School of Chemical & Biomolecular Engineering Georgia Institute of Technology 311 Ferst Drive Atlanta, Georgia 30332-0100, United States.
Acc Chem Res. 2014 Nov 18;47(11):3275-83. doi: 10.1021/ar500018b. Epub 2014 Jun 17.
Not only is hydrogen critical for current chemical and refining processes, it is also projected to be an important energy carrier for future green energy systems such as fuel cell vehicles. Scientists have examined light metal hydrides for this purpose, which need to have both good thermodynamic properties and fast charging/discharging kinetics. The properties of hydrogen in metals are also important in the development of membranes for hydrogen purification. In this Account, we highlight our recent work aimed at the large scale screening of metal-based systems with either favorable hydrogen capacities and thermodynamics for hydrogen storage in metal hydrides for use in onboard fuel cell vehicles or promising hydrogen permeabilities relative to pure Pd for hydrogen separation from high temperature mixed gas streams using dense metal membranes. Previously, chemists have found that the metal hydrides need to hit a stability sweet spot: if the compound is too stable, it will not release enough hydrogen under low temperatures; if the compound is too unstable, the reaction may not be reversible under practical conditions. Fortunately, we can use DFT-based methods to assess this stability via prediction of thermodynamic properties, equilibrium reaction pathways, and phase diagrams for candidate metal hydride systems with reasonable accuracy using only proposed crystal structures and compositions as inputs. We have efficiently screened millions of mixtures of pure metals, metal hydrides, and alloys to identify promising reaction schemes via the grand canonical linear programming method. Pure Pd and Pd-based membranes have ideal hydrogen selectivities over other gases but suffer shortcomings such as sensitivity to sulfur poisoning and hydrogen embrittlement. Using a combination of detailed DFT, Monte Carlo techniques, and simplified models, we are able to accurately predict hydrogen permeabilities of metal membranes and screen large libraries of candidate alloys, selections of which are described in this Account. To further increase the number of membrane materials that can be studied with DFT, computational costs need to be reduced either through methods development to break bottlenecks in the performance prediction algorithm, particularly related to transition state identification, or through screening techniques that take advantage of correlations to bypass constraints.
氢气不仅是当前化学和精炼过程的关键，而且预计也将成为未来绿色能源系统（如燃料电池汽车）的重要能源载体。为此，科学家们研究了轻金属氢化物，它们需要同时具有良好的热力学性质和快速的充/放电动力学。金属中氢的性质对于开发用于氢气净化的膜也很重要。在本综述中，我们重点介绍了我们最近的工作，旨在大规模筛选基于金属的系统，这些系统在金属氢化物中具有有利的储氢容量和热力学性质，可用于车载燃料电池汽车，或者相对于纯钯具有有前途的氢气渗透性，可用于从高温混合气流中通过致密金属膜进行氢气分离。以前，化学家们发现金属氢化物需要达到一个稳定的“甜蜜点”：如果化合物在低温下释放的氢气不足，则稳定性过高；如果化合物在实际条件下不稳定，则反应可能不可逆转。幸运的是，我们可以使用基于 DFT 的方法通过预测候选金属氢化物系统的热力学性质、平衡反应途径和相图来评估这种稳定性，只需使用所提出的晶体结构和组成作为输入，即可达到相当的准确度。我们已经有效地筛选了数百万种纯金属、金属氢化物和合金的混合物，通过巨正则线性规划方法确定了有前途的反应方案。纯 Pd 和 Pd 基膜对其他气体具有理想的氢气选择性，但存在易受硫中毒和氢脆的缺点。我们结合使用详细的 DFT、蒙特卡罗技术和简化模型，能够准确预测金属膜的氢气渗透率，并筛选出大量候选合金库，本综述中描述了这些合金的选择。为了进一步增加可以用 DFT 研究的膜材料数量，需要通过开发方法来降低计算成本，这些方法可以打破性能预测算法中的瓶颈，特别是与过渡态识别有关的瓶颈，或者通过利用相关性来避免约束的筛选技术。
