Computation-informed optimization of Ni(PyC)<sub>2</sub> functionalization for noble gas separations

Our objective is to tune a “lead” metal-organic framework, Ni(PyC)2(pyridine-4-carboxylate [PyC]), by functionalizing its PyC ligands to maximize its adsorptive selectivity for xenon over krypton at room temperature. To guide experiments, we (1) construct a library of Ni(PyC-X)2(X = functional group) crystal structure models then (2) use molecular simulations to predict their noble gas adsorption and selectivity at room temperature. Motivated by our virtual screening, we synthesize Ni(PyC--NH2)2, determine its crystal structure by X-ray powder diffraction, measure its Xe, Kr, and Ar adsorption isotherms (298 K), and indeed find that its dilute Xe/Kr selectivity at 298 K (20) exceeds that of its parent Ni(PyC)2(17). Corroborated by molecular models, X-ray diffraction shows that Ni(PyC--NH2)2organizes well-defined, Xe-tailored binding pockets along its one-dimensional channels. Our study illustrates the computation-informed optimization of a “lead” metal-organic framework.Gantzler et al. find that virtual screening of functionalized Ni(PyC)2analogs for Xe/Kr separations at room temperature highlights Ni(PyC--NH2)2. They synthesize Ni(PyC--NH2)2, determine its structure, and measure its noble gas adsorption isotherms, confirming that Ni(PyC--NH2)2exhibits a higher Xe/Kr selectivity than Ni(PyC)2.

我们的目标是通过对“先导”金属 - 有机框架Ni(PyC)₂（吡啶 - 4 - 羧酸盐[PyC]）的PyC配体进行官能化来调整它，以使其在室温下对氙相对于氪的吸附选择性最大化。为了指导实验，我们（1）构建了一个Ni(PyC - X)₂（X = 官能团）晶体结构模型库，然后（2）利用分子模拟来预测它们在室温下对惰性气体的吸附和选择性。受虚拟筛选的启发，我们合成了Ni(PyC - -NH₂)₂，通过X射线粉末衍射确定其晶体结构，测量其Xe、Kr和Ar的吸附等温线（298 K），并且确实发现它在298 K时的稀Xe/Kr选择性（20）超过了其母体Ni(PyC)₂（17）。经分子模型证实，X射线衍射表明Ni(PyC - -NH₂)₂沿着其一维通道形成了明确的、适合Xe的结合口袋。我们的研究说明了对“先导”金属 - 有机框架进行基于计算的优化。甘茨勒等人发现，对用于室温下Xe/Kr分离的官能化Ni(PyC)₂类似物进行虚拟筛选凸显了Ni(PyC - -NH₂)₂。他们合成了Ni(PyC - -NH₂)₂，确定了其结构，并测量了其惰性气体吸附等温线，证实Ni(PyC - -NH₂)₂比Ni(PyC)₂表现出更高的Xe/Kr选择性。