Insights into the Adsorption, Alloy Formation, and Poisoning Effects of Hg on Monometallic and Bimetallic Adsorbents

The removal of elemental mercury (Hg⁰) from coal-derived syngas at high temperatures is desired to improve the thermal efficiency of the coal-to-chemical processes. First-principles density functional theory (DFT) calculations for Hg⁰ adsorption are performed using different exchange correlation functionals (PBE, optPBE-vdW, and optB88-vdW). Gibbs free energy (ΔG) calculations are further performed to evaluate the feasibility of Hg⁰ adsorption on various exposed planes of metal nanoparticles and to obtain bimetallic compositions for Hg⁰ removal at various temperatures. Pd and Pt are shown to be suitable for Hg⁰ adsorption at high temperatures (473 K), whereas Rh and Ru are effective only until 373 K. The bimetallic adsorbents comprising Ag or Au along with Rh, Ru, Pd, or Pt are identified for Hg⁰ removal at high temperatures (473 K). The increase in Hg⁰ adsorption strength on various bimetallic surfaces is correlated to the upward shift in the d-band center. Further, calculations predict the tendency of Hg to segregate toward the surface of amalgams and disturb the perfect planar geometry of the Pd, Pt, Rh, Ru, Ir, Cu, Ag, and Au surfaces to form a noncrystalline Hg-rich amalgam surface. An analysis of the binding of various adsorbates (H, O, N, and S) shows that the adsorption becomes significantly weaker on various sites in close proximity to pre-adsorbed Hg. Moreover, for specific combinations of the adsorbate, surface composition, and the site location, the adsorption does not take place on the proximal sites. These results are complemented by the partial density of states calculations, which show changes in the electronic properties of the amalgam surface, thus explaining the poisoning effect of Hg on metallic catalysts.

Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning

Single atom alloys (SAAs) show great promise as catalysts for a wide variety of reactions due to their tunable properties, which can enhance the catalytic activity and selectivity. To design SAAs, it is imperative for the heterometal dopant to be stable on the surface as an active catalytic site. One main approach to probe SAA stability is to calculate surface segregation energy. Density functional theory (DFT) can be applied to investigate the surface segregation energy in SAAs. However, DFT is computationally expensive and time-consuming; hence, there is a need for accelerated frameworks to screen metal segregation for new SAA catalysts across combinations of metal hosts and dopants. To this end, we developed a model that predicts surface segregation energy using machine learning for a series of SAA periodic slabs. The model leverages elemental descriptors and features inspired by the previously developed bond-centric model. The initial model accurately captures surface segregation energy across a diverse series of FCC-based SAAs with various surface facets and metal-host pairs. Following our machine learning methodology, we expanded our analysis to develop a new model for SAAs formed from FCC hosts with FCC, BCC, and HCP dopants. Our final, five-feature model utilizes second-order polynomial kernel ridge regression. The model is able to predict segregation energies with a high degree of accuracy, which is due to its physically motivated features. We then expanded our data set to test the accuracy of the five features used. We find that the retrained model can accurately capture E _seg trends across different metal hosts and facets, confirming the significance of the features used in our final model. Finally, we apply our pretrained model to a series of Ir- and Pd-based SAA cuboctahedron nanoparticles (NPs), ranging in size and FCC dopants. Remarkably, our model (trained on periodic slabs) accurately predicts the DFT segregation energies of the SAA NPs. The results provide further evidence supporting the use of our model as a general tool for the rapid prediction of SAA segregation energies. By creating a framework to predict the metal segregation from bulk surfaces to NPs, we can accelerate the SAA catalyst design while simultaneously unraveling key physicochemical properties driving thermodynamic stabilization of SAAs.

Theoretical study of surface segregation and ordering in Ni-based bimetallic surface alloys

Ni-based bimetallic materials are promising for a series of important heterogeneous catalytic reactions because of their low cost and potential high activity. In order to understand their catalytic performances in catalytic processes, it is important to know the structural properties of these bimetallic surfaces, including, in particular, how the guest metal is distributed in the nickle host at finite temperature. By using the cluster expansion model built on density-functional theory calculations, combined with Monte Carlo simulation, we study the segregation and ordering behaviors in several frequently studied Ni-based bimetallic catalysts NiX (X = Fe, Co, and Cu). We found that Ni tends to segregate to the top most layer of the surface in NiFe and NiCo, while Cu tends to segregate to the topmost layer of NiCu surfaces. NiCo and NiCu lose short-range order quickly as the temperature increases. Under low temperature, NiFe forms an ordered Ni₃Fe structure, which, however, disappears above 550 K because of the order-disorder transition. These findings can provide important information for the understanding of the stability and activity of Ni-based bimetallic catalysts at high temperatures.

Rapid Prediction of Bimetallic Mixing Behavior at the Nanoscale

The nanoparticle (NP) design space allows for variations in size, shape, composition, and chemical ordering. In the search for low-energy structures, this results in an extremely large search space which cannot be screened by brute force methods. In this work, we develop a genetic algorithm to predict stable bimetallic NPs of any size, shape, and metal composition. Our method predicts nanostructures in agreement with experimental trends and it captures the detailed chemical ordering of an experimental 23,196-atom FePt NP with nearly atom-by-atom accuracy. Our developed screening process is extremely fast, allowing us to generate and analyze a database of 5454 low-energy bimetallic NPs. By identifying thermodynamically stable NPs, we rationalize bimetallic mixing at the nanoscale and reveal metal-, size-, and temperature-dependent mixing behavior. Importantly, our method is applicable to any bimetallic NP size, bridging the materials gap in nanoscale simulations, and guides experimentation in the lab by elucidating stability, mixing, and detailed chemical ordering behavior of bimetallic NPs.

Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces

Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.

N-Doped Graphene Supported on Metal-Iron Carbide as a Catalyst for the Oxygen Reduction Reaction: Density Functional Theory Study

The development of an efficient electrocatalyst for the oxygen reduction reaction (ORR) is essential for the commercialization of fuel-cell technologies. Iron carbide encapsulated in N-doped graphene (NG/Fe₃ C) has been recognized recently as a promising ORR catalyst. In this study, the stability and catalytic activity of N-doped graphene supported on metal-iron carbide (NG/M_Fe₃ C) toward the ORR are investigated by using DFT calculations. The NG/M_Fe₃ C heterostructure is modeled by substituting Fe atoms in the Fe₃ C substrate near the NG/Fe₃ C interface by metal atoms M (M=Cr-Mn, Co-Zn, Nb-Mo, Ta-W). The calculations show that the introduction of the metal atoms M alters the work function of the overlayer N-doped graphene, which is found to correlate with the binding strength of the ORR intermediates. The introduction of Ni or Co atoms at the interface improves the ORR activity of the NG/Fe₃ C and stabilizes the heterostructure. The ORR activity increases as the concentration of Ni or Co atoms near the interface increases, and the stable heterostructure is available in a wide range of substituted concentrations. These results suggest approaches to improve the ORR activity of NG/Fe₃ C catalysts.

First principles analysis of surface dependent segregation in bimetallic alloys

Stability is an important aspect of alloys, and proposed alloys may be unstable due to unfavorable atomic interactions. Segregation of an alloy may occur preferentially at specific exposed surfaces, which could affect the alloy's structure since certain surfaces may become enriched in certain elements. Using density functional theory (DFT), we modeled surface segregation in bimetallic alloys involving all transition metals doped in Pt, Pd, Ir, and Rh. We not only modeled common (111) surfaces of such alloys, but we also modeled (100), (110), and (210) facets of such alloys. Segregation is more preferred for early and late transition metals, with middle transition metals being most stable within the parent metal. We find these general trends in segregation energies for the parent metals: Pt > Rh > Pd > Ir. A comparison of different surfaces suggests no consistent trends across the different parent hosts, but segregation energies can vary up to 2 eV depending on the exposed surface. We also developed a statistical model to predict surface-dependent segregation energies. Our model is able to distinguish segregation at different surfaces (as opposed to generic segregation common in previous models), and agrees well with the DFT data. The present study provides valuable information about surface-dependent segregation and helps explain why certain alloy structures occur (e.g. core-shell).

First-principle atomistic thermodynamic study on the early-stage corrosion of NiCr alloy under fluoride salt environment

The atomic morphology change in the NiCr alloy surface induced by fluorine-chemisorption was investigated by the ab initio atomistic thermodynamic method to elucidate early-stage corrosion processes of nickel-based alloys in strong oxidizing environment. The surface phase diagrams of Cr-doped Ni(111) surface as a function of fluorine chemical potential were obtained to track the surface structures that are most likely to be fostered in various temperature and pressure conditions. The adsorption of fluorine on the top site of Cr in the alloy surface was the most energetically favorable one. With increasing fluorine chemical potential, more fluorine atoms started to agglomerate in the trapping sink of Cr. Fluorine-fluorine repulsion interaction coupled with strong F-Cr bonding could facilitate a decided morphology modification of the metal substrate. Moreover, an insight into the desorption pathways for potential species revealed that in the presence of fluorine, the dissociation of Cr predominantly stems from the relatively easy desorption in the form of CrF2/CrF3 molecules from the non-passivated Ni-based alloy surface.

A revised thermodynamic model for crystal surfaces. I. Theoretical aspects

A revised thermodynamic model to study surface segregation.