Abstract
Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique ‘representation’ of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure–activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.
Original language | English |
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Pages (from-to) | 924-932 |
Number of pages | 9 |
Journal | Nature Nanotechnology |
Volume | 17 |
Issue number | 9 |
Early online date | 18 Aug 2022 |
DOIs | |
Publication status | Published - Sept 2022 |
Bibliographical note
Funding Information:This work was funded via the European Union’s H2020 project NanoSolveIT (grant agreement number 814572), with additional support from the EU H2020 project NanoInformaTIX (grant agreement number 814426) and EU H2020 project PATROLS (grant agreement number 760813). V.L. and J.S. were supported by Science Foundation Ireland grant number 16/IA/4506.
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