Carbonaceous Nanocomposites for Biomedical Applications as High-Drug Loading Nanocarriers for Sustained Delivery: A Review
Bibliographic record
Abstract
Low drug loading and high initial burst release are common drawbacks for most polymeric nanocarriers in their biomedical applications. This review emphasizes the use of unconventional carbonaceous nanocomposites as functional carriers to improve the drug loading capacity and their capability of protecting drugs from the surrounding environment. The unique properties of typical carbonaceous nanocarriers, including nanotube, graphene/graphite, fullerene, and nanodiamonds/diamond-like carbon, are presented. Advanced methods for the surface functionalization of carbonaceous nanocarriers are described, followed by a summary of the most appealing demonstrations for their efficient drug loading and sustained release in vitro or in vivo. The fundamental drug delivery concepts based on controlling mechanisms, such as targeting and stimulation with pH, chemical interactions, and photothermal induction, are discussed. Additionally, the challenges involved in the full utilization of carbonaceous nanocomposites are described, along with the future perspectives of their use for enhanced drug delivery. Finally, despite its recent emergence as a drug carrier, carbon-based nanocellulose has been viewed as another promising candidate. Its structural geometry and unique application in the biomedical field are particularly discussed. This paper, for the first time, taxonomizes nanocellulose as a carbon-based carrier and compares its drug delivery capacities with other nanocarbons. The outcome of this review is expected to open up new horizons of carbonaceous nanocomposites to inspire broader interests across multiple disciplines.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".