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Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials

Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials

Eleni Vrontaki, Thomas Mavromoustakos, Georgia Melagraki, Antreas Afantitis
Copyright: © 2017 |Pages: 25
ISBN13: 9781522517986|ISBN10: 1522517987|EISBN13: 9781522517993
DOI: 10.4018/978-1-5225-1798-6.ch002
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MLA

Vrontaki, Eleni, et al. "Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials." Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 20-44. https://doi.org/10.4018/978-1-5225-1798-6.ch002

APA

Vrontaki, E., Mavromoustakos, T., Melagraki, G., & Afantitis, A. (2017). Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials. In I. Management Association (Ed.), Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications (pp. 20-44). IGI Global. https://doi.org/10.4018/978-1-5225-1798-6.ch002

Chicago

Vrontaki, Eleni, et al. "Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials." In Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 20-44. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1798-6.ch002

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Abstract

In the last few decades, nanotechnology has been deeply established into human's everyday life with a great number of applications in cosmetics, textiles, electronics, optics, medicine, and many more. Although nanotechnology applications are rapidly increasing, the toxicity of some nanomaterials to living organisms and the environment still remains unknown and needs to be explored. The traditional toxicological evaluation of nanoparticles with the wide range of types, shapes, and sizes often involves expensive and time-consuming procedures. An efficient and cheap alternative is the development and application of predictive computational models using Quantitative Nanostructure-Activity Relationship (QNAR) methods. Towards this goal, researchers are mainly focused on the adverse effects of metal oxides and carbon nanotubes, but to date, QNAR studies are rare mainly because of the limited number of available organized datasets. In this chapter, recent studies for predictive QNAR models for the risk assessment of nanomaterials are reported and the perspectives of computational nanotoxicology that deeply relies on the intense collaboration between experimental and computational scientists are discussed.

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