Understanding the zeta potential of nanomaterials through predictive nanoinformatics

Abstract

Nanomaterials are employed extensively in materials engineering and nanomedicine due to their unique properties. Therefore, understanding how they interact in the environment is imperative to ensure the successful development of complex and safe nanomaterials. The zeta potential is a principal determinant of the behavior of nanomaterials, including surface charge characteristics and stability. This article presents the first comprehensive review of recent advances in the computational study of the relationships between nanomaterial structure and its zeta potential. The implementation of data-driven methods for this purpose is analyzed, particularly the machine learning-based modeling of nano-quantitative structure-property relationships. Moreover, this review examines the application of physics-based methods, specifically quantum mechanics, including density functional theory calculations, molecular dynamics, and Monte Carlo simulations, to predict and understand the factors influencing the zeta potential of nanomaterials in environments, including medium-nanosurface interactions, at the molecular level. The importance of theoretically characterizing the molecular structure by utilizing complex nanodescriptors and their mechanistic interpretation have also been discussed. In summary, this article describes the application of nanoinformatics in predicting the zeta potential of NMs, from its evolving landscape to its challenges and future directions.