First principles analysis of surface dependent segregation in bimetallic alloys

Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data

Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum-group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 fcc slabs with those compositions including all three low-index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine-tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy.

Vertically Stacked Amorphous Ir/Ru/Ir Oxide Nanosheets for Boosted Acidic Water Splitting

Integrating multiple functional components into vertically stacked heterostructures offers a prospective approach to manipulating the physicochemical properties of materials. The synthesis of vertically stacked heterogeneous noble metal oxides remains a challenge. Herein, we report a surface segregation approach to create vertically stacked amorphous Ir/Ru/Ir oxide nanosheets (NSs). Cross-sectional high-angle annular darkfield scanning transmission electron microscopy images demonstrate a three-layer heterostructure in the amorphous Ir/Ru/Ir oxide NSs, with IrOx layers located on the upper and lower surfaces, and a layer of RuOx sandwiched between the two IrOx layers. The vertically stacked heterostructure is a result of the diffusion of Ir atoms from the amorphous IrRuOx solid solution to the surface. The obtained A-Ir/Ru/Ir oxide NSs display an ultralow overpotential of 191 mV at 10 mA cm-2 toward acid oxygen evolution reaction and demonstrate excellent performance in a proton exchange membrane water electrolyzer, which requires only 1.63 V to achieve 1 A cm-2 at 60 °C, with virtually no activity decay observed after a 1300 h test.

Lattice oxygen activation and local electric field enhancement by co-doping Fe and F in CoO nanoneedle arrays for industrial electrocatalytic water oxidation

Oxygen evolution reaction (OER) is critical to renewable energy conversion technologies, but the structure-activity relationships and underlying catalytic mechanisms in catalysts are not fully understood. We herein demonstrate a strategy to promote OER with simultaneously achieved lattice oxygen activation and enhanced local electric field by dual doping of cations and anions. Rough arrays of Fe and F co-doped CoO nanoneedles are constructed, and a low overpotential of 277 mV at 500 mA cm-2 is achieved. The dually doped Fe and F could cooperatively tailor the electronic properties of CoO, leading to improved metal-oxygen covalency and stimulated lattice oxygen activation. Particularly, Fe doping induces a synergetic effect of tip enhancement and proximity effect, which effectively concentrates OH- ions, optimizes reaction energy barrier and promotes O2 desorption. This work demonstrates a conceptual strategy to couple lattice oxygen and local electric field for effective electrocatalytic water oxidation.

Single Atom Alloys Segregation in the Presence of Ligands

Single atom alloys (SAAs) have gained remarkable attention due to their tunable properties leading to enhanced catalytic performance, such as high activity and selectivity. The stability of SAAs is dictated by surface segregation, which can be affected by the presence of surface adsorbates. Research efforts have primarily focused on the effect of commonly found catalytic reaction intermediates, such as CO and H, on the stability of SAAs. However, there is a knowledge gap in understanding the effect of ligands from colloidal nanoparticle (NP) synthesis on surface segregation. Herein, we combine density functional theory (DFT) and machine learning to investigate the effect of thiol and amine ligands on the stability of colloidal SAAs. DFT calculations revealed rich segregation energy (Eseg) data of SAAs with d8 (Pt, Pd, Ni) and d9 (Ag, Au, Cu) metals exposing (111) and (100) surfaces, in the presence and absence of ligands. Using these data, we developed an accurate four-feature neural network using a multilayer perceptron regression (NN MLP) model. The model captures the underlying physics behind surface segregation in the presence of adsorbed ligands by incorporating features representing the thermodynamic stability of metals through the bulk cohesive energy, structural effects using the coordination number of the dopant and the ligands, the binding strength of the adsorbate to the metals, strain effects using the Wigner-Seitz radius, and electronic effects through electron affinities. We found that the presence of ligands makes the thermodynamics of segregation milder compared to the bare (nonligated) SAA surfaces. Importantly, the adsorption configuration (e.g., top vs bridge) and the binding strength of the ligand to the SAA surface (e.g., amines vs thiols) play an important role in altering the Eseg trends compared to the bare surface. We also developed an accurate NN MLP model that predicts Eseg in the presence of ligands to find thermodynamically stable SAAs, leading to the rapid and efficient screening of colloidal SAAs. Our model captures several experimental observations and elucidates complex physics governing segregation at nanoscale interfaces.

Review on Magnetism in Catalysis: From Theory to PEMFC Applications of 3d Metal Pt-Based Alloys

The relationship between magnetism and catalysis has been an important topic since the mid-20th century. At present time, the scientific community is well aware that a full comprehension of this relationship is required to face modern challenges, such as the need for clean energy technology. The successful use of (para-)magnetic materials has already been corroborated in catalytic processes, such as hydrogenation, Fenton reaction and ammonia synthesis. These catalysts typically contain transition metals from the first to the third row and are affected by the presence of an external magnetic field. Nowadays, it appears that the most promising approach to reach the goal of a more sustainable future is via ferromagnetic conducting catalysts containing open-shell metals (i.e., Fe, Co and Ni) with extra stabilization coming from the presence of an external magnetic field. However, understanding how intrinsic and extrinsic magnetic features are related to catalysis is still a complex task, especially when catalytic performances are improved by these magnetic phenomena. In the present review, we introduce the relationship between magnetism and catalysis and outline its importance in the production of clean energy, by describing the representative case of 3d metal Pt-based alloys, which are extensively investigated and exploited in PEM fuel cells.

Understanding and tackling the activity and selectivity issues for methane to methanol using single atom alloys

The process for the direct oxidation of methane to methanol is investigated on single atom alloys using density functional theory. A catalyst search is performed across FCC metal single atom alloys. 7 single atom alloys are found as candidates and microkinetic modelling is performed. The activity and selectivity are remarkably improved over that of pure palladium metal, yet remain unideal.

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.

A computational study on the adsorption of arsenic pollutants on graphene-based single-atom iron adsorbents

Integrated gasification combined cycle (IGCC) is a promising clean technology for coal power generation; however, the high volatility and toxicity of arsenic pollutants (As₂, As₄, AsO and AsH₃) released from an IGCC coal plant cause serious damage to human health and the ecological environment. Therefore, highly efficient adsorbents for simultaneous treatment of multiple arsenic pollutants are urgently needed. In this work, the adsorption characteristics and competitive adsorption behaviors of As₂, As₄, AsO, and AsH₃ on four kinds of graphene-based single-atom iron adsorbents (Fe/GA) were systematically investigated through density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. The results suggest that single-vacancy Fe/GA doped with three nitrogen atoms has the largest adsorption ability for As₂, As₄, AsO and AsH₃. The adsorption energies of As₂, AsO and As₄ on Fe/GA depend on both charge transfer and orbital hybridization, while the adsorption energy of AsH₃ is mainly decided by electronic transfer. The adsorption differences of As₂, As₄, AsO and AsH₃ on four Fe/GA adsorbents can be explained through the obvious linear relationship between the adsorption energy and Fermi softness. As₂, As₄, AsO and AsH₃ will compete for adsorption sites when they exist on the same adsorbent surface simultaneously, and the adsorption capacities of AsO and As₂ are relatively stronger. After the competitive adsorption between AsO and As₂, AsO occupies the adsorption site at 300-900 K. This theoretical work suggests that Fe/GA is a promising adsorbent for the simultaneous removal of multiple arsenic pollutants with high adsorption capacity and low cost.

A First-Principles Study on the Multilayer Graphene Nanosheets Anode Performance for Boron-Ion Battery

Advanced battery materials are urgently desirable to meet the rapidly growing demand for portable electronics and power. The development of a high-energy-density anode is essential for the practical application of B3+ batteries as an alternative to Li-ion batteries. Herein, we have investigated the performance of B3+ on monolayer (MG), bilayer (BG), trilayer (TG), and tetralayer (TTG) graphene sheets using first-principles calculations. The findings reveal significant stabilization of the HOMO and the LUMO frontier orbitals of the graphene sheets upon adsorption of B3+ by shifting the energies from -5.085 and -2.242 eV in MG to -20.08 and -19.84 eV in 2B3+@TTG. Similarly, increasing the layers to tetralayer graphitic carbon B3+@TTG_asym and B3+@TTG_sym produced the most favorable and deeper van der Waals interactions. The cell voltages obtained were considerably enhanced, and B3+/B@TTG showed the highest cell voltage of 16.5 V. Our results suggest a novel avenue to engineer graphene anode performance by increasing the number of graphene layers.

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.

Controlling the Shapes of Nanoparticles by Dopant-Induced Enhancement of Chemisorption and Catalytic Activity: Application to Fe-Based Ammonia Synthesis

We showed recently that the catalytic efficiency of ammonia synthesis on Fe-based nanoparticles (NP) for Haber-Bosch (HB) reduction of N₂ 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) vs 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.

Effect of an external electric field, aqueous solution and specific adsorption on segregation of PtML/MML/Pt(111) (M = Cu, Pd, Au): a DFT study

The oxygen reduction reaction (ORR) that occurs on the outermost layer of electrocatalysts is significantly affected by the composition and structure of the electrocatalysts. During the preparation of PtM alloy electrocatalysts, high-temperature annealing in an inert or reducing atmosphere could promote the segregation of M toward the core, forming a highly active Pt-skin structure. However, under fuel cell operating conditions, the adsorption of oxygen-containing groups could stimulate the easily dissolved M to segregate to the surface, reducing the activity and stability of the electrocatalysts. In this work, we conducted segregation energy calculation of PtM (M = Cu, Pd, Au) electrocatalysts under specific adsorption (SA), aqueous solution (AS) and an external electric field (EEF) with a density functional theory method. It was found that different factors have different effects on the segregation energy: ΔΔESA ≫ ΔΔEEEF > ΔΔEAS. The coupling effects have also been considered and compared: ΔΔESA+EEF > ΔΔESA+AS > ΔΔEEEF+AS. When including all three factors, the change of segregation energy could reach 1.63 eV. Therefore, operating conditions have a noteworthy influence on the segregation behavior of PtM ORR electrocatalysts, which should be considered in the further design of PtM ORR electrocatalysts.

Magnetism and Heterogeneous Catalysis: In Depth on the Quantum Spin-Exchange Interactions in Pt3M (M = V, Cr, Mn, Fe, Co, Ni, and Y)(111) Alloys

Bimetallic Pt-based alloys have drawn considerable attention in the last decades as catalysts in proton-exchange membrane fuel cells (PEMFCs) because they closely fulfill the two major requirements of high performance and good stability under operating conditions. Pt₃Fe, Pt₃Co, and Pt₃Ni stand out as major candidates, given their good activity toward the challenging oxygen reduction reaction (ORR). The common feature across catalysts based on 3d-transition metals and their alloys is magnetism. Ferromagnetic spin-electron interactions, quantum spin-exchange interactions (QSEIs), are one of the most important energetic contributions in allowing milder chemisorption of reactants onto magnetic catalysts, in addition to spin-selective electron transport. The understanding of the role played by QSEIs in the properties of magnetic 3d-metal-based alloys is important to design and develop novel and effective electrocatalysts based on abundant and cheap metals. We present a detailed theoretical study (via density functional theory) on the most experimentally explored bimetallic alloys Pt₃M (M = V, Cr, Mn, Fe, Co, Ni, and Y)(111). The investigation starts with a thorough structural study on the composition of the layers, followed by a comprehensive physicochemical description of their resistance toward segregation and their chemisorption capabilities toward hydrogen and oxygen atoms. Our study demonstrates that Pt₃Fe(111), Pt₃Co(111), and Pt₃Ni(111) possess the same preferential multilayered structural organization, known for exhibiting specific magnetic properties. The specific role of QSEIs in their catalytic behavior is justified via comparison between spin-polarized and non-spin-polarized calculations.

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.

Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

Improving alkane dehydrogenation activity on γ-Al2O3 through Ga doping

Doping the surface of γ-Al₂O₃ with gallium enhances the alkane dehydrogenation catalytic activity.

X-ray photoelectron and ion scattering spectroscopic surface analyses of amorphous and crystalline calcium phosphate nanoparticles with different chemical histories

The surface of hydroxyapatite nanoparticles is enriched in the topmost atomic layer with calcium and depleted of it elsewhere, alongside being dependent on the history of formation of hydroxyapatite from the amorphous precursor.