Reaction Mechanisms, Kinetics, and Improved Catalysts for Ammonia Synthesis from Hierarchical High Throughput Catalyst Design
Reaction Mechanisms, Kinetics, and Improved Catalysts for Ammonia Synthesis from Hierarchical High Throughput Catalyst Design
复制标题
分级高通量催化剂设计合成氨的反应机理、动力学和改进催化剂
DOI：
10.1021/acs.accounts.1c00789
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发表时间：
2022
影响因子：
18.3
通讯作者：
Goddard, William A.
中科院分区：
文献类型：
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作者：
Fuller, Jon;An, Qi;Fortunelli, Alessandro;Goddard, William A.
Haber-Bosch（HB）工艺是工业生产氨（NH3）的主要化学合成技术，用于制造硝酸盐基肥料和作为潜在的氢载体。仅HB过程就占全球能源使用量的2%以上，每年生产超过1.6亿吨NH3。铁催化剂用于加速反应，但需要高温和高压的大气氮气（N2）和氢气（H2）。在上个世纪，大量的研究旨在提高性能，但进展速度缓慢。本文着重于确定工业上常用的Fe催化剂上HB合成NH3的原子级反应机理，以及如何利用这些知识通过一种新的催化剂合理设计的范例来提出大大改进的催化剂。我们确定了HB过程中两个最活跃的表面Fe（111）和Fe（211）R上的完整反应机理。我们使用密度泛函理论（DFT）来预测所有12个重要反应和34个最重要的2 × 2表面构型的自由能垒。然后，我们将该机制纳入动力学蒙特卡罗（kMC）模拟运行几个小时的真实的时间来预测周转频率（TOF）。预测的TOF在实验误差范围内，表明预测的势垒在实验的0.04 eV范围内。有了这个准确度，我们准备使用DFT来改进催化剂。我们的目标不是形成具有均匀浓度的块体合金，而是寻找强烈偏好近表面位置的添加剂，使得少量的添加剂可能导致显著的改善。然而，即使是单一的添加剂，表面物种和反应的组合显着倍增，与1048个反应步骤检查和近100个表面配置每2 × 2网站。为了使其能够实际检查数十个掺杂剂候选者，我们开发了分层高通量催化筛选（HHTCS）方法，我们将其应用于Fe（111）和Fe（211）表面。对于HHTCS，我们确定了两个表面的12个反应步骤中最重要的4个，以检查>50种掺杂剂情况，其中我们要求每个步骤的性能不劣于纯Fe。使用HHTCS，计算成本约为完成完整反应机理的1%，使我们能够在大约1/2的时间内完成1050个案例，这是纯Fe（111）的时间。对于Fe（111），在400 °C和20 atm下，我们预测了三种高性能掺杂剂强烈倾向于第二层：Co的速率高8倍，Ni的速率高16倍，Si的速率高43倍。我们还发现了四种掺杂剂强烈偏好顶层并提高性能：Pt或Rh快3倍，Pd或Cu快2倍。对于Fe（211），第二层Co的掺杂效果最好，其掺杂速率比未掺杂表面快3倍. DFT/kMC数据用于预测反应条件下催化剂颗粒的重塑以及如何调整掺杂剂含量以最大化催化面积和活性.最后，我们将展示如何通过理论和实验操作光谱特征之间的比较来验证我们的机制模型。
ConspectusThe Haber–Bosch (HB) process is the primary chemical synthesis technique for industrial production of ammonia (NH3) for manufacturing nitrate-based fertilizer and as a potential hydrogen carrier. The HB process alone is responsible for over 2% of all global energy usage to produce more than 160 million tons of NH3annually. Iron catalysts are utilized to accelerate the reaction, but high temperatures and pressures of atmospheric nitrogen gas (N2) and hydrogen gas (H2) are required. A great deal of research has aimed at increased performance over the last century, but the rate of progress has been slow. This Account focuses on determining the atomic-level reaction mechanism for HB synthesis of NH3on the Fe catalysts used in industry and how to use this knowledge to suggest greatly improved catalysts via a novel paradigm of catalyst rational design.We determined the full reaction mechanism on the two most active surfaces for the HB process, Fe(111) and Fe(211)R. We used density functional theory (DFT) to predict the free-energy barriers for all 12 important reactions and the 34 most important 2 × 2 surface configurations. Then we incorporated the mechanism into kinetic Monte Carlo (kMC) simulations run for several hours of real time to predict turnover frequencies (TOFs). The predicted TOFs are within experimental error, indicating that the predicted barriers are within 0.04 eV of experiment.With this level of accuracy, we are poised to use DFT to improve the catalyst. Rather than forming bulk alloys with uniform concentration, we aimed at finding additives that strongly prefer near-surface sites so that minor amounts of the additive might lead to dramatic improvements. However, even for a single additive, the combinations of surface species and reactions multiplies significantly, with ∼48 reaction steps to examine and nearly 100 surface configurations per 2 × 2 site. To make it practical to examine tens of dopant candidates, we developed thehierarchical high-throughput catalysis screening(HHTCS) approach, which we applied to both the Fe(111) and Fe(211) surfaces. For HHTCS, we identified the most important 4 reaction steps out of 12 for the two surfaces to examine >50 dopant cases, where we required performance at each step no worse than for pure Fe. With HHTCS, the computational cost is about 1% of that for doing the full reaction mechanism, allowing us to do ≈50 cases in about 1/2 the time it took to do pure Fe(111). The new leads identified with HHTCS are then validated with full mechanistic studies.For Fe(111), we predict three high-performance dopants that strongly prefer the second layer: Co with a rate 8 times higher, Ni with a rate 16 times higher, and Si with a rate 43 times higher, at 400 °C and 20 atm. We also found four dopants that strongly prefer the top layer and improve performance: Pt or Rh 3 times faster and Pd or Cu 2 times faster. For Fe(211), the best dopant was found to be second-layer Co with a rate 3 times faster than that for the undoped surface.The DFT/kMC data were used to predict reshaping of the catalyst particles under reaction conditions and how to tune dopant content so as to maximize catalytic area and thus activity. Finally, we show how to validate our mechanistic modeling via a comparison between theoretical and experimental operando spectroscopic signatures.