<scp>2D</scp> Nanomaterials for Biomedical Applications
Bibliographic record
Abstract
Two-dimensional (2D) nanomaterials have attracted significant research interest since the outbreak of graphene. In general, exfoliation is used to obtain few layered 2D nanostructures. Owing to their unique optical, electronic, and mechanical properties, 2D nanomaterials hold great potential for harnessing them as key components in the fields of electronics, optoelectronics, and clinical biomedicine. Their atomic thickness and exposed huge surface even make them highly designable leading to their usage in various applications. Furthermore, hydrophilicity, surface area, mechanical stability, and conductivity are crucial parameters to improve the biocompatibility of 2D nanostructures and adopt them into medical applications. In this chapter, we discuss the utility of 2D nanomaterials for bioimaging, photothermal therapy (PTT), photodynamic therapy (PDT), drug/gene delivery, biosensors, antibacterials, tissue engineering, and regenerative medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".