Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11552
Title: Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology
Authors: Öksel Karakuş, Ceyda
Winkler, David A.
Keywords: Computational nanotoxicology
Safe-by-design
Roadmap
Machine learning
Adverse biological effects
Nanomaterials regulation
Issue Date: 2021
Publisher: IOP Publishing
Abstract: The rapid rise of nanotechnology has resulted in a parallel rise in the number of products containing nanomaterials. The unusual properties that nano forms of materials exhibit relative to the bulk has driven intense research interest and relatively rapid adoption by industry. Regulatory agencies are charged with protecting workers, the public, and the environment from any adverse effects of nanomaterials that may also arise because of these novel physical and chemical properties. They need data and models that allow them to flag nanomaterials that may be of concern, while balancing potential stifling of commercial innovation. Roadmaps for the future of safe nanotechnology were defined more than a decade ago, but many roadblocks identified in these studies remain. Here, we discuss the roadblocks that are still hindering the effective application of informatics and predictive computational nanotoxicology methods from providing more effective guidance to nanomaterials regulatory agencies and safe-by-design rationale for industry. We describe how developments in high throughput synthesis, characterization, and biological assessment of nanomaterials will overcome many of these roadblocks, allowing a clearly defined roadmap for computational design of effective but safe-by-design nanomaterials to be realized.
URI: https://doi.org/10.1088/2399-1984/abe560
https://hdl.handle.net/11147/11552
ISSN: 2399-1984
Appears in Collections:Bioengineering / Biyomühendislik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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