An increasing demand for freshwater inspires further understanding of the mechanism of water diffusion in reverse-osmosis membranes for the development of high-performance membranes for desalination. Water diffusion has a close relationship with the structural and dynamical characteristics of hydrogen bonds, which is not well-understood for the confining environment inside the polyamide membrane at the molecular level. In this work, an atomistic model of a highly cross-linked polyamide membrane was built with an equilibrated mixture of m-phenylenediamine and trimesoyl chloride monomers. The structure and dynamics of water in the regions from the bulk phase to the membrane interior were investigated by molecular dynamics simulations. Explicit hydrogen bond criteria were determined for hydrogen-bonding analysis. The local distribution and orientation of water reveal that the hydrogen-bonding affinity of the hydrophilic functional groups of polymers inhibits water diffusion inside the membrane. The affinity helps to produce percolated water channels across the membrane. The hydrogen-bonding structures of water in different regions indicate dehydration is required for the entry of water into the polyamide membrane, which dominates water flux across the membrane. This paper not only deepens the understanding of the structure and dynamics of water confined in the polyamide membrane but also stimulates the future work on high-performance reverse-osmosis membranes.
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 large-sized 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.
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