Unsteady Dynamics of Vesicles in a Confined Poiseuille Flow

The dynamic behaviors of a single vesicle bounded by the cylindrical wall in a Poiseuille flow were investigated by considering different confinements and dimensionless shear rates. By observing the evolution of two adjacent particles attached to the internal and external surfaces of the spherical vesicles, we found they had the same frequency. The vorticity trajectories formed by the time-tracing of the particles on the membrane are parallel, which can be identified as the unsteady rolling motion of the membranes due to the unfixed axis. The dynamic behaviors of vesicles are associated with the confinement degree and the dimensionless shear rate. The smaller dimensionless shear rate will result in the slower frequency of the rolling by examining the velocity of the rolling. The weakened rolling motion under stronger confinements is observed by measuring the evolution of the orientation angles. The changes of revolution axes over time can be interpreted by the lateral excursion of the center of mass on the orthogonal plane of the flow.

Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes

Machine learning models for exploring structure-property relation for hydroxyapatite nanoparticles(HANPs)are still lacking.A multiscale multisource dataset is presented,including both experimental data(TEM/SEM,XRD/crystallinity,ROS,anti-tumor effects,and zeta potential)and computation results(containing 41,976 data samples with up to 9768 atoms)of nanoparticles with different sizes and morphologies at density functional theory(DFT),semi-empirical DFTB,and force field,respectively.Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance.To avoid the pre-determination of features,we also developed a predictive deep learning model within the framework of graph convolution neural network with good generalizability.Energies with DFT accuracy are achievable for largesized nanoparticles from the learned correlations and scale functions for mapping different theoretical levels and particle sizes.The simulated XRD spectra and crystallinity values are in good agreement with experiments.