RESUMEN
Photocatalytic covalent organic frameworks (COFs) are typically constructed with rigid aromatic linkers for crystallinity and extended π-conjugation. However, the essential hydrophobicity of the aromatic backbone can limit their performances in water-based photocatalytic reactions. Here, we for the first time report the synthesis of hydrophilic COFs with aliphatic linkers [tartaric acid dihydrazide (TAH) and butanedioic acid dihydrazide] that can function as efficient photocatalysts for H2O2 and H2 evolution. In these hydrophilic aliphatic linkers, the specific multiple hydrogen bonding networks not only enhance crystallization but also ensure an ideal compatibility of crystallinity, hydrophilicity, and light harvesting. The resulting aliphatic linker COFs adopt an unusual ABC stacking, giving rise to approximately 0.6 nm nanopores with an improved interaction with water guests. Remarkably, both aliphatic linker-based COFs show strong visible light absorption, along with a narrow optical band gap of â¼1.9 eV. The H2O2 evolution rate for TAH-COF reaches up to 6003 µmol h-1 g-1, in the absence of sacrificial agents, surpassing the performance of all previously reported COF-based photocatalysts. Theoretical calculations reveal that the TAH linker can enhance the indirect two-electron oxygen reduction reaction for H2O2 production by improving the O2 adsorption and stabilizing the *OOH intermediate. This study opens a new avenue for constructing semiconducting COFs using nonaromatic linkers.
RESUMEN
The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor's information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6 cm, and the maximum longitudinal deviation is 16 cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13 cm, the average lateral deviation is less than 7 cm, and the standard lateral deviation is less than 8 cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.
RESUMEN
Dynamic chiral superstructures are of vital importance for understanding the organization and function of chirality in biological systems. However, achieving high conversion efficiency for photoswitches in nanoconfined architectures remains challenging but fascinating. Herein, we report a series of dynamic chiral photoswitches based on supramolecular metallacages through the coordination-driven self-assembly of dithienylethene (DTE) units and octahedral zinc ions, thereby successfully achieving an ultrahigh photoconversion yield of 91.3% in nanosized cavities with a stepwise isomerization mechanism. Interestingly, the chiral inequality phenomenon is observed in metallacages, resulting from the intrinsic photoresponsive chirality in the closed form of the dithienylethene unit. Upon hierarchical organization, we establish a dynamic chiral system at the supramolecular level, featuring chiral transfer, amplification, induction, and manipulation. This study provides an intriguing idea to simplify and understand chiral science.