclusion, the perspectives and future roadmap of nanoengineered 2D materials beyond graphene are outlined for biomedical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".