Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Graphene. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

SO2-Tolerant Selective Catalytic Reduction of NOx over Meso-TiO2@Fe2O3@Al2O3 Metal-Based Monolith Catalysts

Abstract: It is an intractable issue to improve the low-temperature SO2-tolerant selective catalytic reduction (SCR) of NOx with NH3 because deposited sulfates are difficult to decompose below 300 °C. Herein, we established a low-temperature self-prevention mechanism of mesoporous-TiO2@Fe2O3 core–shell composites against sulfate deposition using experiments and density functional theory. The mesoporous TiO2-shell effectively restrained the deposition of FeSO4 and NH4HSO4 because of weak SO2 adsorption and promoted NH4HSO4 decomposition on the mesoporous-TiO2. The electron transfer at the Fe2O3 (core)-TiO2 (shell) interface accelerated the redox cycle, launching the “Fast SCR” reaction, which broadened the low-temperature window. Engineered from the nano- to macro-scale, we achieved one-pot self-installation of mesoporous-TiO2@Fe2O3 composites on the self-tailored AlOOH@Al-mesh monoliths. After the thermal treatment, the mesoporous-TiO2@Fe2O3@Al2O3 monolith catalyst delivered a broad window of 220–420 °C with NO con...

Modelling Chemical Reasoning to Predict and Invent Reactions

Abstract: The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.