Gao Wang, Chen Yun, Li Bo, Liu Shan-Ping, Liu Xin, Jiang Qing
Key Laboratory of Automobile Materials, Ministry of Education, and College of Materials Science and Engineering, Jilin University, Changchun, 130022, China.
Nat Commun. 2020 Mar 5;11(1):1196. doi: 10.1038/s41467-020-14969-8.
Adsorption is essential for many processes on surfaces; therefore, an accurate prediction of adsorption properties is demanded from both fundamental and technological points of view. Particularly, identifying the intrinsic determinants of adsorption energy has been a long-term goal in surface science. Herein, we propose a predictive model for quantitative determination of the adsorption energies of small molecules on metallic materials and oxides, by using a linear combination of the valence and electronegativity of surface atoms and the coordination of active sites, with the corresponding prefactors determined by the valence of adsorbates. This model quantifies the effect of the intrinsic properties of adsorbates and substrates on adsorbate-substrate bonding, derives naturally the well-known adsorption-energy scaling relations, and accounts for the efficiency and limitation of engineering the adsorption energy and reaction energy. All involved parameters are predictable and thus allow the rapid rational design of materials with optimal adsorption properties.
吸附对于表面上的许多过程至关重要;因此,从基础和技术角度都需要准确预测吸附特性。特别是,确定吸附能的内在决定因素一直是表面科学的长期目标。在此,我们提出了一个预测模型,用于定量测定小分子在金属材料和氧化物上的吸附能,该模型使用表面原子的价态和电负性以及活性位点的配位的线性组合,相应的前置因子由吸附质的价态确定。该模型量化了吸附质和底物的内在性质对吸附质 - 底物键合的影响,自然地推导了著名的吸附能标度关系,并解释了设计吸附能和反应能的效率和局限性。所有涉及的参数都是可预测的,因此可以快速合理设计具有最佳吸附特性的材料。