Hai Guangtong, Gao Hongyi, Huang Xiubing, Tan Li, Xue Xiangdong, Feng Shihao, Wang Ge
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing 100083 P. R. China
Beijing Key Laboratory of Membrane Materials and Engineering, Department of Chemical Engineering, Tsinghua University Beijing 100084 P. R. China.
Chem Sci. 2022 Mar 9;13(15):4397-4405. doi: 10.1039/d2sc00377e. eCollection 2022 Apr 13.
Two-dimensional (2D) metal-organic frameworks (MOFs) are promising materials for catalyzing the oxygen evolution reaction (OER) due to their abundant exposed active sites and high specific surface area. However, how to rapidly screen out highly-active 2D MOFs from numerous candidates is still a great challenge. Herein, based on the high-throughput density functional theory (DFT) calculations for 20 kinds of different transition metal-based MOFs, we propose a factor for fast screening of 2D MOFs for the OER under alkaline conditions (pH = 14.0), that is, when the Gibbs free energy change of the O-O bond formation (defined as Δ ) is located at ∼1.15 eV, the peak OER performance would be achieved. Based on the high-throughput calculation results, the prediction factor can be further simplified by replacing the Gibbs free energy with the sum of the associated single point energy (SPE) and a binding energy-dependent term. Guided by this factor, we successfully predicted and then obtained the high-performance Ni-based 2D MOFs. This factor would be a practical approach for fast screening of 2D MOF candidates for the OER, and also provide a meaningful reference for the study of other materials.
二维（2D）金属有机框架（MOFs）因其丰富的暴露活性位点和高比表面积而成为催化析氧反应（OER）的有前景的材料。然而，如何从众多候选物中快速筛选出高活性的二维MOFs仍然是一个巨大的挑战。在此，基于对20种不同过渡金属基MOFs的高通量密度泛函理论（DFT）计算，我们提出了一个在碱性条件（pH = 14.0）下快速筛选用于OER的二维MOFs的因子，即当O - O键形成的吉布斯自由能变化（定义为Δ ）位于约1.15 eV时，将实现最佳的OER性能。基于高通量计算结果，通过用相关单点能量（SPE）和一个与结合能相关的项的总和代替吉布斯自由能，可以进一步简化预测因子。在这个因子的指导下，我们成功地预测并获得了高性能的镍基二维MOFs。这个因子将是快速筛选用于OER的二维MOF候选物的实用方法，也为其他材料的研究提供有意义的参考。
