Chen Chen, Li Zhihui, Yang Jia, Wang Haifeng, Chen De
Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, Shanghai 200237, China.
Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway.
J Phys Chem C Nanomater Interfaces. 2025 Mar 25;129(13):6245-6253. doi: 10.1021/acs.jpcc.5c00005. eCollection 2025 Apr 3.
Adsorption is a critical factor in heterogeneous catalysis, as the interaction between adsorbate and adsorbent significantly impacts catalytic efficiency and selectivity. In this study, we utilized density functional theory (DFT) to comprehensively analyze the adsorption behavior of various oxygenates on the surfaces of metal dioxide (MO) catalysts. Our findings reveal a strong dependence of adsorption energy ( ) on two primary descriptors: the effective charge ( ) of oxygen atoms in oxygenates and the electron affinity (EA) of the surface metal atoms in MO. We observed that oxygenates with more negative exhibit stronger adsorption, while MO with lower EA offer greater adsorption stability. Using these two descriptors, a predictive scaling relationship was developed and validated across different MO surfaces. This descriptor-based model establishes an efficient framework for accurately predicting adsorption strength and offers valuable theoretical insights for designing and screening MO catalysts with optimized adsorption properties.
吸附是多相催化中的一个关键因素，因为吸附质与吸附剂之间的相互作用会显著影响催化效率和选择性。在本研究中，我们利用密度泛函理论（DFT）全面分析了各种含氧化合物在金属氧化物（MO）催化剂表面的吸附行为。我们的研究结果表明，吸附能（ ）强烈依赖于两个主要描述符：含氧化合物中氧原子的有效电荷（ ）和MO中表面金属原子的电子亲和能（EA）。我们观察到， 更负的含氧化合物表现出更强的吸附，而EA较低的MO提供更高的吸附稳定性。利用这两个描述符，建立了一个预测性的 标度关系，并在不同的MO表面进行了验证。这个基于描述符的模型建立了一个准确预测吸附强度的有效框架，并为设计和筛选具有优化吸附性能的MO催化剂提供了有价值的理论见解。
