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Bacterial inactivation by using near- and supercritical carbon dioxide
Open accessAbstract
The three most common methods of sterilization in use today are ethylene oxide exposure, γ-irradiation, and steam sterilization. Each of these methods has serious limitations for the sterilization of some materials used in medicine, especially thermally and hydrolytically sensitive polymers by themselves and in combination with proteins. In this work, we demonstrate a potential new method of sterilization by using supercritical fluid carbon dioxide. Using this method we achieve complete inactivation of a wide variety of bacterial organisms at moderate temperatures and in the absence of organic solvents or irradiation. Sterilization is a function of both the proximity to the fluid’s critical point and the chemical nature of the fluid itself. When biodegradable polymers poly(lactic- co- glycolic) acid and polylactic acid were included in the sterilization process, there was no effect on the inactivation efficiency, yet no physical or chemical damage to these thermally and hydrolytically labile materials was observed.
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Langer, R. (1990) · Science · doi:10.1126/science.2218494 · Cited by 1,778
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Metadata
- Authors
- Dillow, A. K., Dehghani, F., Hrkach, J. S., Foster, N. R., & Langer, R.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 1999-08-31
- Volume
- 96
- Issue
- 18
- Pages
- 10344-10348
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2004019285
- Cited by
- 323
- References
- 1
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- green
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- Dillow, A. K., Dehghani, F., Hrkach, J. S., Foster, N. R., & Langer, R. (1999). Bacterial inactivation by using near- and supercritical carbon dioxide. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.96.18.10344
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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.