Rathnayake Hemali, Saha Sujoy, Dawood Sheeba, Loeffler Shane, Starobin Joseph
Department of Nanoscience, Joint School of Nanoscience and Nanoengineering, University of North Carolina at Greensboro, Greensboro, North Carolina 27401, United States.
J Phys Chem Lett. 2021 Jan 21;12(2):884-891. doi: 10.1021/acs.jpclett.0c03401. Epub 2021 Jan 12.
A rapid and simple analytical approach is developed to screen the semiconducting properties of metal organic frameworks (MOFs) by modeling the band structure and predicting the density of state of isoreticular MOFs (IRMOFs). One can consider the periodic arrangement of metal nodes linked by organic subunits as a 1D periodic array crystal model, which can be aligned with any unit-cell axis included in the IRMOF's primitive cubic lattice. In such a structure, each valence electron of a metal atom feels the potential field of the entire periodic array. We allocate the 1D periodic array in a crystal unit cell to three IRMOFs- ( = 1, 8, and 10) of the ZnO(L) IRMOF series and apply the model to their crystal lattices with unit-cell constants = 25.66, 30.09, and 34.28 Å, respectively. By solving Schrödinger's equation with a Kronig-Penney periodic potential and fitting the computed energy spectra to IRMOFs' experimental spectroscopic data, we model electronic band structures and obtain densities of state. The band diagram of each IRMOF reveals the nature of its electronic structures and density of state, allowing one to identify its n- or p-type semiconducting behavior. This novel analytical approach serves as a predictive and rapid screening tool to search the MOF database to identify potential semiconducting MOFs.
通过对金属有机框架（MOF）的能带结构进行建模并预测同构MOF（IRMOF）的态密度，开发了一种快速简单的分析方法来筛选MOF的半导体性质。可以将由有机亚基连接的金属节点的周期性排列视为一维周期性阵列晶体模型，该模型可以与IRMOF原始立方晶格中包含的任何晶胞轴对齐。在这样的结构中，金属原子的每个价电子都感受到整个周期性阵列的势场。我们将晶体晶胞中的一维周期性阵列分配给ZnO(L) IRMOF系列的三个IRMOF-（ = 1、8和10），并将该模型应用于它们的晶格，其晶胞常数分别为 = 25.66、30.09和34.28 Å。通过用克龙尼克-彭尼周期性势求解薛定谔方程，并将计算出的能谱与IRMOF的实验光谱数据拟合，我们对电子能带结构进行建模并获得态密度。每个IRMOF的能带图揭示了其电子结构和态密度的性质，从而使人们能够识别其n型或p型半导体行为。这种新颖的分析方法可作为一种预测性的快速筛选工具，用于搜索MOF数据库以识别潜在的半导体MOF。
