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Covalent triazine frameworks (CTFs), noted for their rich nitrogen content, have attracted significant attention as promising photocatalysts. However, the structural complexity introduced by the diversity of nitrogen atoms in nitrogen‐rich CTFs poses a substantial challenge in discovering high‐performance CTFs. To address this challenge, a machine‐learning approach is developed to rationally design nitrogen‐rich CTFs, which is subsequently validated through experimental methods. A framework is employed based on the special orthogonal group in three dimensions (SO(3))‐invariant graph neural networks to predict photocatalytic properties of CTFs structures. This approach achieves exceptionally high accuracies with R2 scores exceeding 0.98. From a dataset of 14920 CTFs structures, this framework identifies 45 high‐performance candidates. Guided by these predictions, a novel CTF structure, pyridine‐2,5‐dicarbaldehyde (CTF‐DCPD) is selected and successfully synthesized, which exhibits an ultrahigh hydrogen evolution rate of 17.70 mmol g−1 h−1. This rate significantly surpasses that of the widely studied CTF‐1,4‐dicyanobenzene (CTF‐DCB, 10.41 mmol g−1 h−1). This work provides a new paradigm for machine learning to accelerate materials development, which can be generalized to the development of other functional materials. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing stands as a pivotal technological approach for enhanced tight gas recovery. This paper investigates the influences of geological and engineering parameters on the vertical extension mechanism of hydraulic fractures. In addition, the feasibility and effectiveness of fracture height prediction method and various fracture height control techniques have been examined. The results indicate that the height of hydraulic fractures decreases with an increase in the thickness of the barrier layers, the stress difference between the barrier and reservoir layers, the difference in tensile strength, and the difference in fracture toughness, whereas it increases with the increasing of difference in elastic modulus between the barrier and reservoir layers. Compred with the difference in Poisson's ratio, the volume of fracturing fluid, discharge rate, and fluid viscosity have little impactd. The influence of these factors on fracture height, in descending order, is stress difference between barrier and reservoir layers, fracturing fluid viscosity, fracturing discharge, fracturing fluid volume, barrier layer thickness, tensile strength difference between barrier and reservoir layers, elastic modulus difference between barrier and reservoir layers, Poisson's ratio difference between barrier and reservoir layers. Furthermore, based on typical geomechanic and reservoir parameters of the target area, a fracture height prediction workflow has been developed. Engineering practice has proven the reliability of fracture height prediction method. The results of this study provide theoretical support and guidance for predicting fracture morphology, controlling fracture height in the hydraulic fracturing development of the tight gas reservoir, and optimizing fracturing process design. [ABSTRACT FROM AUTHOR]

Graphitic carbon nitride (g-C3N4) is an attractive photocatalyst and a thermal reflux method was used to significantly increase the yield of g-C3N4 nanostructures and enhance their photocatalytic activity for H2 production. When raw material was placed in an inclined quartz tube with the bottom in the high temperature zone and the top in the low temperature zone, the vapor of the raw material was condensed from the top and refluxed to the bottom to then participate in the reaction again. This method can significantly improve the yield of g-C3N4 with an increase of 28.2%. When used as the photocatalyst in water splitting, these g-C3N4 nanostructures exhibit excellent activity with an H2 production of 2102 μmol h−1 g−1 under visible irradiation, which is 43.2% higher than the sample prepared by a traditional thermal polymerization method. This method is also promising to use for the synthesis of similar two-dimensional nanostructures for effective H2 production. [ABSTRACT FROM AUTHOR]

Flexible job shop scheduling problem is the allocation of available shared resources and the sequencing of processing tasks within a certain period of time to meet certain or certain specific production indicators. The research and application of effective scheduling methods and optimization technologies are the foundation and key to realizing advanced manufacturing and improving production efficiency. Improving the production scheduling plan can greatly improve production efficiency and resource utilization, thereby enhancing the competitiveness of enterprises. Therefore, the production scheduling problem has always been a research hotspot in manufacturing systems. In this paper, we introduce the opposition-based learning strategy and combine it with whale optimization algorithm to solving flexible job shop scheduling problem better. 10 FJSP cases are introduced to test the performance of our algorithm and other comparison algorithms. The results obtrain show that our algorithm is more better and practical than other algorithm when dealing with FJSP cases.

In order to improve the accuracy of the parameters during the process of rice straw shredding and returning to the field in discrete element simulation, the stubble straw in the field after rice harvest were taken as the research object, and the contact parameters between straw and steel, straw and straw were measured through the slope test. Then based on simulation model and through the stacking angle and shear test, the bonding radius between straw model particles, tangential critical stress, normal critical stress, coefficient of restitution, static friction coefficient and dynamic friction coefficient were taken as the test factors, taking the shear resistance and radial stacking angle of straw as evaluation indicators, a multi-objective optimization simulation calibration experiment were conducted using the Box-Behnken experimental scheme. The results showed that the bonding radius between straw particles was 1.06 mm, and the normal critical stress of straw was 4.77x1010 Pa, tangential critical stress was 4.67x106 Pa, the collision restitution coefficient between straw and straw was 0.21, the static Friction coefficient was 0.19, and the rolling friction coefficient was 0.09. The results of the simulation test results under the optimal parameter combination conditions and the physical test showed that the relative errors of the shear resistance and radial stacking angle of straw in the simulation test and physical test were 1.7% and 2.8%, respectively. This study could provide reference for the discrete element simulation analysis between straw and straw, as well as between straw and agricultural machinery. [ABSTRACT FROM AUTHOR]