2026-10-14T07:21:10Z https://www.scidb.cn/oai

10.57760/sciencedb.j00124.00478 2026-05-14T10:00:31Z

2026-05-14 Hydrophobically modified UiO-66 supported CuZn catalysts for CO2 hydrogenation to methanol-data doi:10.57760/sciencedb.j00124.00478 en Hydrophobically modified UiO-66-NH-C18 and hydrophilic UiO-66-NH2 supports were designed and employed for the co-impregnation of CuZn active components, aiming to systematically investigated the influence mechanism of catalyst surface wettability on the catalytic performance of CO2 hydrogenation to methanol. Water contact angle measurements and infrared spectroscopy (IR) results indicated that the catalysts prepared using UiO-66-NH2 and UiO-66-NH-C18 supports exhibited excellent hydrophilic and hydrophobic properties, respectively. After optimizing the Cu/Zn molar ratio, the hydrophobic Cu3Zn1/C18 catalyst showed the optimal catalytic performance under conditions of 240 °C, 3 MPa, and a space velocity (GHSV) of 3600 mL/(g·h), with a CO2 conversion of 1.5 %, methanol selectivity of 70.6%, CO selectivity of 29.4%, and a methanol space-time yield (STY) of 40.7 mgMeOH·gCu-1·h-1. In comparison, the hydrophilic Cu3Zn1/NH2 catalyst showed a CO2 conversion of 1.8%, methanol and CO selectivities of 46.8% and 53.2%, and a methanol STY of 17.2 mgMeOH·gCu-1·h-1. X-ray Photoelectron Spectroscopy (XPS) and Temperature-programmed desorption (TPD) experiments further revealed the surface hydrophobicity repeled the H2O generated during the reaction, suppressing the oxidation of Cu0 active sites by H2O, while promoting the CH3OH desorption, thereby significantly improving CH3OH selectivity, reducing CO selectivity and obtaining 2.4 times higher STY based on Cu loading. In addition, the presence of an appropriate amount of H2O on the catalyst surface can accelerate the hydrogenation consumption of the key intermediate CH3O* in the CO pathway, thereby enhancing CO2 conversion. CO2 hydrogenation; methanol synthesis; UiO-66-NH2; hydrophobic modification; CuZn catalyst xu pan pan He Huanhuan Li Yufeng Fan Zhaobo Liu Bing Liu Xiaohao RESTRICTED dataset Science Data Bank