Effect of Co doping on mechanism and kinetics of ammonia synthesis on Fe(1 1 1) surface

Understanding Reaction Networks through Controlled Approach to Equilibrium Experiments Using Transient Methods

We report a combined experimental/theoretical approach to studying heterogeneous gas/solid catalytic processes using low-pressure pulse response experiments achieving a controlled approach to equilibrium that combined with quantum mechanics (QM)-based computational analysis provides information needed to reconstruct the role of the different surface reaction steps. We demonstrate this approach using model catalysts for ammonia synthesis/decomposition. Polycrystalline iron and cobalt are studied via low-pressure TAP (temporal analysis of products) pulse response, with the results interpreted through reaction free energies calculated using QM on Fe-BCC(110), Fe-BCC(111), and Co-FCC(111) facets. In TAP experiments, simultaneous pulsing of ammonia and deuterium creates a condition where the participation of reactants and products can be distinguished in both forward and reverse reaction steps. This establishes a balance between competitive reactions for D* surface species that is used to observe the influence of steps leading to nitrogen formation as the nitrogen product remains far from equilibrium. The approach to equilibrium is further controlled by introducing delay timing between NH₃ and D₂ which allows time for surface reactions to evolve before being driven in the reverse direction from the gas phase. The resulting isotopic product distributions for NH₂D, NHD₂, and HD at different temperatures and delay times and NH₃/D₂ pulsing order reveal the role of the N₂ formation barrier in controlling the surface concentration of NH_x* species, as well as providing information on the surface lifetimes of key reaction intermediates. Conclusions derived for monometallic materials are used to interpret experimental results on a more complex and active CoFe bimetallic catalyst.

Si-Doped Fe Catalyst for Ammonia Synthesis at Dramatically Decreased Pressures and Temperatures

The Haber-Bosch (HB) process combining nitrogen (N₂) and hydrogen (H₂) into ammonia (NH₃) gas plays an essential role in the synthesis of fertilizers for food production and many other commodities. However, HB requires enormous energy resources (2% of world energy production), and the high pressures and temperatures make NH₃ production facilities very expensive. Recent advances in improving HB catalysts have been incremental and slow. To accelerate the development of improved HB catalysts, we developed a hierarchical high-throughput catalyst screening (HHTCS) approach based on the recently developed complete reaction mechanism to identify non-transition-metal (NTM) elements from a total set of 18 candidates that can significantly improve the efficiency of the most active Fe surface, Fe-bcc(111), through surface and subsurface doping. Surprisingly, we found a very promising subsurface dopant, Si, that had not been identified or suggested previously, showing the importance of the subsurface Fe atoms in N₂ reduction reactions. Then we derived the full reaction path of the HB process for the Si doped Fe-bcc(111) from QM simulations, which we combined with kinetic Monte Carlo (kMC) simulations to predict a ∼13-fold increase in turnover frequency (TOF) under typical extreme HB conditions (200 atm reactant pressure and 500 °C) and a ∼43-fold increase in TOF under ideal HB conditions (20 atm reactant pressure and 400 °C) for the Si-doped Fe catalyst, in comparison to pure Fe catalyst. Importantly, the Si-doped Fe catalyst can achieve the same TOF of pure Fe at 200 atm/500 °C under much milder conditions, e.g. at a much decreased reactant pressure of 20 atm at 500 °C, or alternatively at temperature and reactant pressure decreased to 400 °C and 60 atm, respectively. Production plants using the new catalysts that operate under such milder conditions could be much less expensive, allowing production at local sites needing fertilizer.

Identifying Active Sites for CO2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations

Gold nanoparticles (AuNPs) and dealloyed Au₃Fe core-shell NP surfaces have been shown to have dramatically improved performance in reducing CO₂ to CO (CO2RR), but the surface features responsible for these improvements are not known. The active sites cannot be identified with surface science experiments, and quantum mechanics (QM) is not practical for the 10 000 surface sites of a 10 nm NP (200 000 bulk atoms). Here, we combine machine learning, multiscale simulations, and QM to predict the performance (a-value) of all 5000-10 000 surface sites on AuNPs and dealloyed Au surfaces. We then identify the optimal active sites for CO2RR on dealloyed gold surfaces with dramatically reduced computational effort. This approach provides a powerful tool to visualize the catalytic activity of the whole surface. Comparing the a-value with descriptors from experiment, computation, or theory should provide new ways to guide the design of high-performance electrocatalysts for applications in clean energy conversion.