The DOI matches its Crossref record; 183 of the 183 references it cites were found in a registry or an index.
Biomedical applications of carbon nanomaterials: Drug and gene delivery potentials
Abstract
One of the major components in the development of nanomedicines is the choice of the right biomaterial, which notably determines the subsequent biological responses. The popularity of carbon nanomaterials (CNMs) has been on the rise due to their numerous applications in the fields of drug delivery, bioimaging, tissue engineering, and biosensing. Owing to their considerably high surface area, multifunctional surface chemistry, and excellent optical activity, novel functionalized CNMs possess efficient drug‐loading capacity, biocompatibility, and lack of immunogenicity. Over the past few decades, several advances have been made on the functionalization of CNMs to minimize their health concerns and enhance their biosafety. Recent evidence has also implied that CNMs can be functionalized with bioactive peptides, proteins, nucleic acids, and drugs to achieve composites with remarkably low toxicity and high pharmaceutical efficiency. This review focuses on the three main classes of CNMs, including fullerenes, graphenes, and carbon nanotubes, and their recent biomedical applications.
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1 retracted
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Cited by
Xu, Y., & Li, Z. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.031 · Cited by 404
Metadata
- Authors
- Mohajeri, M., Behnam, B., & Sahebkar, A.
- Venue
- Journal of Cellular Physiology
- Publisher
- Wiley
- Published
- 2018-08-04
- Volume
- 234
- Issue
- 1
- Pages
- 298-319
- ISSN
- 0021-9541 (ISSN-L)
- 1097-4652
- OpenAlex
- W2887860492
- Cited by
- 258
- References
- 183
- FWCI
- 10.38
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
