Carbonaceous Nanocomposites for Biomedical Applications as High-Drug Loading Nanocarriers for Sustained Delivery: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it