Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations.
Inga C KuschnerusHaotian WenJuanfang RuanXinrui ZengChun-Jen SuYen-Ju ChengGeorge OpletalAmanda S BarnardMing LiuMasahiro NishikawaShery L Y ChangPublished in: ACS nanoscience Au (2023)
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.
Keyphrases
- electron microscopy
- drug delivery
- molecular dynamics simulations
- machine learning
- high resolution
- molecular dynamics
- cancer therapy
- healthcare
- molecular docking
- artificial intelligence
- drug release
- primary care
- big data
- deep learning
- magnetic resonance imaging
- computed tomography
- monte carlo
- tissue engineering
- dual energy