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Abstract

A novel g-C3N4nanoparticle@porous g-C3N4(CNNP@PCN) composite has been successfully fabricated by loading g-C3N4nanoparticles on the porous g-C3N4matrix viaa simply electrostatic self-assembly method. The composition, morphological structure, optical property, and photocatalytic performance of the composite were evaluated by various measurements, including XRD, SEM, TEM, Zeta potential, DRS, PL, FTIR, and XPS. The results prove that the nanolization of g-C3N4leads to an apparent blueshift of the absorption edge, and the energy band gap is increased from 2.84 eV of porous g-C3N4to 3.40 eV of g-C3N4nanoparticle (Fig. 6). Moreover, the valence band position of the g-C3N4nanoparticle is about 0.7 eV lower than that of porous g-C3N4. Therefore, the photo-generated holes and electrons in porous g-C3N4can transfer to the conduction band of g-C3N4nanoparticle, thereby obtaining higher separation efficiency of photo-generated carriers as well as longer carrier lifetime. Under visible-light irradiation, 6CNNP@PCN exhibits the highest photocatalytic performance (Fig. 8) on MB, which is approximately 3.4 times as that of bulk g-C3N4.

Background: The non-coding RNA identification at the organelle genome level is a challenging task. In our previous work, an ncRNA dataset with less than 80% sequence identity was built, and a method incorporating an increment of diversity combining with support vector machine method was proposed. Objective: Based on the ncRNA_361 dataset, a novel decision-making method-an improved KNN (iKNN) classifier was proposed. Methods: In this paper, based on the iKNN algorithm, the physicochemical features of nucleotides, the degeneracy of genetic codons, and topological secondary structure were selected to represent the effective ncRNA characters. Then, the incremental feature selection method was utilized to optimize the feature set. Results: The results of iKNN indicated that the decision-making method of mean value is distinctly superior to the traditional decision-making method of majority vote the Increment of Diversity Combining Support Vector Machine (ID-SVM). The iKNN algorithm achieved an overall accuracy of 97.368% in the jackknife test, when k=3. Conclusion: It should be noted that the triplets of the structure-sequence mode under reading frames not only contains the entire sequence information but also reflects whether the base was paired or not, and the secondary structural topological parameters further describe the ncRNA secondary structure on the spatial level. The ncRNA dataset and the iKNN classifier are freely available at http://202.207.14.87:8032/fuwu/iKNN/index.asp.

This paper studies the consensus problem of a second-order multiagent system (MAS) with fixed communication delay under the structure of leaderless and leader-following systems. By using graph theory and finite-time control scheme, a distributed control protocol is proposed for each agent to reach consensus in a finite time. In practical application, the time delay of states is unavoidable, and for this, the consensus method is supposed to be extended to solve the time-delay problem. Thus, a finite-time consensus protocol with communication time delay is proposed in this paper. Compared with the general consensus method, the reliability and convergence speed of the system are increased by using the finite-time control. In addition, the protocol is distributed, and all agents have only local interactions. Finally, the effectiveness of the proposed protocol is verified by two numerical simulations.