High-Throughput Experimentation and Machine Learning-Assisted Optimization of Iridium-Catalyzed Cross-Dimerization of Sulfoxonium Ylides.

A novel and convenient approach that combines high-throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross-dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide-, ketone-, ester-, and N-heterocycle-substituted unsymmetrical E-alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step-economy, and excellent chemoselectivity and functional group compatibility.

Selective recovery of vanadium and scandium by ion exchange with D201 and solvent extraction using P507 from hydrochloric acid leaching solution of red mud.

D201 resin and P507 extractant diluted with sulfonated kerosene were used to respectively separate vanadium and scandium, and impurity ions from hydrochloric acid leaching solution of red mud. More than 99% of vanadium was selectively adsorbed from the hydrochloric acid leaching solution under the conditions of pH value of 1.8, volume ratio of leaching solution to resin of 10, and flow rate of 3.