The self-assembly of diblock copolymers under soft confinement is studied systematically using a simulated annealing method applied to a lattice model of polymers. The soft confinement is realized by the formation of polymer droplets in a poor solvent environment. Multiple sequences of soft confinement-induced copolymer aggregates with different shapes and self-assembled internal morphologies are predicted as functions of solvent-polymer interaction and the monomer concentration. It is discovered that the self-assembled internal morphology of the aggregates is largely controlled by a competition between the bulk morphology of the copolymer and the solvent-polymer interaction, and the shape of the aggregates can be non-spherical when the internal morphology is anisotropic and the solvent-polymer interaction is weak. These results demonstrate that droplets of diblock copolymers formed in poor solvents can be used as a model system to study the self-assembly of copolymers under soft confinement.

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With the speedy growth of economic development, the imbalance of energy supply and demand pose a critical challenge for the energy security of our country. Meanwhile, the increasing and excessive energy consumption lead to the greenhouse effect and atmospheric pollution, greatly threatening the survival and development of human beings. This study integrated two nighttime light remote sensing datasets, namely Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data, to extend the temporal coverage of the study. Then, the distributions of China’s energy consumption from 1995 to 2016 at a 1-km resolution were estimated using different models and the spatiotemporal variations of energy consumption were explored on the basis of the best estimated results. Next, the factors influencing China’s energy intensity on the provincial level were investigated based on the spatial econometric model. The results show that: (1) The integrated nighttime light datasets can be successfully applied to estimate the dynamic changes of energy consumption. Moreover, the panel data model established in our research performed better than the quadratic polynomial model. (2) During the observation period, the energy consumption in China significantly increased, especially in the Yangtze River Delta, the Pearl River Delta, the Beijing–Tianjin–Hebei region, eastern coastal cities, and provincial capitals. (3) Different from the random spatial distribution pattern of energy consumption on the provincial level, the spatial distribution of energy consumption on the prefectural level has significant clusters, and its spatial agglomeration was strengthened year by year during the research period. (4) The spatial Durbin model (SDM) with a spatial fixed effect has been proved to be more suitable to explore the impact mechanism of China’s energy consumption. Among the four socio-economic factors, industrial structure has the greatest impact on the provincial energy intensity in China. Moreover, the changes in industrial structure and foreign direct investment (FDI) can not only influence the local energy intensity but also affect the energy intensity of the neighboring provinces.