One-pot synthesis of interfacially bonded Bi4O5Br2/Bi2S3 Z-scheme heterostructures with boosted photocatalysis towards dodecylbenzenesulfonate and real hotel laundry wastewater.

The ubiquitous contamination of surfactants in wastewater has raised global concerns. Photocatalysis is deemed as a promising yet challenging approach for the decomposition of surfactant residues. Herein, a novel Z-scheme heterojunction of Bi4O5Br2/Bi2S3 with covalent S-O bonds was prepared via a facile one-pot hydrothermal and subsequent annealing process. The prepared optimal Bi4O5Br2/Bi2S3 composite exhibited remarkable photo-degradation activity towards the sodium dodecylbenzene sulfonate (SDBS). The Z-scheme reaction mechanism was proposed and validated by meticulous analysis of quenching tests, ESR spectroscopy and DFT calculations. Furthermore, the presence of chemical S-O linkages between Bi4O5Br2 and Bi2S3 was identified via FT-IR and XPS analyses, which served as a distinct bridge to modify the Z-scheme route for carrier transport. The Z-scheme heterostructure, in conjunction with chemical S-O bonds, synergistically enhanced the separation rate of electron-hole pairs and thus greatly boosted the photocatalytic activity. Additionally, the possible degradation pathways of SDBS were proposed by using HR-MS technology. Moreover, real hotel laundry wastewater could be efficiently disposed by the photocatalysis of the Bi4O5Br2/Bi2S3 with a decrease in the COD value from 428 to 74 mg/L, indicating that the fabricated Z-scheme heterojunction hold great promise for effectively removing refractory surfactant contaminants from aquatic environment.

Machine Learning Topological Phases with a Solid-State Quantum Simulator.

We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks-a class of deep feed-forward artificial neural networks with widespread applications in machine learning-can be trained to successfully identify different topological phases protected by chiral symmetry from experimental raw data generated with a solid-state quantum simulator. Our results explicitly showcase the exceptional power of machine learning in the experimental detection of topological phases, which paves a way to study rich topological phenomena with the machine learning toolbox.