Fe/Zr-MOFs constructed by a sunlight-responsive ligand for efficient photocatalytic nitrogen fixation under ambient condition.

Photocatalytic nitrogen fixation opens new opportunities for sustainable and healthier futures, and developing effective and inexpensive photocatalysts is the key. We use the ligand 3,3',5,5'-azomellitic acid (H4abtc) to connect with Fe clusters and Zr clusters to form stable metal-organic frameworks (MOFs) Fe-abtc and Zr-abtc, both of which are responsive to visible lights for nitrogen fixation. It is worth noting that the presence of NN in the ligand makes it respond to visible lights. The tetracarboxyl group is connected to the metal cluster to form a stable structure. The field-only surface integral method verified that the ligands were successfully applied into the synthesized MOF particles, which expanded the photoresponse range and enhanced the photonic interactions of the synthesized photocatalysts compared with pure MOF particles. The best photocatalytic nitrogen fixation performance of Fe-abtc and Zr-abtc is 49.8 µmol·g(cat.)-1·h-1 and 35.7 µmol·g(cat.)-1·h-1, respectively, the apparent quantum efficiency (AQY) of the sample Fe-abtc is 0.56 %, and the reliability of the source of N element is proved by the isotope 15N2. This work provides a new idea for the design of cheap and effective MOFs for photocatalytic nitrogen fixation.

An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection.

With the rapid development of network technologies and the increasing amount of network abnormal traffic, network anomaly detection presents challenges. Existing supervised methods cannot detect unknown attack, and unsupervised methods have low anomaly detection accuracy. Here, we propose a clustering-based network anomaly detection model, and then a novel density peaks clustering algorithm DPC-GS-MND based on grid screening and mutual neighborhood degree for network anomaly detection. The DPC-GS-MND algorithm utilizes grid screening to effectively reduce the computational complexity, improves the clustering accuracy through mutual neighborhood degree, and also defines a cluster center decision value for automatically selecting cluster centers. We implement complete experiments on two real-world datasets KDDCup99 and CIC-IDS-2017, and the experimental results demonstrated that the proposed DPC-GS-MND can detect network anomaly traffic with higher accuracy and efficiency. Together, it has a good application prospect in the network anomaly detection system in complex network environments.