Benzoic Acid: Electrode-Regenerated Molecular Catalyst to Boost Cycloolefin Epoxidation.

Stoichiometric oxidants are always consumed in organic oxidation reactions. For example, olefins react with peroxy acids to be converted to epoxy, while the oxidant, peroxy acid, is downgraded to carboxylic acid. In this paper, we aim to regenerate carboxylic acid into peroxy acid through electric water splitting at the anode, in order to construct an electrochemical catalytic cycle to accomplish the cycloolefin epoxidation reaction. Benzoic acid, which can be strongly adsorbed onto the anode and rapidly converted to peroxy acid, was selected to catalyze the cycloolefin epoxidation. Furthermore, the peroxybenzoic acid will be further activated on the electrode to fulfill the epoxidation and release the benzoic acid to complete the catalytic cycle. In this designed reaction cycle, benzoic acid acts as a molecular catalyst with the assistance of the electrode-generated reactive oxygen species (ROS). This method can successfully reform the consumable oxidants to molecular catalysts, which can be generalized to other green organic syntheses.

Electrochemical oxidation of styrene to benzaldehyde by discrimination of spin-paired π electrons.

The oxidation of styrene to benzaldehyde has been a considerable challenge in the electrochemical synthesis of organic compounds because styrene is more easily oxidized to benzoic acid. In this work, MnO2 with an asymmetric electronic configuration is designed to discriminate the spin-paired π electrons of styrene. One of these discriminated π electrons combined with reactive oxygen species (ROS), ˙OH, ˙OOH, etc., produced simultaneously on a MnO2/(Ru0.3Ti0.7)O2/Ti bifunctional anode, to form benzaldehyde via Grob fragmentation, rather than benzoic acid. However, only benzoic acid is obtained from the oxidation of styrene on the anodes MOs/(Ru0.3Ti0.7)O2/Ti, where MOs are other metal oxides with symmetric electronic configurations.

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion.

As affected by limited information and the complex background, the accuracy of small-target water-floating garbage detection is low. To increase the detection accuracy, in this research, a small-target detection method based on APM-YOLOv7 (the improved YOLOv7 with ACanny PConv-ELAN and MGA attention) is proposed. Firstly, the adaptive algorithm ACanny (adaptive Canny) for river channel outline extraction is proposed to extract the river channel information from the complex background, mitigating interference of the complex background and more accurately extracting the features of small-target water-floating garbage. Secondly, the lightweight partial convolution (PConv) is introduced, and the partial convolution-efficient layer aggregation network module (PConv-ELAN) is designed in the YOLOv7 network to improve the feature extraction capability of the model from morphologically variable water-floating garbage. Finally, after analyzing the limitations of the YOLOv7 network in small-target detection, a multi-scale gated attention for adaptive weight allocation (MGA) is put forward, which highlights features of small-target garbage and decreases missed detection probability. The experimental results showed that compared with the benchmark YOLOv7, the detection accuracy in the form of the mean Average Precision (mAP) of APM-YOLOv7 was improved by 7.02%, that of mmAP (mAP0.5:0.95) was improved by 3.91%, and Recall was improved by 11.82%, all of which meet the requirements of high-precision and real-time water-floating garbage detection and provide reliable reference for the intelligent management of water-floating garbage.