4.0 Article

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations

期刊

ACS NANOSCIENCE AU
卷 3, 期 3, 页码 211-221

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsnanoscienceau.2c00055

关键词

detonation nanodiamonds; Cryo-TEM; SAXS; machine learning; coarse-grained molecular dynamics; modeling

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Understanding the polydispersity of nanoparticles is crucial for their application as drug delivery carriers in biomedical field. Detonation nanodiamonds (DNDs), synthesized through detonation process, have great potential for drug delivery due to their stability and biocompatibility. However, their aggregate formation is poorly understood. In this study, we propose a novel characterization method using machine learning and cryo-transmission electron microscopy to characterize the unique colloidal behavior of DNDs, and we explain the differences in aggregation behavior between positively and negatively charged DNDs using small-angle X-ray scattering and mesoscale simulations. This method can be applied to other complex particle systems, providing essential knowledge for safe implementation of nanoparticles in drug delivery.
Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3-5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery.

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