An Expedient Method for the Umpolung Coupling of Enols with Heteronucleophiles**

Silver-Catalyzed Coupling of Ethynylbenziodoxolones with CO2 and Amines to Afford O-β-Oxoalkyl Carbamates

A novel three-component coupling reaction of ethynylbenziodoxolones (EBXs) with CO2 and amines has been achieved via silver catalysis, thereby providing an efficient method for the construction of a range of structurally diverse and valuable O-β-oxoalkyl carbamates. The transformation proceeds under mild reaction conditions and exhibits a wide substrate scope and good functional group compatibility. In addition, this strategy could be extended to the synthesis of α-acyloxyketones using carboxylic acids as the nucleophiles to react with EBXs.

Tailoring the CO2 Hydrogenation Performance of Fe-Based Catalyst via Unique Confinement Effect of the Carbon Shell

Even though Fe-based catalysts have been widely employed for CO2 hydrogenation into hydrocarbons, oxygenates, liquid fuels, etc., the precise regulation of their physicochemical properties is needed to enhance the catalytic performance. Herein, under the guidance of the traditional concept in heterogeneous catalysis-confinement effect, a core-shell structured catalyst Na-Fe3 O4 @C is constructed to boost the CO2 hydrogenation performance. Benefiting from the carbon-chain growth limitation, tailorable H2 /CO2 ratio on the catalytic interface, and unique electronic property that all endowed by the confinement effect, the selectivity and space-time yield of light olefins (C2 = -C4 = ) are as high as 47.4 % and 15.9 g molFe -1  h-1 , respectively, which are all notably higher than that from the shell-less counterpart. The function mechanism of the confinement effect in Fe-based catalysts are clarified in detail by multiple characterization and density functional theory (DFT). This work may offer a new prospect for the rational design of CO2 hydrogenation catalyst.

Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

The use of micro/meso-fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large-scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re-designing long-established batch processes to fit these new conditions. The reliance on experimental trial-and-error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, to provide mechanistic insight, to contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions.