Dalian Institute of Chemical Physics achieves low-temperature, efficient, and long-lasting catalytic hydrogenation of carbon dioxide to methanol

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Dalian Institute of Chemical Physics achieves low-temperature, efficient, and long-lasting catalytic hydrogenation of carbon dioxide to produce methanol. According to a report from the Dalian Institute of Chemical Physics in March 2021, recently, the research team led by Researcher Deng Dehui from the Special Research Group on Two-Dimensional Materials and the Conversion of Small Molecules for Energy at the Institute’s Key Laboratory of Catalytic Fundamentals, in collaboration with Professor Wang Ye’s team from Xiamen University, made significant progress in the field of catalytic hydrogenation of carbon dioxide to produce methanol. For the first time, low-temperature, efficient, and long-lasting catalysis of CO2 hydrogenation to methanol was achieved using a few-layer molybdenum disulfide (MoS2) catalyst rich in sulfur vacancies. Its activity and selectivity are significantly superior to those of commercial Cu/ZnO/Al2O3 catalysts, and it exhibits excellent stability. This catalyst opens up new pathways for the low-energy, high-efficiency conversion and utilization of CO2. The efficient conversion and utilization of CO2 hold significant strategic importance for alleviating the energy crisis and achieving the goal of \"carbon neutrality\". The research on the conversion of carbon dioxide into methanol through hydrogenation, carried out by the Catalysis and New Materials Research Center at Dalian Institute of Chemical Physics in collaboration with CNPC, achieved partial results in 2019: the pilot-scale unit was successfully operated for the first time at CNPC’s Daqing Chemical Research Center, with a one-way conversion rate of CO2 of 20% and a methanol selectivity of 70% (with a purity of methanol in the organic liquid product exceeding 99.9%), and stable operation was achieved. The pilot-scale tests also involved verifying the patterns observed in lab-scale tests, examining the scaling effects, optimizing process conditions, and collecting basic data.