The interest in curtailing environmental pollution issues through physical separation processes has inspired an extensive search for novel nanoporous materials with exceptional adsorption capabilities.Covalent triazin...The interest in curtailing environmental pollution issues through physical separation processes has inspired an extensive search for novel nanoporous materials with exceptional adsorption capabilities.Covalent triazine frameworks(CTFs),emerged as a class of crystalline covalent organic frameworks(COFs),have been widely examined for various separation applications,owing to their large porosity,high stability,and rich nitrogen(N)doping.The development of CTFs for efficient adsorption of mercury(Ⅱ)(Hg^(2+))is of great importance for the field,whereas it is rarely attempted,on account of limited synthetic strategies and unknown structural-property relations of conventional CTFs derived from ionothermal approaches.Herein,we report rational synthesis of a crystalline CTF with methylthio pendant arms for efficient removal of Hg^(2+)with an exceptional capacity of 751 mg·g^(-1),ranking at the top among previously-reported adsorbents.This work may open up new possibility in the synthesis of COFs for various separations.展开更多
The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operationa...The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.展开更多
基金The National Natural Science Foundation of China(22078349,22005319,52170109)Self-deployment Program from Lanzhou Institute of Chemical Physics(E30159SQ).
文摘The interest in curtailing environmental pollution issues through physical separation processes has inspired an extensive search for novel nanoporous materials with exceptional adsorption capabilities.Covalent triazine frameworks(CTFs),emerged as a class of crystalline covalent organic frameworks(COFs),have been widely examined for various separation applications,owing to their large porosity,high stability,and rich nitrogen(N)doping.The development of CTFs for efficient adsorption of mercury(Ⅱ)(Hg^(2+))is of great importance for the field,whereas it is rarely attempted,on account of limited synthetic strategies and unknown structural-property relations of conventional CTFs derived from ionothermal approaches.Herein,we report rational synthesis of a crystalline CTF with methylthio pendant arms for efficient removal of Hg^(2+)with an exceptional capacity of 751 mg·g^(-1),ranking at the top among previously-reported adsorbents.This work may open up new possibility in the synthesis of COFs for various separations.
文摘The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.
