Solution-based Supramolecular Hierarchical Assembly of Frenkel Excitonic Nanotubes Driven by Gold Nanoparticle Formation and Temperature.

Translating nature's successful design principle of solution-based supramolecular self-assembling to broad applications─ranging from renewable energy and information technology to nanomedicine─requires a fundamental understanding of supramolecular hierarchical assembly. Though the forces behind self-assembly (e.g., hydrophobicity) are known, the specific mechanism by which monomers form the hierarchical assembly still remains an open question. A crucial step toward formulating a complete mechanism is understanding not only how the monomer's specific molecular structure but also how manifold environmental conditions impact the self-assembling process. Here, we elucidate the complex correlation between the environmental self-assembling conditions and the resulting structural properties by utilizing a well-characterized model system: well-defined supramolecular Frenkel excitonic nanotubes (NTs), self-assembled from cyanine dye molecules in aqueous solution, which further self-assemble into bundled nanotubes (b-NTs). The NTs and b-NTs inhabit distinct spectroscopic signatures, which allows the use of steady-state absorption spectroscopy to monitor the transition from NTs to b-NTs directly. Specifically, we investigate the impact of temperature (ranging from 23 °C, 55 °C, 70 °C, 85 °C, up to 100 °C) during in situ formation of gold nanoparticles to determine their role in the formation of b-NTs. The considered time regime for the self-assembling process ranges from 1 min to 8 days. With our work, we contribute to a basic understanding of how environmental conditions impact solution-based hierarchical supramolecular self-assembly in both the thermodynamic and the kinetic regime.

Nonlinear forces in urban thermal environment using Bayesian optimization-based ensemble learning.

Urbanization witnessed unprecedented development globally, which causes citizens and urban temperature to become increasingly intertwined. Although researchers were interested in the field, most studies focused on holistic linear links between the characteristics of the urban built-up environment and temperature. The study used Bayesian optimization ensemble learning and Shapley value to decouple the urban thermal environment by Landsat satellite data. This work's novelties reveal the specific driving effect of different value ranges of urban features in the overall process on the urban thermal environment and advancing an optimum observation buffer zone of the urban surface temperature. The study's results were only for daytime and Beijing scope. The following are the main findings: (1) The 2 km observation buffer zone is best to analyze the urban thermal environment for this dataset. (2) The ecological environment factors have a more significant effect on the urban temperature than the urban morphology factors. (3) In summer, when the vegetation coverage exceeds 58.1%, every 10% increase could reduce the temperature by 0.84 °C. In contrast to summer, when vegetation coverage exceeds 64.7% and 73.2%, respectively, in spring and fall, there will be a significant marginal utility. (4) The effect of the building height has seasonal variations. It has the greatest cooling effect in the spring when the height is between 18 m and 75 m, and the daytime surface temperature at the time of Landsat overpass will drop by 1.25 °C. These findings will aid in understanding how building construction influences urban surface temperature and provide statistical support for planners.