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Antibacterial Effects of Carbon Nanotubes: Size Does Matter!
Abstract
We provide the first evidence that the size (diameter) of carbon nanotubes (CNTs) is a key factor governing their antibacterial effects and that the likely main CNT-cytotoxicity mechanism is cell membrane damage by direct contact with CNTs. Experiments with well-characterized single-walled carbon nanotubes (SWNTs) and multiwalled carbon nanotubes (MWNTs) demonstrate that SWNTs are much more toxic to bacteria than MWNTs. Gene expression data show that in the presence of both MWNTs and SWNTs, Escherichia coli expresses high levels of stress-related gene products, with the quantity and magnitude of expression being much higher in the presence of SWNTs.
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Citations
Cited by
Banerjee, I., Pangule, R. C., & Kane, R. S. (2011) · Advanced Materials · doi:10.1002/adma.201001215 · Cited by 2,749
Applications of nanotechnology in water and wastewater treatment
Qu, X., Alvarez, P. J. J., & Li, Q. (2013) · Water Research · doi:10.1016/j.watres.2012.09.058 · Cited by 2,444
Li, Q., Mahendra, S., Lyon, D. Y., Brunet, L., Liga, M. V., Li, D., & Alvarez, P. J. J. (2008) · Water Research · doi:10.1016/j.watres.2008.08.015 · Cited by 2,385
Graphene-based nanomaterials for drug delivery and tissue engineering
Goenka, S., Sant, V., & Sant, S. (2014) · Journal of Controlled Release · doi:10.1016/j.jconrel.2013.10.017 · Cited by 1,311
Lee, J., Mahendra, S., & Alvarez, P. J. J. (2010) · ACS Nano · doi:10.1021/nn100866w · Cited by 750
Metadata
- Authors
- Kang, S., Herzberg, M., Rodrigues, D. F., & Elimelech, M.
- Venue
- Langmuir
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-05-30
- Volume
- 24
- Issue
- 13
- Pages
- 6409-6413
- ISSN
- 0743-7463 (ISSN-L)
- 1520-5827
- OpenAlex
- W1973383827
- Cited by
- 1,182
- References
- 0
- FWCI
- 27.29
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
