Search

Selective hydrogenation of phenol to cyclohexanone is intriguing in chemical industry. Though a few catalysts with promising performances have been developed in recent years, the basic principle for catalyst design is still missing owing to the unclear catalytic mechanism. This work tries to unravel the mechanism of phenol hydrogenation and the reasons causing the selectivity discrepancy on noble metal catalysts under mild conditions. Results show that different reaction pathways always firstly converge to the formation of cyclohexanone under mild conditions. The selectivity discrepancy mainly depends on the activity for cyclohexanone sequential hydrogenation, in which two factors are found to be responsible, i.e. the hydrogenation energy barrier and the competitive chemisorption between phenol and cyclohexanone, if the specific co-catalyzing effect of H2O on Ru is not considered. Based on the above results, a quantitative descriptor, Eb(one/pl)/Ea, in which Ea can be further correlated to the d band center of the noble metal catalyst, is proposed by the first time to roughly evaluate and predict the selectivity to cyclohexanone for catalyst screening.

Nuclear magnetic resonance (NMR) is a non-destructive and atom-specific specific tool that has become a burgeoning analytic method for understanding the detailed molecular interactions in catalysis and energy storage materials. However, the observation of diverse chemical shifts arising from complex molecular interactions makes the interpretation of NMR spectroscopy increasingly challenging, in particular for a novel system without standards for comparison. Density functional theory-NMR (DFT-NMR) is an indispensable tool to mitigate these challenges and provide detailed 3D molecular structures that relate materials and reaction intermediate structures, and information about chemical interactions, dynamics, and reaction mechanisms. This review provides a fundamental background in DFT-NMR relating to theory development, critical parameters for calculating NMR properties, computational accuracy, and the current capabilities. A variety of practical examples from the fields of catalysis and energy storage, including CO2 capture, are summarized to illustrate the capabilities of DFT-NMR application to date. Last but not least, cautionary notes on the application of these strategies are presented for researchers modeling their own systems.