Applications of Carbon Nanotubes-Based Biomaterials in Biomedical Nanotechnology
Bibliographic record
Abstract
One of the facets of nanotechnology applications is the immense opportunities they offer for new developments in medicine and health sciences. Carbon nanotubes (CNTs) have particularly attracted attention for designing new monitoring systems for environment and living cells as well as nanosensors. Carbon nanotubes-based biomaterials are also employed as support for active prosthesis or functional matrices in reparation of parts of the human body. These nanostructures are studied as molecular-level building blocks for the complex and miniaturized medical device, and substrate for stimulation of cellular growth. The CNTs are cylindrical shaped with caged molecules which can act as nanoscale containers for molecular species, well required for biomolecular recognition and drug delivery systems. Endowed with very large aspect ratios, an excellent electrical conductivity and inertness along with mechanical robustness, nanotubes found enormous applications in molecular electronics and bioelectronics. The ballistic electrical behaviour of SWNTs conjugated with functionalization promotes a large variety of biosensors for individual molecules. Actuative response of CNTs is considered very promising feature for nanodevices, micro-robots and artificial muscles. An description of CNTs based biomaterials is attempted in this review, in order to point out their enormous potential for biomedical nanotechnology and nanobiotechnology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.002 |
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".