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Summary: As a major greenhouse gas, the continuous increase of carbon dioxide (CO2) in the atmosphere has caused serious environmental problems, although CO2 is also an abundant, inexpensive, and nontoxic carbon source. Here, we use metal-organic framework (MOF) with highly ordered hierarchical structure as adsorbent and catalyst for chemical fixation of CO2 at atmospheric pressure, and the CO2 can be converted to the formate in excellent yields. Meanwhile, we have successfully integrated highly ordered macroporous and mesoporous structures into MOFs, and the macro-, meso-, and microporous structures have all been presented in one framework. Based on the unique hierarchical pores, high surface area (592 m2/g), and high CO2 adsorption capacity (49.51 cm3/g), the ordered macroporous-mesoporous MOFs possess high activity for chemical fixation of CO2 (yield of 77%). These results provide a promising route of chemical CO2 fixation through MOF materials. : Chemical Reactions in Materials Science; Chemical Synthesis; Materials Characterization Techniques Subject Areas: Chemical Reactions in Materials Science, Chemical Synthesis, Materials Characterization Techniques

As China's economic development brings the country out of poverty and into modernity, a long-lasting debate concerns whether the Chinese public's value system is also changing toward the so-called "modern values," or whether some distinctly traditional Chinese values remain unchanged. Using empirical data collected at three points in time during the 1990s and the first two decades of the 21st Century (1993, 2002, and 2011), I found that Chinese citizens who benefitted from urbanization, rising levels of education and employment in non-farm, knowledge-based industries displayed stronger modern values. People with stronger modern values are more likely to emphasize individual autonomy, competition, gender equality, and market transaction, among others. Some characteristics of the Chinese people, most importantly family values, however, seem to remain stable amidst rapid social changes.

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments., (© 2024. The Author(s).)