An Qi, McDonald Molly, Fortunelli Alessandro, Goddard William A
Department of Chemical and Materials Engineering, University of Nevada-Reno, Reno, Nevada 89577, United States.
Materials and Process Simulation Center (MSC), California Institute of Technology, Pasadena, California 91125, United States.
ACS Nano. 2021 Jan 26;15(1):1675-1684. doi: 10.1021/acsnano.0c09346. Epub 2020 Dec 23.
We showed recently that the catalytic efficiency of ammonia synthesis on Fe-based nanoparticles (NP) for Haber-Bosch (HB) reduction of N to ammonia depends very dramatically on the crystal surface exposed and on the doping. In turn, the stability of each surface depends on the stable intermediates present during the catalysis. Thus, under reaction conditions, the shape of the NP is expected to evolve to optimize surface energies. In this paper, we propose to manipulate the shape of the nanoparticles through doping combined with chemisorption and catalysis. To do this, we consider the relationships between the catalyst composition (adding dopant elements) and on how the distribution of the dopant atoms on the bulk and facet sites affects the shape of the particles and therefore the number of active sites on the catalyst surfaces. We use our hierarchical, high-throughput catalyst screening (HHTCS) approach but extend the scope of HHTCS to select dopants that can increase the catalytically active surface orientations, such as Fe-bcc(111), at the expense of catalytically inactive facets, such as Fe-bcc(100). Then, for the most promising dopants, we predict the resulting shape and activity of doped Fe-based nanoparticles under reaction conditions. We examined 34 possible dopants across the periodic table and found 16 dopants that can potentially increase the fraction of active Fe-bcc(111) inactive Fe-bcc(100) facets. Combining this reshaping criterion with our HHTCS estimate of the resulting catalytic performance, we show that Si and Ni are the most promising elements for improving the rates of catalysis by optimizing the shape to decrease reaction barriers. Then, using Si dopant as a working example, we build a steady-state dynamical Wulff construction of Si-doped Fe bcc nanoparticles. We use nanoparticles with a diameter of ∼10 nm, typical of industrial catalysts. We predict that doping Si into such Fe nanoparticles at the optimal atomic content of ∼0.3% leads to rate enhancements by a factor of 56 per nanoparticle under target HB conditions.
我们最近表明,铁基纳米颗粒(NP)上用于哈伯-博施法(HB)将N还原为氨的氨合成催化效率,极大地取决于所暴露的晶体表面以及掺杂情况。反过来,每个表面的稳定性又取决于催化过程中存在的稳定中间体。因此,在反应条件下,预计纳米颗粒的形状会发生演变以优化表面能。在本文中,我们提议通过掺杂结合化学吸附和催化来操控纳米颗粒的形状。为此,我们考虑催化剂组成(添加掺杂元素)之间的关系,以及掺杂原子在体相和晶面位点上的分布如何影响颗粒形状,进而影响催化剂表面活性位点的数量。我们使用分层高通量催化剂筛选(HHTCS)方法,但扩展了HHTCS的范围,以选择能够增加催化活性表面取向(如Fe-bcc(111))的掺杂剂,同时减少催化惰性晶面(如Fe-bcc(100))。然后,对于最有前景的掺杂剂,我们预测在反应条件下掺杂铁基纳米颗粒的最终形状和活性。我们研究了周期表中的34种可能的掺杂剂,发现有16种掺杂剂有可能增加活性Fe-bcc(111)晶面相对于惰性Fe-bcc(100)晶面的比例。将这种重塑标准与我们对最终催化性能的HHTCS估计相结合,我们表明Si和Ni是通过优化形状以降低反应势垒来提高催化速率最有前景的元素。然后,以Si掺杂剂为例,我们构建了Si掺杂的Fe bcc纳米颗粒的稳态动力学伍尔夫结构。我们使用直径约为10 nm的纳米颗粒,这是工业催化剂的典型尺寸。我们预测,在目标HB条件下,以约0.3%的最佳原子含量将Si掺杂到此类Fe纳米颗粒中,每个纳米颗粒的反应速率可提高56倍。