A highly efficient La-modified ZnAl-LDO catalyst and its performance in the synthesis of dimethyl carbonate from methyl carbamate and methanol

In this paper, the highly efficient ZnAlLa layered double oxide (ZnAlLa-LDO) catalyst was evaluated and used in methyl carbamate (MC) alcoholysis synthesis of dimethyl carbonate. Under optimal conditions, the MC conversion was 33.5% and the dimethyl carbonate (DMC) selectivity was up to 92.4% at 443 K and in 9 h. The prepared catalysts were well characterized to investigate the effect on the catalytic performance and reaction catalysis mechanism. The experimental results show that the addition of La adjusted the structure and chemical properties of ZnAl composite oxide and that the synergistic effect among Zn, Al and La play a key role in adjusting the acid-base properties and stability of the catalyst, which definitely improved the DMC selectivity and catalytic stability. Based on the proposed reaction mechanism, two kinetic models of the catalytic reaction were established and modified: Langmuir-Hinshelwood and power-rate law kinetic model. The good agreement between kinetic models and experimental data showed that the power-rate law kinetic model based on the elementary reactions is a suitable model for providing a theoretical basis. The pre-exponential factor and activation energy of the main reaction are 5.77 x 10(7) and 77.60 kJ & BULL;mol(-1), respectively.& COPY; 2023 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co. Ltd. All rights reserved

Automated summary

This paper evaluates the highly efficient ZnAlLa layered double oxide (ZnAlLa-LDO) catalyst and its application in the synthesis of dimethyl carbonate through methyl carbamate alcoholysis. The addition of La adjusts the structure and properties of ZnAl composite oxide, leading to improved DMC selectivity and catalytic stability. Two kinetic models, Langmuir-Hinshelwood and power-rate law, were established and modified based on the proposed reaction mechanism, with good agreement between the models and experimental data.