Representing and describing nanomaterials in predictive nanoinformatics.

Area of Science:

• Materials Science
• Nanotechnology
• Computational Chemistry

Background:

• Engineered nanomaterials (ENMs) possess unique nanoscale properties (1-100 nm) enabling novel products.
• These properties, while advantageous, present poorly understood risks to human health and the environment.
• The safe-and-sustainable-by-design (SSbD) approach is crucial for developing new ENMs.

Purpose of the Study:

• To explore the development of nanomaterial representations and nanodescriptors.
• To enhance the reliability of computational modeling for designing safer ENMs.
• To integrate experimental data with theoretical models for optimizing ENM functionality and minimizing hazards.

Main Methods:

• Utilizing nanodescriptors (physicochemical characteristics like size, shape, composition) to represent ENMs.
• Applying nanoinformatics tools, including quantitative structure-activity/property relationship (QSAR/QSPR) models.
• Integrating experimental data with computational and theoretical models.

Main Results:

• Physicochemical characteristics create unique ENM representations ('nanodescriptors').
• Nanodescriptors can be used in QSAR/QSPR models for optimizing ENMs at the design stage.
• Computational screening can identify optimal nanostructures and mitigate hazardous features early in manufacturing.

Conclusions:

• Reliable computational modeling is essential for designing safer and more sustainable ENMs.
• Successful large-scale adoption of ENMs depends on integrating experimental and computational data.
• Further development of nanomaterial representations and nanodescriptors is needed to improve computational modeling accuracy.