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C: Physical Properties of Materials and InterfacesNovember 13, 2023

Single Atom Alloys Segregation in the Presence of Ligands
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Maya Salem
Maya Salem
Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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Dennis J. Loevlie
Dennis J. Loevlie
Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
More by Dennis J. Loevlie
https://orcid.org/0000-0002-2822-7763
Giannis Mpourmpakis*
Giannis Mpourmpakis
Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
*Email: [email protected]
More by Giannis Mpourmpakis
https://orcid.org/0000-0002-3063-0607

Open PDF Supporting Information (2)

The Journal of Physical Chemistry C

Cite this: J. Phys. Chem. C 2023, 127, 46, 22790–22798

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https://doi.org/10.1021/acs.jpcc.3c05827

Published November 13, 2023

CC-BY 4.0 .

Abstract

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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 (E_seg) 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 E_seg trends compared to the bare surface. We also developed an accurate NN MLP model that predicts E_seg 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.

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Subjects

Introduction

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The design of single atom active sites is desired for many catalytic reactions due to their unique physicochemical properties and potential to decrease catalyst cost. (1) Single atom alloys (SAAs), a class of single site catalysts, consist of highly active (Pd, Pt, and Ni) dopants incorporated on the surface of less active but more selective metal hosts, typically made of d⁹ metals (Ag, Cu, and Au). (2,3) These distinct and unique active sites have shown remarkable and improved catalytic activity against their monometallic counterparts. For example, Pei et al. demonstrated that for the semi-hydrogenation reaction, a single Pd on a Cu host produced optimal ethylene selectivity of ∼85% and 100% acetylene conversion compared to pure Cu and pure Pd. (4)

The presence of well-defined active sites results in a more rational and controlled SAA design. (2,5) Moreover, this leads to more efficient catalysts due to their ability to alter adsorption and catalytic intermediate properties. (6) For instance, it was found that Co_dopantRu_host and Pt_dopantRu_host exhibit enhanced catalytic activity in the production of methanol from CO₂ due to the binding strength and charge distribution on the surface. (7) The binding strength between the adsorbate and the surface of SAA is key to improved catalytic stability and activity. Liu et al. tested the activity of Pt_dopantCu_host compared to pure Pt for acetylene hydrogenation in the presence of CO. (8) 50% of the reaction rate (i.e., activity) was recovered in the SAA case, while 10% was retained for the Pt nanoparticle (NP), when CO was introduced. The ∼40% difference is attributed to the binding strength of the CO to the Pt in the SAA and Pt NP, indicating that the SAA has a high tolerance to CO poisoning. (8) Similarly, Xing et al. found that when a Pd dopant was isolated on a Cu host, a high N₂ catalytic activity, selectivity, and stability (for ∼30 h) were attained for NO reduction in the presence of CO. (9) These outstanding catalytic performances are due to the interaction of the adsorbate with the SAA surface under the reaction conditions. On the other hand, strong binding can lead to undesirable outcomes such as the formation of aggregates and poisoning of the catalyst. Yang indicated that the acetylene adsorption leads to surface restructuring arising from the strong binding of acetylene to the Ni_dopantCu_host and Rh_dopantCu_host SAAs, forming aggregates. (10) Alternatively, when Pd_dopantCu_host and Pt_dopantCu_host SAAs were utilized for the same reaction, they found that the dopant remained isolated and acetylene hydrogenation to ethylene was favored.

The presence of adsorbates has a great effect on surface segregation, which is one of the key factors (a secondary factor is aggregation) dictating stability of the SAA. (2,11) Surface segregation is defined as the thermodynamic stability of the dopant to segregate to the surface. The binding strength of the adsorbate may induce segregation of the dopant to the surface. (12) Papanikolaou et al. have found that when platinum-group metals were doped in d⁹ metals, segregation was not likely to occur (except for PdCu). However, in the presence of CO, a reverse E_seg trend was observed due to the strong affinity of CO to platinum-group metals. (13) It is important to note that the presence of an adsorbate may not always lead to segregation of the dopant. Wang et al. found Pt doped in Au and Ag, (111) and (100) facets, does not result in the segregation of Pt, even in the presence of H. (14) Hence, how the presence of the adsorbate alters the segregation tendencies (i.e., thermodynamic stability of SAAs) is not straightforward to assess and requires a deep fundamental understanding of bonding interactions.

The segregation energy (E_seg) of nonligated, bare surfaces (absence of any adsorbates) has been widely studied using density functional theory (DFT) (11,13,15,16) and tight-binding theory. (17,18) Furthermore, to accelerate the process of predicting E_seg, statistical and machine learning techniques have been implemented. In our recent work, we proposed a five-feature second-order polynomial kernel ridge regression model to predict E_seg of the bare (111), (100), (110), and (210) facets on platinum group metal-based SAAs. (19) The model incorporated as features the difference in the bulk cohesive energy divided by the coordination number of the dopant (inspired by the bond-centric model on bimetallic NPs (20)), the atomic radius of the dopant, the electronegativity of the host, the difference in the electron affinity, and the first ionization potential of the dopant. (19) The model, which was trained on DFT results on periodic surfaces, was able to capture E_seg trends in NPs, generalizing well across different materials scales. Through this study, factors controlling host–dopant interactions that can either thermodynamically promote or hinder the segregation of the dopant were discovered. To further examine how an adsorbate affects surface segregation, there have been many studies that focused on how CO, H₂, NO, and O₂ alter the stability (i.e., segregation and aggregation energies) of SAAs through experimental and computational work. (8,11−14,21−23) These adsorbates are studied either as probe molecules (13) or as part of elementary steps for many catalytic reactions, such as hydrogenations. (16) DFT is time-consuming (i.e., computationally expensive); hence, there is a need for a quick and accurate alternative to screen different SAA catalysts in the presence of an adsorbate. Han et al. developed a model that predicted E_seg in the presence of H through compressed-sensing data-analytics approach (SISSO), utilizing multiple DFT inputs. (16) More recently, Sulley et al. applied machine learning techniques to determine the stability of single atom alloys in the absence and presence of CO. (24) Although these models were able to screen through different SAAs in the presence of H and CO, an understanding of how different adsorbates, specifically ligands, affect E_seg has yet to be unraveled.

In this study, our first aim is to understand how the nature of widely used ligands, such as methylamine (H₃C–NH₂) and methylthiolate (H₃C–S), affect E_seg on SAAs. −NH₂ and −S are commonly used as ligands in noble-metal NP synthesis. (25,26) For instance, it has been shown that the −NH₂ and −S anchoring groups restrict growth and prevent aggregation in Ag and Au NP synthesis, respectively. (27,28) Additionally, because the H₃C–NH₂ can lose H forming H₃C–NH, (29) we also investigate the effect of amine saturation on the adsorption configuration and E_seg. We consider different SAA combinations of d⁸ (Ni, Pd, and Pt) and d⁹ (Ag, Au, and Cu) metals on low-index surfaces such as (111) and (100). Finally, with the generated DFT data, we develop a regression model using tabulated features that are able to accurately describe the surface segregation of SAAs in the presence of ligands.

Methodology

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Density Functional Theory

The E_seg of nonligated (bare) and ligated slabs were calculated using CP2K. (30) Exchange correlation was accounted for using the PBE functional (31) in conjunction with Grimme’s D3 dispersion correction method. (31) The DZVP (double-ζ valence polarized) basis set was used with the Goedecker, Teter, and Hutter (GTH) pseudopotentials at a 600 Ry cutoff. (32) All of the calculations were spin-polarized. Self-consistent field cycles were performed with a convergence criterion of 10^–7 hartree. Geometry relaxations were performed using the Broyden–Fletcher–Goldfarb–Shanno minimization algorithm until the forces converged to 4.0 × 10^–4 hartree bohr^–1.

The bulk structure of the metals contains 108 atoms, as demonstrated in Figure 1a. The (111) and (100) slabs were modeled using a 6 × 6 × 6 cell, where the bottom three layers were fixed and the top three layers were allowed to relax, as shown in Figure 1b. The (111) surface consists of atoms with a coordination number of 9, meaning that each surface atom is coordinated with six other surface atoms and 3 additional atoms in the layer below. On the other hand, the (100) surface has a coordination number of 8, where each surface atom is bonded to 4 other surface atoms and 4 additional atoms in the layer below. Metal combinations of d⁸ (Ni, Pd, Pt) and d⁹ (Ag, Au, Cu) are considered in this study. Additionally, the adsorbates H₃C–NH₂, H₃C–NH, and H₃C–S are used. The four different cases (nonligated and 3 ligated systems) resulted in a total of 240 different systems studied in this work. It should be noted that the ligated systems (180 data points) are considered in the model development. To compute the E_seg of the bare surface, the following equation was used: (15)

E_{seg} = E_{pure bulk} + E_{dopant, 1 st layer} - E_{dopant, bulk} - E_{pure surface}

(1)

E_seg is the segregation energy of the dopant from the bulk to the surface, and E_pure bulk and E_{pure surface} are the total energies of pure (monometallic) bulk and surface, respectively. The E_{dopant,1st layer} is the total energy of the dopant present in the first layer of the surface, and E_dopant,bulk is the total energy of the dopant present in the bulk. To account for adsorbate effects, eq 2 was used to compute E_seg in the presence of the amine and thiol ligands (E_seg/X). The most stable configuration was considered in this study, i.e., hollow site for the thiolate ligand, with an exception of Au(100), where H₃C–S prefers to form a bridge site, top site for the H₃C–NH_2, and bridge site for the H₃C–NH, as illustrated in Figure 1c–e. Thus, our data have diverse adsorption configurations due to the selection of the specific ligands.

E_{seg / X} = E_{pure bulk} + E_{dopant, 1 st layer, X} - E_{dopant, bulk} - E_{pure surface, X}

(2)

In eq 2, E_seg/X is the segregation energy of the dopant from the bulk to the surface in the presence of an adsorbate (X). E_{pure surface,X} is the total energy of the monometallic surface in the presence of a ligand, as illustrated in Figures 1c and 1d. E_{dopant,first layer,X} is the total energy of the dopant present in the first layer in the presence of a ligand, as shown in Figure 1e. A negative E_seg value indicates that the dopant has the thermodynamic tendency to segregate to the surface, while a positive E_seg value denotes that the dopant prefers to stay in bulk.

Figure 1. Different structures involved in E_seg calculations: (a) side view of the bulk structure, (b) top view of the dopant (Au) on the (111) host metal surface, (c) H₃C–S hollow adsorption on a (111) surface, (d) H₃C–NH₂ top adsorption on a (111) surface, and (e) dopant (Cu) on a (111) metal host surface with H₃C–NH adsorbed on the bridge position.

Machine Learning Implementation

We applied a supervised machine learning approach to develop an accurate E_seg regression model. Along with the binding energy of the adsorbate on a single atom (displayed in Figure 2; refer to Sections 1 and 3 of the Supporting Information for calculation details), tabulated elemental properties of the host and dopant such as the covalent radius, electronegativity, electron affinity, and first ionization potential (obtained from the Mendeleev Python package (33)) were considered as inputs. In addition to these features, the atomic radius, (34) Wigner–Seitz radius, (35) and bulk cohesive energy (Table S1) were also considered. The features were standardized by transforming the inputs in a manner that the distribution has a mean of 0 and a standard deviation of 1, ensuring an equal contribution of the different features. A full list of the elemental properties used in this analysis can be found in Table S2.

Figure 2. Optimized structures of a single metal atom bonded with (a) H₃C–NH₂ and (b) H₃C–NH, and (c) H₃C–S ligands. The colors represent different atoms: green is the metal atom of interest, blue is nitrogen, yellow is sulfur, black is carbon, and white is hydrogen.

A 85/15% train/test split was chosen, and a 5-fold cross-validation was implemented using the training data to obtain the train and validation errors. The 15% test data was used in the final step to evaluate the accuracy of the model in predicting E_seg on unseen data using MAE and RMSE (eqs 3 and 4, respectively). For feature selection, a variable importance plot based on the random forest regression was employed to determine which features contribute more to predicting E_seg in the presence of a ligand. (36) In our study, we use random forest regression to account for any complex (nonlinear) interactions between the features and the output. (37)

MAE = \frac{1}{x} \sum_{i = 1}^{x} | y - \hat{y} |

(3)

RMSE = \sqrt{\frac{1}{x} \sum_{i = 1}^{x} {(\hat{y} - y)}^{2}}

(4)

In eqs 3 and 4, y is the actual output value, ŷ is the predicted output value, and x is the total number of data points. After the features were selected, the hyperparameters present in the neural network multilayer perceptron (NN MLP), (38) kernel ridge regression (KRR), (39) support vector regressor (SVR), (40) random forest regressor, (41) and extreme gradient boosting regressor (XGB) (42) were optimized using GridSearchCV (43) by minimizing the MAE of the validation data set. To gain a better understanding of the model’s overall performance on the data set, we evaluate the model after it is generated by using 100 different random train test splits (with different random seeds) and obtain the mean and standard deviation of the train, validation, and test set MAE. The implementation and evaluation of the models was performed using the Scikit-Learn Python package. (44)

Results and Discussion

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DFT Calculated Segregation Trends

First, we compare how the DFT calculated E_seg values of bare surfaces are influenced by the different coordination environments present on the (100) and (111) surfaces (coordination number 8 on the (100) facet vs 9 on the (111) facet). In Figure 3a, we present the SAA E_seg on (111) vs the (100) facets. It can be observed that there is a linear trend, however, with a slightly changed slope from the parity (blue line vs black parity line). In cases where surface segregation was preferred (i.e., E_seg was negative), the (100) yielded more negative values than the (111) facet, indicating that the dopant had a greater thermodynamic tendency to segregate. This is because (100) has more dangling bonds than the (111) surface, causing the dopant to segregate from the bulk (high coordinated environment) to the surface (lower coordinated environment) to stabilize the system. Ag and Au metal hosts do not promote segregation of the dopant, regardless of the facet. This is because the radii of Ag and Au are larger than the radii of the d⁸ metals (shown in Figure 3b), with the atomic radius being one of the driving forces in segregation. (17) Conversely, Ag and Au dopants are more stable on the surface of the host, regardless of the host metal. It was also found that there is a greater tendency of the dopant to segregate in the Ni-based (host) SAAs. This is because the radius of the Ni host is significantly smaller than the metal dopants considered in this study, as demonstrated in the lighter colored points in Figure 3b. Additionally, this trend is experimentally observed on AuNi systems, where Au prefers to segregate to the surface to lower the lattice strain energy arising from the change in the radius. (45)

Figure 3. (a) Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the absence of an adsorbate. Color indicates the different metal hosts, and the marker type indicates the different dopants. The inset figure demonstrates the SAA in the absence of an adsorbate. (b) Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the absence of an adsorbate, with data points being colored based on the change in the radius between the metal host and dopant. A darker shade indicates that the radius of the metal host is larger than that of the dopant, while lighter shade indicates that the radius of the host is smaller than that of the dopant.

To understand the effect of adsorbates on the E_seg trends, we first investigate the effect of the adsorption site (i.e., H₃C–NH₂ (top) vs H₃C–NH (bridge) adsorption). The addition of the adsorbate has produced similar E_seg trends as the bare surface in terms of the exposed facet, meaning that the presence of the dopant on the surface is more thermodynamically preferred on the (100) than the (111) surface (shown in Figure 4). There is a wider E_seg value distribution in the bare surface compared to those in the presence of H₃C–NH₂ and H₃C–NH, indicating that the presence of a ligand makes the thermodynamics of segregation milder. Interestingly, H₃C–NH₂ affects the slope of the E_seg data more than H₃C–NH (compare blue lines in Figures 4a and 4b). H₃C–NH alters the adsorption trend compared to H₃C–NH₂ bringing the trend back to parity, similar to the bare SAA systems, but leading to a narrower E_seg range, similar to the H₃C–NH₂ case. This is due to the adsorption configuration change of H₃C–NH, which prefers to bind on a bridge site, involving two metal atoms (the dopant atom and one metal host atom), compared to the top site adsorption of H₃C–NH₂, which entirely involves the dopant. Thus, our results demonstrate that a top adsorption of the ligand will have a stronger effect on E_seg of a single atom compared to a bridge adsorption. It should be noted that although H₃C–NH is a less saturated amine than H₃C–NH₂ and one would expect to have a stronger effect on the E_seg due to the stronger adsorption on the surface, the adsorption configuration (bridge in H₃C–NH vs top in H₃C–NH₂) plays a more important role to change the slopes in Figure 4. d⁸ metals doped in d⁹ metals in the presence of H₃C–NH₂ and H₃C–NH led to reverse E_seg trends. A similar effect has been reported for the same SAA combinations, when CO was introduced. (11,13) For instance, Ni doped in Au (111) in the absence of adsorbates results in a positive E_seg, meaning that the dopant prefers to reside in the bulk. In the presence of H₃C–NH₂ and H₃C–NH, the E_seg behavior of Ni doped in Au (111) produced opposite (i.e., reverse) E_seg trends, promoting the dopant to the surface. On the other hand, when d⁸ metals are doped with d⁹ metals in the presence of H₃C–NH₂ and H₃C–NH, an increase in the E_seg is observed. For example, in the presence of the adsorbates, Au is less likely to segregate to the Pd surface, regardless of the facet type.

Figure 4. Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the presence of adsorbed (a) H₃C–NH₂ and (b) H₃C–NH. Colors indicate the different metal hosts, and symbols indicate the different metal dopants. The inset image demonstrates SAA in the presence of ligand.

With regard to the H₃C–S adsorption, the fcc-hollow adsorption is preferred for all of the metal hosts on (111) and (100) surfaces. Although the (100) facet maintained the same adsorption configuration of thiolate after the addition of the dopant, the adsorption on (111) varied. In the cases highlighted in blue in Figure 5a, the binding strength between the thiolate and host is stronger than that between the thiolate and dopant, leading to a new configuration. As a result, the thiolate–dopant bond was broken, and the thiolate formed a bond with the host metal instead during geometry optimization (shown in Figures 5b and 5c). Such a change only occurs in the (111) case due to the weaker binding of H₃C–S on (111) compared to the (100) facet from the different surface coordination. Because of the new geometric configurations, the E_seg of H₃C–S on the (111) surface trends changed significantly compared to the (100) surface and the other adsorbates investigated in this study (H₃C–NH₂ and H₃C–NH). In the case of Cu(111)Au, the H₃C–S adsorption changes from a hollow-site to a bridge site (still binds with the dopant and the metal host). Additionally, as a result of this adsorption deviation, a wider E_seg value distribution was found in the presence of H₃C–S, compared to H₃C–NH₂ and H₃C–NH, as illustrated in Figures 4 and 5. In the specific cases where H₃C–S moved away from the dopant, the E_seg values are relatively similar to the bare surfaces (within 0.1 eV) due to the weak adsorbate effect on the metal dopant. This indicates how the direct coordination of the ligand to the metal dopant and the coordination environment dictate the E_seg behavior, demonstrating the complexity involved in surface segregation.

Figure 5. (a) Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the presence of an adsorbed H₃C–S ligand. Colors indicate the different metal hosts, and symbols indicate the different metal dopants (as in Figure 4). Top view of H₃C–S on (b) pure Ni (111) and (c) Ag dopant on a Ni (111) surface, where the adsorbate preferentially interacts with the metal host moving away from the dopant. Silver color represents the Ag, yellow the S, black the C, and white the H atom. The blue transparent circles in (a) refer to the cases where the thiolate does not bind with the dopant but preferentially interacts with the metal host.

The wide range of E_seg is also attributed to the higher strength of the metal–adsorbate bond. Specifically, thiolate ligands exhibit a stronger affinity to the metals we investigated in this study (−2.29 to −4.49 eV), compared to the amine ligands, leading to noticeable deviations in the cases of thiolate–M(111). It is important to note that the binding energy of CH₃NH₂ to the SAA surfaces ranges from −0.57 to −1.79 eV (shown in Figure S1), leading to a shift in the segregation behavior of SAAs compared to the nonligated systems. However, the presence of an adsorbate may not always promote dopant segregation in SAAs. Wang et al. revealed that H does not always induce dopant segregation, emphasizing the significance of the metal–adsorbate bond. (14)

Model Development

After gaining deep insight into how the different ligands and adsorption configurations can affect the E_seg, we seek for accelerated way to screen through the different SAAs in the presence of ligands. It is infeasible to use computationally expensive DFT (i.e., time-consuming) or trial and error in experiments to screen the vast amount of possible SAA and adsorbate configurations. Supervised machine learning approaches allow us to locate optimal (i.e., with high thermodynamic stability) SAA catalysts by efficiently and accurately predicting E_seg. Variable importance was employed (shown in Figure 6) to determine which variables contribute the most to predicting E_seg. We also incorporated CN and CN_ads as separate terms (illustrated in Figure S2), and we consistently observed the retention of the same four features, indicating their importance in capturing the segregation energy in SAAs. We then implemented variance inflation factor to check for multicollinearity, which occurs when features convey redundant information. Our analysis, as depicted in Table S3, suggests that Δvdw is strongly correlated to another feature (ΔWS), evident from the significant coefficient of 15.28. Hence, we selected the top four features. Furthermore, we performed a comparative analysis between the best performing model using four features and the model’s performance when restricted to three features, with the latter showing a poor performance. These four features are the following: the difference in the bulk cohesive energy of the host and dopant divided by the coordination number of the dopant (ΔCE/CN), the difference in the binding energy of the adsorbate on a single atom of the host and dopant divided by the coordination number of the adsorbate on the surface (ΔBE/CNads; see Section 1 of the Supporting Information for details), the difference in the Wigner–Seitz radius of the host and dopant (ΔWS), and the difference in the electron affinity of the host and dopant (ΔEA). There is an overlap in the features between the previously developed second-order KRR model (19) on SAA segregation on bare surfaces and the features from this analysis: ΔCE/CN, ΔEA, and a strain term such as the ΔWS, showing the transferability of these descriptors in determining the segregation behavior in the presence and absence of adsorbates. The DFT E_seg data, features, and the Python code utilized to develop the model are available free of charge on our GitHub repository (https://github.com/mpourmpakis/EsegAdsModel).

Figure 6. Variable importance based on random forest regression.

After the features were obtained, the hyperparameters were optimized (Table S4) and the performance of the different regression models in predicting the E_seg was compared, as demonstrated in Figure S3. Moreover, NN MLP resulted in the lowest validation MAE. We also found that the second-order KRR had comparable performance despite having fewer hyperparameters to tune; however, the KRR model misses a few cases of the E_seg behavior, specifically the antisegregation behavior, which is why NN MLP was selected. The NN MLP train, validation, and test MAEs and RMSEs were relatively similar, which denotes the model was not overfitting to the training data set, as shown in Tables S5 and S6. To better understand the model’s performance when trained on different subsets of the data, we ran 100 different train/test splits (using different random seeds) and found similar trends, as illustrated in Figure S4 and Table S7, and similar errors, further confirming that the model is not overfitting. To take a closer look at the model results on the test data set, we plot the model’s predictions against DFT E_seg (Figure 7). The model captures the E_seg trends across the different ligands, producing an MAE of 0.107 eV and an RMSE of 0.137 eV. Compared to the other adsorbates, R-NH led to the highest deviation from the parity line. Despite this deviation, our model still captures their overall segregation behavior (segregation vs thermoneutrality vs antisegregation). We also conducted a comparison with the same model, utilizing only three features. We observed a significant drop in model accuracy with a test MAE increasing to ∼0.20 eV, signifying the critical role of the top four features in capturing the E_seg trends.

Figure 7. Parity plot between the NN MLP model predictions and DFT E_seg of the test set (27 data points are the test set, and 153 points are the training set). Colors indicate the different metal hosts, symbols indicate the different metal dopants, and edge color represents the adsorbate.

The utilized features play a crucial role in the model’s performance. The four features used capture the underlying physics behind segregation in the presence of an adsorbate. The first term, ΔCE/CN, represents the thermodynamic stability of the system, while also accounting for the coordination environment of the dopant. The term is derived from the bond-centric model, used to capture the stability of metal NPs, where CE_bulk and CN contribute to computing the bond energies. (20,46) The second term, ΔBE/CN_ads, accounts for the type of ligand used and its adsorption configuration (i.e., top: CN_ads = 1; bridge: CN_ads = 2; hollow: CN_ads = 3). The addition of CN_ads is critical in capturing the different adsorption configurations that may arise, allowing for the model to distinguish between the different ligands that are considered in this study. The ΔBE is important in capturing the binding strength between the ligand and metal atom and can also capture any ligand adsorption changes that may occur on the surface of the pure host compared to the SAA (e.g., thiolate case). The third term, ΔWS, captures strain effects that are important for segregation (Figure 3b). Lastly, ΔEA qualitatively describes the electron transfer occurring between the host and dopant metals. Therefore, the four features used capture the underlying physics behind segregation in the presence of an adsorbate. This is a prime example of how critical physically relevant features are in predicting E_seg in the presence of the adsorbate.

To further validate our model’s predictions, we compare it against experimental observations (10 different experimental systems reported in Table S8). We find that the model captures the experimental observations accurately (8 out of 10 experimental observations). We do acknowledge that the model predicts thermoneutral segregation for two cases; however, our model does not consider any entropic effects, which can drive the segregation of the dopants at elevated temperatures. (2) We note that the experimental observations are based on how dopants behave in an alloy, meaning not all the experimental results are based on highly dilute alloys but on cases where the host composition dominates the dopant concentration. This experimental validation indicates that our model shows great promise, allowing for rapid screening across SAAs in the presence of ligands. Future studies could further incorporate surface coverage and ligand size effects. Although our studies focused on one ligand adsorption on SAAs and the size of the ligand was restricted to a methyl group, the resulting segregation energy model was able to capture very complex behavior emanating from different ligands (e.g., thiols and amines), metal combinations, surface facets, and ligand adsorption configurations. We anticipate that the physical descriptors revealed in this study will play a key role in the development of more complex segregation models in the future. Our model’s applicability also extends to other adsorbates, such as CO and H, by simply calculating the ΔBE/CN_ads term using DFT. It is important to emphasize that this calculation is efficiently and rapidly performed, given that the systems (one metal atom bonded to one ligand) involve very few atoms.

Conclusions

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In this work, we investigated the effect of three different ligands (H₃C–NH₂, H₃C–NH, and H₃C–S) and two surface facets on the E_seg behavior of d⁸- and d⁹-based SAAs. Regardless of the adsorbate (absence or presence), in the SAA cases where segregation is favored in both facets, the (100) led to a more negative E_seg trend compared to the (111) surface. It was also found that the presence of ligands makes the thermodynamics of segregation milder compared to the trends on bare surfaces. The binding strength between the ligand and metals and the binding configuration of the ligand can lead to significant changes in the E_seg trends. These findings are critical in understanding the behavior of different adsorbates on the stability of SAAs, leading to a more efficient and informed screening of different SAA catalyst. To this goal, we leveraged machine learning techniques to predict E_seg in the presence of the three different ligands studied in this work. Based on the variable importance plot, it was determined that ΔCE/CN, ΔBE/CN_ads, ΔEA, and ΔWS contributed the most in predicting E_seg in the presence of adsorbates. These descriptors capture the thermodynamic stability of the SAA, ligand adsorption effects, electronic modification effects, and strain effects. Multiple studies, including our own, have concluded that dopant segregation is favored when the cohesive energy of the host is larger than the cohesive energy of the dopant. Additionally, our analysis indicates that dopant segregation also depends on the coordination environment, highlighting the importance of ΔCE/CN. The second term, ΔWS, signifies that when the radius of the dopant is larger than the radius of the host, the dopant tends to be more stable on the surface. The ΔBE/CN_ads reveals that when the binding energy of the adsorbate to the dopant is stronger than the binding energy of the adsorbate to the host, there is a greater tendency for the dopant to segregate. Lastly, the ΔEA, describes the tendency for charge transfer between the metal and the host. These features have been previously individually shown to affect the segregation behavior, capturing the underlying physics occurring in SAA in the presence of ligands. Finally, we employed these features in different regression models and found that the NN MLP produced holistically optimal model performance compared to those of the other regression models. Our model predictions verified a series of experimental observations and elucidated important properties that can drive segregation, accelerating the controlled synthesis of SAAs.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcc.3c05827.

Calculation details of ΔBE/CN_ads term; DFT calculated bulk cohesive energy (CE_bulk); binding energy of the ligands on SAA surfaces; assessing multicollinearity using variance inflation factor; descriptors used in the feature importance analysis; extended variable importance plot; tuned hyperparameters used in regression models; parity plots of the predicted vs calculated E_seg using different regression models; MAE and RMSE scores of train, validation, and test sets of different regression models; bootstrapping analysis and related MAE scores; experimental observations from the literature against NN MLP E_seg predictions; architecture of the NN MLP; DFT electronic energy of single metal atoms, ligands, a single atom bonded to ligands, and the binding energy of the latter (PDF)
Optimized surfaces from DFT (ZIP)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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Corresponding Author
- Giannis Mpourmpakis - Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States; https://orcid.org/0000-0002-3063-0607; Email: [email protected]
Authors
- Maya Salem - Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- Dennis J. Loevlie - Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States; https://orcid.org/0000-0002-2822-7763
Notes
The authors declare no competing financial interest.

Acknowledgments

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This work has been supported by the National Science Foundation (NSF, CBET-CAREER program) under Grant 1652694. The authors acknowledge computational support from the Center for Research Computing at the University of Pittsburgh, RRID:SCR_022735, through the resources provided. Specifically, this work used the H2P cluster, which is supported by NSF Award OAC-2117681. In addition, the Extreme Science and Engineering Discovery Environment (XSEDE) is acknowledged using Expanse at SDSC Dell Cluster with AMD Rome HDR IB through Allocation ENG150034, which is supported by the NSF (ACI-1053575).

References

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This article references 46 other publications.

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A review. Monometallic catalysts, in particular those contg. noble metals, are frequently used in heterogeneous catalysis, but they are expensive, rare and the ability to tailor their structures and properties remains limited. Traditionally, alloy catalysts have been used instead that feature enhanced electronic and chem. properties at a reduced cost. Furthermore, the introduction of single metal atoms anchored onto supports provided another effective strategy to increase both the at. efficiency and the chance of tailoring the properties. Most recently, single-atom alloy catalysts have been developed in which one metal is atomically dispersed throughout the catalyst via alloy bonding; such catalysts combine the traditional advantages of alloy catalysts with the new feature of tailoring properties achievable with single atom catalysts. This review will first outline the at. scale structural anal. on single-atom alloys using microscopy and spectroscopy tools, such as high-angle annular dark field imaging-scanning transmission electron microscopy and extended X-ray absorption fine structure spectroscopy. Next, progress in research to understand the electronic properties of single-atom alloys using X-ray spectroscopy techniques and quantum calcns. will be presented. The catalytic activities of single-atom alloys in a few representative reactions will be further discussed to demonstrate their structure-property relationships. Finally, future perspectives for single-atom alloy catalysts from the structural, electronic and reactivity aspects will be proposed.
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A review. The development of low-cost, high-performance catalysts at the at. level has become a challenging issue for large-scale applications of renewable clean energy technologies. Atomic sites catalysts, such as single atoms catalysts (SAC), single clusters catalysts (SCC), single-atom alloys (SAA), have proved their performance in various catalytic reactions due to their extremely high atom utilization efficiency, unique structure, and exceptional catalytic selectivity. A deep understanding and design of the active center of the catalyst at the at. level has become a top priority for current research. Compared with SAC and SCC, SAA has its own uniqueness. In this review, we focused on the recent progress on the prepn. methods of SAA and discussed the key factors controlling the structure of SAA. In addn., several important catalytic reactions performed over well-defined SAA are analyzed. Finally, the challenges and the perspectives of this cutting-edge field are suggested. We believe that this crit. review provides a guidance for the rational design of SAA for catalytic applications.
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Nano Today (2020), 34 (), 100917CODEN: NTAOCG; ISSN:1748-0132. (Elsevier Ltd.)
A review. The development of low-cost, high-performance catalysts at the at. level has become a challenging issue for large-scale applications of renewable clean energy technologies. Atomic sites catalysts, such as single atoms catalysts (SAC), single clusters catalysts (SCC), single-atom alloys (SAA), have proved their performance in various catalytic reactions due to their extremely high atom utilization efficiency, unique structure, and exceptional catalytic selectivity. A deep understanding and design of the active center of the catalyst at the at. level has become a top priority for current research. Compared with SAC and SCC, SAA has its own uniqueness. In this review, we focused on the recent progress on the prepn. methods of SAA and discussed the key factors controlling the structure of SAA. In addn., several important catalytic reactions performed over well-defined SAA are analyzed. Finally, the challenges and the perspectives of this cutting-edge field are suggested. We believe that this crit. review provides a guidance for the rational design of SAA for catalytic applications.
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The transformations of CO2 mols. into valuable products are of increasing interest due to the neg. impact of anthropogenic CO2 emissions on global warming. The CO2-to-methanol hydrogenation is an economically profitable reaction of carbon fixation, but it still steps away from widespread industrialization because of the lack of efficient and selective catalysts. Recently, single-atom alloy (SAA) catalysts have been developed to work remarkably in CO2 hydrogenation reactions. Doping isolated single atoms into metallic catalyst can dramatically alter the catalytic performance of the host. A screening discovery is performed on Ru and 6 RuX (X = Fe, Co, Ni, Cu, Ir and Pt) SAAs using d. functional theory (DFT) computations. A 13 possible elementary reactions are considered in 4 possible reaction pathways on Ru and all RuX surfaces. In the computed mechanisms, it was found that the formation of *H2COOH and *HCOO intermediates plays a crit. role in detg. catalysts' activities. Doping Co and Pt isolated single atoms into Ru surface can thermodynamically and kinetically facilitate these intermediates formation processes, eventually promoting the prodn. of methanol. The combination of weak binding and enhanced charge redistribution on RuCo and RuPt surfaces give them improved catalytic activities over pure Ru. This work will ultimately facilitate the discovery and development of SAAs for CO2 to methanol, serving as guidance to expts. and theoreticians alike.
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Platinum catalysts are extensively used in the chem. industry and as electrocatalysts in fuel cells. Pt is notorious for its sensitivity to poisoning by strong CO adsorption. Here we demonstrate that the single-atom alloy (SAA) strategy applied to Pt reduces the binding strength of CO while maintaining catalytic performance. By using surface sensitive studies, we detd. the binding strength of CO to different Pt ensembles, and this in turn guided the prepn. of PtCu alloy nanoparticles (NPs). The at. ratio Pt:Cu = 1:125 yielded a SAA which exhibited excellent CO tolerance in H2 activation, the key elementary step for hydrogenation and hydrogen electro-oxidn. As a probe reaction, the selective hydrogenation of acetylene to ethene was performed under flow conditions on the SAA NPs supported on alumina without activity loss in the presence of CO. The ability to maintain reactivity in the presence of CO is vital to other industrial reaction systems, such as hydrocarbon oxidn., electrochem. methanol oxidn., and hydrogen fuel cells.
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A series of Cu-Pd alloy nanoparticles supported on Al2O3 were prepd. and tested as catalysts for deNOx reactions. XRD, HAADF-STEM, XAFS, and FT-IR analyses revealed that a single-atom alloy structure was formed when the Cu/Pd ratio was 5, where Pd atoms were well isolated by Cu atoms. Compared with Pd/Al2O3, Cu5Pd/Al2O3 exhibited outstanding catalytic activity and N2 selectivity in the redn. of NO by CO: for the first time, the complete conversion of NO to N2 was achieved even at 175 °C, with long-term stability for at least 30 h. High catalytic performance was also obtained in the presence of O2 and C3H6 (model exhaust gas), where a 90% decrease in Pd use was achieved with min. evolution of N2O. Kinetic and DFT studies demonstrated that N-O bond breaking of the (NO)2 dimer was the rate-detg. step and was kinetically promoted by the isolated Pd.
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The stabilities and catalytic performances of single-atom alloy (SAA) structures under the reaction conditions of acetylene hydrogenation are thoroughly examd. utilizing d. functional theory (DFT) calcns. Four Cu-based alloys with stable SAA structures reported before, namely PdCu, PtCu, RhCu and NiCu alloys, are investigated here. We find that the SAA structures of PdCu and PtCu are stable during the reaction, while the RhCu-SAA and NiCu-SAA structures are thermodynamically unstable upon acetylene adsorption and surface restructuring through the aggregation of the Rh and Ni atoms on the surfaces may also happen. It is also found that all the investigated structures of RhCu and NiCu alloys may give rise to the further hydrogenation of ethylene. However, desorption of ethylene is favored over the PdCu-SAA and PtCu-SAA surfaces, indicating that acetylene could be selectively hydrogenated to ethylene over these two surfaces, which is consistent with the exptl. observations reported in the literature. Our work provides new understandings regarding SAA surface structures under reaction conditions and their catalytic reaction performances upon aggregation of the doped metal atoms.
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Kim, H. Y.; Henkelman, G. CO Adsorption-Driven Surface Segregation of Pd on Au/Pd Bimetallic Surfaces: Role of Defects and Effect on CO Oxidation. ACS Catal. 2013, 3 (11), 2541– 2546, DOI: 10.1021/cs4006259
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Kim, Hyun You; Henkelman, Graeme
ACS Catalysis (2013), 3 (11), 2541-2546CODEN: ACCACS; ISSN:2155-5435. (American Chemical Society)
We use d. functional theory (DFT) to study CO-adsorption-induced Pd surface segregation in Au/Pd bimetallic surfaces, dynamics of Pd-Au swapping, effect of defects on the swapping rate, CO-induced Pd clustering, and the reaction mechanism of CO oxidn. The strong CO-philic nature of Pd atoms supplies a driving force for the preferential surface segregation of Pd atoms and Pd cluster formation. Surface vacancies are found to dramatically accelerate the rate of Pd-Au swapping. We find that Pd clusters consisting of at least four Pd atoms prefer to bind O2 rather than CO. These clusters facilitate the rapid dissocn. of O2 and supply reactive oxygen species for CO oxidn. Our findings suggest that geometric, electronic, and dynamic effects should be considered in the function of bimetallic alloys or nanoparticles whose components asym. interact with reacting mols.
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Papanikolaou, K. G.; Darby, M. T.; Stamatakis, M. CO-Induced Aggregation and Segregation of Highly Dilute Alloys: A Density Functional Theory Study. J. Phys. Chem. C 2019, 123 (14), 9128– 9138, DOI: 10.1021/acs.jpcc.9b00649
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CO-induced aggregation and segregation of highly dilute alloys: Density functional theory study
Papanikolaou, Konstantinos G.; Darby, Matthew T.; Stamatakis, Michail
Journal of Physical Chemistry C (2019), 123 (14), 9128-9138CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)
Highly dil. binary alloys composed of an active platinum group metal (PGM) and a more inert coinage metal are important in the field of catalysis, as they function as active and selective catalysts. Their catalytic properties depend on the surface "ensemble" of PGM atoms, whose size may be altered under reactive conditions. We use d. functional theory and investigate the interaction of CO, a mol. common in numerous industrially important chemistries, with alloys that are composed of a PGM (Pt, Pd, Rh, Ir, and Ni) doped in coinage metal hosts (Cu, Au, and Ag). We study the adsorption of CO on the (211) step and (100) facet and compare our results to those previously obtained on the (111) facet. We det. strong correlations between the adsorption energies of CO across the facets and highlight the corresponding thermochem. scaling relations. Finally, we study the stability of isolated surface dopant atoms with respect to aggregation into clusters and segregation into the bulk, both in the presence and absence of CO. We find that strong CO-dopant interactions significantly influence the morphol. of the catalyst surface, suggesting that it may be possible to establish control over the ensemble size of the dopant by tuning Pco.
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Wang, Q.; Zhu, B.; Tielens, F.; Tichit, D.; Guesmi, H. Mapping Surface Segregation of Single-Atom Pt Dispersed in M Surfaces (M = Cu, Ag, Au, Ni, Pd, Co, Rh and Ir) under Hydrogen Pressure at Various Temperatures. Appl. Surf. Sci. 2021, 548, 149217, DOI: 10.1016/j.apsusc.2021.149217
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Mapping surface segregation of single-atom Pt dispersed in M surfaces (M = Cu, Ag, Au, Ni, Pd, Co, Rh and Ir) under hydrogen pressure at various temperatures
Wang, Qing; Zhu, Beien; Tielens, Frederik; Tichit, Didier; Guesmi, Hazar
Applied Surface Science (2021), 548 (), 149217CODEN: ASUSEE; ISSN:0169-4332. (Elsevier B.V.)
Single-atom alloys (SAAs) are emerging materials contg. isolated metal atoms dispersed on host metal surfaces, exhibiting unique reactivity compared with the corresponding monometallic counterparts. However, the stability of the isolated atoms in the host metal has hardly been studied, although, metal segregation has been commonly obsd. in bimetallic nanoparticles under reaction conditions. In this work we focus on single-atom Pt anchored on various metallic support surfaces. D. Functional Theory (DFT) calcns. coupled with environmental segregation energy anal. are performed to map the segregation trends of 22 different Pt-SAA surfaces under various hydrogen conditions. The results show the high stability of single-atom Pt in Ni, Co, Rh and Ir host metallic surfaces while no stability is predicted on Au and Ag surfaces. For Pd and Cu host supports, the single-atom Pt is found to be stable on sp. surface facets and within definite temp. and pressure conditions. This work brings an important understanding of SAA systems through the prediction of surface at. ordering changes under operating conditions which related to the reactivity will ultimately allow the design of more efficient catalysts.
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Farsi, L.; Deskins, N. A. First Principles Analysis of Surface Dependent Segregation in Bimetallic Alloys. Phys. Chem. Chem. Phys. 2019, 21 (42), 23626– 23637, DOI: 10.1039/C9CP03984H
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First principles analysis of surface dependent segregation in bimetallic alloys
Farsi, Lida; Deskins, N. Aaron
Physical Chemistry Chemical Physics (2019), 21 (42), 23626-23637CODEN: PPCPFQ; ISSN:1463-9076. (Royal Society of Chemistry)
Stability is an important aspect of alloys, and proposed alloys may be unstable due to unfavorable at. 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 d. 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).
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Han, Z.-K.; Sarker, D.; Ouyang, R.; Mazheika, A.; Gao, Y.; Levchenko, S. V. Single-Atom Alloy Catalysts Designed by First-Principles Calculations and Artificial Intelligence. Nat. Commun. 2021, 12 (1), 1833, DOI: 10.1038/s41467-021-22048-9
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Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence
Han, Zhong-Kang; Sarker, Debalaya; Ouyang, Runhai; Mazheika, Aliaksei; Gao, Yi; Levchenko, Sergey V.
Nature Communications (2021), 12 (1), 1833CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissocn. and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large no. of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with d.-functional inputs. Besides consistently predicting efficiency of the exptl. studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qual. anal. of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.
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Wang, L.-L.; Johnson, D. D. Predicted Trends of Core-Shell Preferences for 132 Late Transition-Metal Binary-Alloy Nanoparticles. J. Am. Chem. Soc. 2009, 131 (39), 14023– 14029, DOI: 10.1021/ja903247x
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Predicted trends of core-shell preferences for 132 late transition-metal binary-alloy nanoparticles
Wang, Lin-Lin; Johnson, Duane D.
Journal of the American Chemical Society (2009), 131 (39), 14023-14029CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
Transition metal-alloyed nanoparticles with core-shell features (shell enrichment by one of the metals) are becoming ubiquitous, from (electro-)catalysis to biomedical applications, due to their size control, performance, biocompatibility, and cost. Many binary-alloyed nanoparticle systems from groups 8-11 in the Periodic Table were investigated by using d. functional theory, and their segregation energies were systematically explored to det. their core-shell preferences. The core-shell preferences are generally described by two independent factors (1) cohesive energy related to vapor pressure (2) at. size quantified by the Wigner-Seitz radius, and the interplay between them. These independent factors provide general trends for the surface segregation preference for atoms in nanoparticles as well as semi-infinite surfaces, and give a simple correlation or design map for the alloying and catalytic behavior. A universal description of the core-shell preference is provided by the tight-binding theory involving band-energy differences that (1) quant. reproduces the d. functional theory segregation energies (2) confirms the electronic origins and correlations for core-shell behavior.
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Tréglia, G.; Legrand, B.; Ducastelle, F. Segregation and Ordering at Surfaces of Transition Metal Alloys: The Tight-Binding Ising Model. Europhysics Letters (EPL) 1988, 7 (7), 575– 580, DOI: 10.1209/0295-5075/7/7/001
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Salem, M.; Cowan, M. J.; Mpourmpakis, G. Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning. ACS Omega 2022, 7 (5), 4471– 4481, DOI: 10.1021/acsomega.1c06337
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Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning
Salem, Maya; Cowan, Michael J.; Mpourmpakis, Giannis
ACS Omega (2022), 7 (5), 4471-4481CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)
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 calc. surface segregation energy. D. 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 methodol., we expanded our anal. 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 phys. 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 Eseg 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 physicochem. properties driving thermodn. stabilization of SAAs.
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Yan, Z.; Taylor, M. G.; Mascareno, A.; Mpourmpakis, G. Size-, Shape-, and Composition-Dependent Model for Metal Nanoparticle Stability Prediction. Nano Lett. 2018, 18 (4), 2696– 2704, DOI: 10.1021/acs.nanolett.8b00670
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Size-, shape-, and composition-dependent model for metal nanoparticle stability prediction
Yan, Zihao; Taylor, Michael G.; Mascareno, Ashley; Mpourmpakis, Giannis
Nano Letters (2018), 18 (4), 2696-2704CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)
Although tremendous applications for metal nanoparticles have been found in modern technologies, the understanding of their stability as related to morphol. (size and shape) and chem. ordering (e.g., in bimetallics) remains limited. First-principles methods such as d. functional theory (DFT) are capable of capturing accurate nanoalloy energetics; however, they are limited to very small nanoparticle sizes (<2 nm in diam.) due to their computational cost. Herein, we propose a bond-centric (BC) model able to capture cohesive energy trends over a range of monometallic and bimetallic nanoparticles and mixing behavior (excess energy) of nanoalloys, in great agreement with DFT calcns. We apply the BC model to screen the energetics of a recently reported 23 196-atom FePt nanoalloys (Yang et al. Nature 2017, 542, 75-79), offering insights into both segregation and bulk-chem. ordering behavior. Because the BC model utilizes tabulated data (diat. bond energies and bulk cohesive energies) and structural information on nanoparticles (coordination nos.), it can be applied to calc. the energetics of any nanoparticle morphol. and chem. compn., thus significantly accelerating nanoalloy design.
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Dhouib, A.; Guesmi, H. DFT Study of the M Segregation on MAu Alloys (M = Ni, Pd, Pt) in Presence of Adsorbed Oxygen O and O2. Chem. Phys. Lett. 2012, 521, 98– 103, DOI: 10.1016/j.cplett.2011.11.050
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DFT study of the M segregation on MAu alloys (M = Ni, Pd, Pt) in presence of adsorbed oxygen O and O2
Dhouib, Adnene; Guesmi, Hazar
Chemical Physics Letters (2012), 521 (), 98-103CODEN: CHPLBC; ISSN:0009-2614. (Elsevier B.V.)
Segregation phenomena of Group 10 (M = Ni, Pd, Pt) transition-metals substituted in Au(111) surface and sub-surface layers are studied by DFT periodic calcns. in presence of adsorbed at. and mol. oxygen. In contrast with vacuum conditions, where the metal impurities M prefer to be in the bulk of gold, in the presence of adsorbed O or O2, the impurities mainly segregate to the surface. The anal. of oxygen adsorption trends and electronic surface structures explain the change in the local at. arrangement which is expected to occur on the surface of alloys during reaction conditions.
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Løvvik, O. M.; Opalka, S. M. Reversed Surface Segregation in Palladium-Silver Alloys Due to Hydrogen Adsorption. Surf. Sci. 2008, 602 (17), 2840– 2844, DOI: 10.1016/j.susc.2008.07.016
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Reversed surface segregation in palladium-silver alloys due to hydrogen adsorption
Lovvik, O. M.; Opalka, Susanne M.
Surface Science (2008), 602 (17), 2840-2844CODEN: SUSCAS; ISSN:0039-6028. (Elsevier B.V.)
Ag segregates to the surface of pure and ideal Pd-Ag alloy surfaces. By first-principles band-structure calcns. it is shown how this may be changed when H is adsorbed on a Pd-Ag(1 1 1) surface. Due to H binding more strongly to Pd than to Ag, there is a clear energy gain from a reversal of the surface segregation. H-induced segregation may provide a fundamental explanation for the H or reducing treatments that are required to activate H-selective membrane or catalyst performance.
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Dhifallah, M.; Dhouib, A.; Aldulaijan, S.; Renzo, F. D. I.; Guesmi, H. First-Principles Study of Au-Cu Alloy Surface Changes Induced by Gas Adsorption of CO, NO, or O2. J. Chem. Phys. 2016, 145 (2), 024701, DOI: 10.1063/1.4955104
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First-principles study of Au-Cu alloy surface changes induced by gas adsorption of CO, NO, or O2
Dhifallah, Marwa; Dhouib, Adnene; Aldulaijan, Sarah; Renzo, Francesco D. I.; Guesmi, Hazar
Journal of Chemical Physics (2016), 145 (2), 024701/1-024701/8CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
The surface compn. of bimetallics can be strongly altered by adsorbing mols. where the metal with the strongest interaction with the adsorbate segregates into the surface. To study the effect of reactive gas on the surface compn. of Au-Cu alloy, the authors examd. by d. functional theory to study the segregation behavior of Cu in Au matrixes. The adsorption mechanisms of CO, NO, and O2 gas mols. on Au, Cu, and Au-Cu low index (111), (100), and (110) surfaces were analyzed from energetic and electronic points of view. The results show a strong segregation of Cu toward the (110) surface in the presence of all adsorbed mols. The Cu segregation toward the (111) and (100) surface could occur only in the presence of CO and at a lower extent in the presence of NO. The anal. of the electronic structure highlights the different binding characters of adsorbates inducing the Cu segregation. (c) 2016 American Institute of Physics.
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Sulley, G. A.; Hamm, J.; Montemore, M. M. Machine Learning Approach for Screening Alloy Surfaces for Stability in Catalytic Reaction Conditions. Journal of Physics: Energy 2023, 5 (1), 015002, DOI: 10.1088/2515-7655/aca122
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Heuer-Jungemann, A.; Feliu, N.; Bakaimi, I.; Hamaly, M.; Alkilany, A.; Chakraborty, I.; Masood, A.; Casula, M. F.; Kostopoulou, A.; Oh, E. The Role of Ligands in the Chemical Synthesis and Applications of Inorganic Nanoparticles. Chem. Rev. 2019, 119 (8), 4819– 4880, DOI: 10.1021/acs.chemrev.8b00733
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The Role of Ligands in the Chemical Synthesis and Applications of Inorganic Nanoparticles
Heuer-Jungemann, Amelie; Feliu, Neus; Bakaimi, Ioanna; Hamaly, Majd; Alkilany, Alaaldin; Chakraborty, Indranath; Masood, Atif; Casula, Maria F.; Kostopoulou, Athanasia; Oh, Eunkeu; Susumu, Kimihiro; Stewart, Michael H.; Medintz, Igor L.; Stratakis, Emmanuel; Parak, Wolfgang J.; Kanaras, Antonios G.
Chemical Reviews (Washington, DC, United States) (2019), 119 (8), 4819-4880CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
A review. The authors provide a comprehensive review on the role of the ligands with respect to the nanoparticle morphol., stability, and function. The design of nanoparticles is crit. for their efficient use in many applications ranging from biomedicine to sensing and energy. While shape and size are responsible for the properties of the inorg. nanoparticle core, the choice of ligands is of utmost importance for the colloidal stability and function of the nanoparticles. Moreover, the selection of ligands employed in nanoparticle synthesis can det. their final size and shape. Ligands added after nanoparticle synthesis infer both new properties as well as provide enhanced colloidal stability. The authors analyze the interaction of nanoparticle surface and ligands with different chem. groups, the types of bonding, the final dispersibility of ligand-coated nanoparticles in complex media, their reactivity, and their performance in biomedicine, photodetectors, photovoltaic devices, light-emitting devices, sensors, memory devices, thermoelec. applications, and catalysis.
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Chen, T.; Luo, Z.; Yao, Q.; Yeo, A. X. H.; Xie, J. Synthesis of Thiolate-Protected Au Nanoparticles Revisited: U-Shape Trend between the Size of Nanoparticles and Thiol-to-Au Ratio. Chem. Commun. 2016, 52 (61), 9522– 9525, DOI: 10.1039/C6CC04433F
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synthesis of thiolate-protected Au nanoparticles revisited: U-shape trend between size of nanoparticles and thiol-to-Au ratio
Chen, Tiankai; Luo, Zhentao; Yao, Qiaofeng; Yeo, Andrea Xin Hui; Xie, Jianping
Chemical Communications (Cambridge, United Kingdom) (2016), 52 (61), 9522-9525CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)
We report a new understanding of the factors controlling the size of thiolate-protected gold nanoparticles (Au NPs): the formation and the state of a protecting layer (Au(I)-thiolate motifs) outside the gold core det. the size of Au NPs, depending on the feeding thiol-to-Au ratio. As a result, a U-shape trend is identified between the size of Au NPs and the thiol-to-Au ratio as opposed to the commonly expected decreasing trend.
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Frattini, A.; Pellegri, N.; Nicastro, D.; Sanctis, O. de. Effect of Amine Groups in the Synthesis of Ag Nanoparticles Using Aminosilanes. Mater. Chem. Phys. 2005, 94 (1), 148– 152, DOI: 10.1016/j.matchemphys.2005.04.023
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Effect of amine groups in the synthesis of Ag nanoparticles using aminosilanes
Frattini, A.; Pellegri, N.; Nicastro, D.; de Sanctis, O.
Materials Chemistry and Physics (2005), 94 (1), 148-152CODEN: MCHPDR; ISSN:0254-0584. (Elsevier B.V.)
This work presents a simple method to produce Ag nanoparticles through AgNO3 chem. redn. in a continuous media. Aminosilanes act as catalytic reactors and superficial modifiers of Ag nanoparticles, inhibiting their growth and avoiding aggregation. Nanoparticle formation was studied by UV-vis spectroscopy, AFM, and dynamic light scattering (DLS) techniques. The extent of the redn. reaction increases with either a higher aminosilane concn. or with aminosilanes with a higher no. of amine groups. The no. of amine groups in the aminosilane has also a strong effect on the size of the resulting Ag particles. The morphol. of the Ag nanoparticles obtained is spherical and the mean size is of ≈5 nm.
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Frenkel, A. I.; Nemzer, S.; Pister, I.; Soussan, L.; Harris, T.; Sun, Y.; Rafailovich, M. H. Size-Controlled Synthesis and Characterization of Thiol-Stabilized Gold Nanoparticles. J. Chem. Phys. 2005, 123 (18), 184701, DOI: 10.1063/1.2126666
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Size-controlled synthesis and characterization of thiol-stabilized gold nanoparticles
Frenkel, A. I.; Nemzer, S.; Pister, I.; Soussan, L.; Harris, T.; Sun, Y.; Rafailovich, M. H.
Journal of Chemical Physics (2005), 123 (18), 184701/1-184701/6CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
Size-controlled synthesis of nanoparticles of less than a few nanometers in size is a challenge due to the spatial resoln. limit of most scattering and imaging techniques used for their structural characterization. The authors present the self-consistent anal. of the extended x-ray absorption fine-structure (EXAFS) spectroscopy data of ligand-stabilized metal nanoclusters. Method employs the coordination no. truncation and the surface-tension models to measure the av. diam. and analyze the structure of the nanoparticles. EXAFS anal. was performed on the 2 series of dodecanethiol-stabilized Au nanoparticles prepd. by 1-phase and 2-phase syntheses where the only control parameter was the Au/thiol ratio ξ, varied between 6:1 and 1:6. The 2-phase synthesis resulted in the smaller particles whose size decreased monotonically and stabilized at 16 Å when ξ was lowered <1:1. This behavior is consistent with the theor. predicted thermodn. limit obtained previously in the framework of the spherical drop model of Au nanoparticles.
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Lv, C.-Q.; Li, J.; Ling, K.-C.; Shang, Z.-F.; Wang, G.-C. Methylamine Decomposition on Nickel Surfaces: A Density Functional Theory Study. Surf. Sci. 2010, 604 (9), 779– 787, DOI: 10.1016/j.susc.2010.01.027
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Methylamine decomposition on Ni surfaces. A density functional theory study
Lv, Cun-Qin; Li, Jun; Ling, Kai-Cheng; Shang, Zhen-Feng; Wang, Gui-Chang
Surface Science (2010), 604 (9-10), 779-787CODEN: SUSCAS; ISSN:0039-6028. (Elsevier B.V.)
The adsorption and decompn. of methylamine on Ni(1 1 1), Ni(1 0 0), stepped Ni(1 1 1), and N atom modified Ni(1 0 0) (denoted N-Ni(1 0 0)) were studied with the DFT-GGA method using the periodic slab models. The initial scissions of C-H, N-H and C-N bond are considered. The adsorption energies under the most stable configurations for the possible species and the activation energies for the possible initial elementary reactions involved are obtained in the present work. Through systematic exploring of the kinetics mechanism of methylamine decompn. on these 4 surfaces, it is found that the reactivity of these surfaces decreased with the order of stepped Ni(1 1 1) > Ni(1 0 0) > Ni(1 1 1) > N-Ni(1 0 0). The reactivity is related to the openness of the surface, and the presence of N atom reduces the reactivity of the Ni(1 0 0). For the 3 reactions, the barriers decreased with the order of C-N > N-H > C-H on Ni(1 1 1) and Ni(1 0 0), whereas they decreased with the order of C-N > C-H > N-H on stepped Ni(1 1 1) and N-Ni(1 0 0).
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Vandevondele, J.; Krack, M.; Mohamed, F.; Parrinello, M.; Chassaing, T.; Hutter, J. Quickstep: Fast and Accurate Density Functional Calculations Using a Mixed Gaussian and Plane Waves Approach. Comput. Phys. Commun. 2005, 167 (2), 103– 128, DOI: 10.1016/j.cpc.2004.12.014
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QUICKSTEP: fast and accurate density functional calculations using a mixed Gaussian and plane waves approach
VandeVondele, Joost; Krack, Matthias; Mohamed, Fawzi; Parrinello, Michele; Chassaing, Thomas; Hutter, Juerg
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Abstract
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Figure 1
Figure 1. Different structures involved in E_seg calculations: (a) side view of the bulk structure, (b) top view of the dopant (Au) on the (111) host metal surface, (c) H₃C–S hollow adsorption on a (111) surface, (d) H₃C–NH₂ top adsorption on a (111) surface, and (e) dopant (Cu) on a (111) metal host surface with H₃C–NH adsorbed on the bridge position.
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Figure 2
Figure 2. Optimized structures of a single metal atom bonded with (a) H₃C–NH₂ and (b) H₃C–NH, and (c) H₃C–S ligands. The colors represent different atoms: green is the metal atom of interest, blue is nitrogen, yellow is sulfur, black is carbon, and white is hydrogen.
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Figure 3
Figure 3. (a) Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the absence of an adsorbate. Color indicates the different metal hosts, and the marker type indicates the different dopants. The inset figure demonstrates the SAA in the absence of an adsorbate. (b) Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the absence of an adsorbate, with data points being colored based on the change in the radius between the metal host and dopant. A darker shade indicates that the radius of the metal host is larger than that of the dopant, while lighter shade indicates that the radius of the host is smaller than that of the dopant.
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Figure 4
Figure 4. Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the presence of adsorbed (a) H₃C–NH₂ and (b) H₃C–NH. Colors indicate the different metal hosts, and symbols indicate the different metal dopants. The inset image demonstrates SAA in the presence of ligand.
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Figure 5
Figure 5. (a) Parity plot between E_seg,111 and E_seg,100 of d⁸- and d⁹-based SAAs in the presence of an adsorbed H₃C–S ligand. Colors indicate the different metal hosts, and symbols indicate the different metal dopants (as in Figure 4). Top view of H₃C–S on (b) pure Ni (111) and (c) Ag dopant on a Ni (111) surface, where the adsorbate preferentially interacts with the metal host moving away from the dopant. Silver color represents the Ag, yellow the S, black the C, and white the H atom. The blue transparent circles in (a) refer to the cases where the thiolate does not bind with the dopant but preferentially interacts with the metal host.
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Figure 6
Figure 6. Variable importance based on random forest regression.
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Figure 7
Figure 7. Parity plot between the NN MLP model predictions and DFT E_seg of the test set (27 data points are the test set, and 153 points are the training set). Colors indicate the different metal hosts, symbols indicate the different metal dopants, and edge color represents the adsorbate.
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References
This article references 46 other publications.
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  Platinum is ubiquitous in the prodn. sectors of chems. and fuels; however, its scarcity in nature and high price will limit future proliferation of platinum-catalyzed reactions. One promising approach to conserve platinum involves understanding the smallest no. of platinum atoms needed to catalyze a reaction, then designing catalysts with the minimal platinum ensembles. Here, we design and test a new generation of platinum-copper nanoparticle catalysts for the selective hydrogenation of 1,3-butadiene,, an industrially important reaction. Isolated platinum atom geometries enable hydrogen activation and spillover but are incapable of C-C bond scission that leads to loss of selectivity and catalyst deactivation. γ-Alumina-supported single-atom alloy nanoparticle catalysts with <1 platinum atom per 100 copper atoms are found to exhibit high activity and selectivity for butadiene hydrogenation to butenes under mild conditions, demonstrating transferability from the model study to the catalytic reaction under practical conditions.
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  Single-Atom Alloy Catalysis
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  Chemical Reviews (Washington, DC, United States) (2020), 120 (21), 12044-12088CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
  A review. Single-atom alloys (SAAs) play an increasingly significant role in the field of single-site catalysis and are typically composed of catalytically active elements atomically dispersed in more inert and catalytically selective host metals. SAAs have been shown to catalyze a range of industrially important reactions in electro-, photo-, and thermal catalysis studies. Due to the unique geometry of SAAs, the location of the transition state and the binding site of reaction intermediates are often decoupled, which can enable both facile dissocn. of reactants and weak binding of intermediates, two key factors for efficient and selective catalysis. Often, this results in deviations from transition metal scaling relationships that limit conventional catalysts. SAAs also offer reduced susceptibility to CO poisoning, cost savings from reduced precious metal usage, opportunities for bifunctional mechanisms via spillover, and higher resistance to deactivation by coking that plagues many industrial catalysts. In this review, we begin by introducing SAAs and describe how model systems and nanoparticle catalysts can be prepd. and characterized. We then review all available SAA literature on a per reaction basis before concluding with a description of the general properties of this new class of heterogeneous catalysts and presenting opportunities for future research and development.
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  Single-atom alloy catalysts: structural analysis, electronic properties and catalytic activities
  Zhang, Tianjun; Walsh, Andrew G.; Yu, Jihong; Zhang, Peng
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  A review. Monometallic catalysts, in particular those contg. noble metals, are frequently used in heterogeneous catalysis, but they are expensive, rare and the ability to tailor their structures and properties remains limited. Traditionally, alloy catalysts have been used instead that feature enhanced electronic and chem. properties at a reduced cost. Furthermore, the introduction of single metal atoms anchored onto supports provided another effective strategy to increase both the at. efficiency and the chance of tailoring the properties. Most recently, single-atom alloy catalysts have been developed in which one metal is atomically dispersed throughout the catalyst via alloy bonding; such catalysts combine the traditional advantages of alloy catalysts with the new feature of tailoring properties achievable with single atom catalysts. This review will first outline the at. scale structural anal. on single-atom alloys using microscopy and spectroscopy tools, such as high-angle annular dark field imaging-scanning transmission electron microscopy and extended X-ray absorption fine structure spectroscopy. Next, progress in research to understand the electronic properties of single-atom alloys using X-ray spectroscopy techniques and quantum calcns. will be presented. The catalytic activities of single-atom alloys in a few representative reactions will be further discussed to demonstrate their structure-property relationships. Finally, future perspectives for single-atom alloy catalysts from the structural, electronic and reactivity aspects will be proposed.
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  Performance of Cu-Alloyed Pd Single-Atom Catalyst for Semihydrogenation of Acetylene under Simulated Front-End Conditions
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  ACS Catalysis (2017), 7 (2), 1491-1500CODEN: ACCACS; ISSN:2155-5435. (American Chemical Society)
  Selective hydrogenation of acetylene to ethylene is an industrially important reaction. Pd-based catalysts have been proved to be efficient for the acetylene conversion, while enhancing the selectivity to ethylene is challenging. Here, we chose Cu as the partner of Pd, fabricated an alloyed Pd single-atom catalyst (SAC), and investigated its catalytic performance for the selective hydrogenation of acetylene to ethylene under a simulated front-end hydrogenation process in industry: i.e., with a high concn. of hydrogen and ethylene. The Cu-alloyed Pd SAC showed ∼85% selectivity to ethylene and 100% acetylene elimination. In comparison with the Au- or Ag-alloyed Pd SAC, the Cu-alloyed analog exceeded both of them in conversion, while the selectivity rivaled that of the Ag-alloyed Pd SAC and surpassed that of the Au-alloyed Pd SAC. As Cu is a low-cost metal, Cu-alloyed Pd SAC would minimize the noble-metal usage and possess high utilization potential for industry. The Cu-alloyed Pd SAC was verified by EXAFS, with the Pd/Cu at. ratio lowered to 0.006, corresponding to the loading of Pd at 494 ppm. The microcalorimetric measurement results demonstrated that the adsorption of C2H4 over the Cu-alloyed Pd SAC was weaker than that over the catalyst with large Pd ensembles; thus, the selectivity to ethylene was greatly enhanced. At the same time, the adsorption of H2 was stronger than that over the corresponding monometallic Cu catalyst; thus, the activation of H2 was obviously promoted. On the basis of the above results, a possible reaction path over the Cu-alloyed Pd SAC was proposed. Furthermore, by systematic comparison of the IB-metal-alloyed Pd SACs, we found that the apparent activation energies of the IB-metal-alloyed Pd SACs were close to each other, indicating similar active sites and/or catalytic mechanisms over the three catalysts. The isolation of the Pd atoms by the IB metal distinctly contributed to both the conversion and the selectivity. Further DFT calcn. results suggested that electron transfer between the IB metal and Pd might be responsible for their different selectivities to ethylene.
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  Single atom alloy: An emerging atomic site material for catalytic applications
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  Nano Today (2020), 34 (), 100917CODEN: NTAOCG; ISSN:1748-0132. (Elsevier Ltd.)
  A review. The development of low-cost, high-performance catalysts at the at. level has become a challenging issue for large-scale applications of renewable clean energy technologies. Atomic sites catalysts, such as single atoms catalysts (SAC), single clusters catalysts (SCC), single-atom alloys (SAA), have proved their performance in various catalytic reactions due to their extremely high atom utilization efficiency, unique structure, and exceptional catalytic selectivity. A deep understanding and design of the active center of the catalyst at the at. level has become a top priority for current research. Compared with SAC and SCC, SAA has its own uniqueness. In this review, we focused on the recent progress on the prepn. methods of SAA and discussed the key factors controlling the structure of SAA. In addn., several important catalytic reactions performed over well-defined SAA are analyzed. Finally, the challenges and the perspectives of this cutting-edge field are suggested. We believe that this crit. review provides a guidance for the rational design of SAA for catalytic applications.
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  Single atom alloy: An emerging atomic site material for catalytic applications
  Mao, Junjie; Yin, Jiansong; Pei, Jiajing; Wang, Dingsheng; Li, Yadong
  Nano Today (2020), 34 (), 100917CODEN: NTAOCG; ISSN:1748-0132. (Elsevier Ltd.)
  A review. The development of low-cost, high-performance catalysts at the at. level has become a challenging issue for large-scale applications of renewable clean energy technologies. Atomic sites catalysts, such as single atoms catalysts (SAC), single clusters catalysts (SCC), single-atom alloys (SAA), have proved their performance in various catalytic reactions due to their extremely high atom utilization efficiency, unique structure, and exceptional catalytic selectivity. A deep understanding and design of the active center of the catalyst at the at. level has become a top priority for current research. Compared with SAC and SCC, SAA has its own uniqueness. In this review, we focused on the recent progress on the prepn. methods of SAA and discussed the key factors controlling the structure of SAA. In addn., several important catalytic reactions performed over well-defined SAA are analyzed. Finally, the challenges and the perspectives of this cutting-edge field are suggested. We believe that this crit. review provides a guidance for the rational design of SAA for catalytic applications.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsVGqsL%252FP&md5=5aa8a50b909c406640064e98d83952b8
7. 7
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  Discovery of single-atom alloy catalysts for CO2-to-methanol reaction by density functional theory calculations
  Li, Meng; Hua, Bin; Wang, Lu-Cun; Zhou, Zheng; Stowers, Kara J.; Ding, Dong
  Catalysis Today (2022), 388-389 (), 403-409CODEN: CATTEA; ISSN:0920-5861. (Elsevier B.V.)
  The transformations of CO2 mols. into valuable products are of increasing interest due to the neg. impact of anthropogenic CO2 emissions on global warming. The CO2-to-methanol hydrogenation is an economically profitable reaction of carbon fixation, but it still steps away from widespread industrialization because of the lack of efficient and selective catalysts. Recently, single-atom alloy (SAA) catalysts have been developed to work remarkably in CO2 hydrogenation reactions. Doping isolated single atoms into metallic catalyst can dramatically alter the catalytic performance of the host. A screening discovery is performed on Ru and 6 RuX (X = Fe, Co, Ni, Cu, Ir and Pt) SAAs using d. functional theory (DFT) computations. A 13 possible elementary reactions are considered in 4 possible reaction pathways on Ru and all RuX surfaces. In the computed mechanisms, it was found that the formation of *H2COOH and *HCOO intermediates plays a crit. role in detg. catalysts' activities. Doping Co and Pt isolated single atoms into Ru surface can thermodynamically and kinetically facilitate these intermediates formation processes, eventually promoting the prodn. of methanol. The combination of weak binding and enhanced charge redistribution on RuCo and RuPt surfaces give them improved catalytic activities over pure Ru. This work will ultimately facilitate the discovery and development of SAAs for CO2 to methanol, serving as guidance to expts. and theoreticians alike.
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8. 8
  Liu, J.; Lucci, F. R.; Yang, M.; Lee, S.; Marcinkowski, M. D.; Therrien, A. J.; Williams, C. T.; Sykes, E. C. H.; Flytzani-Stephanopoulos, M. Tackling CO Poisoning with Single-Atom Alloy Catalysts. J. Am. Chem. Soc. 2016, 138 (20), 6396– 6399, DOI: 10.1021/jacs.6b03339
  8
  Tackling CO Poisoning with Single-Atom Alloy Catalysts
  Liu, Jilei; Lucci, Felicia R.; Yang, Ming; Lee, Sungsik; Marcinkowski, Matthew D.; Therrien, Andrew J.; Williams, Christopher T.; Sykes, E. Charles H.; Flytzani-Stephanopoulos, Maria
  Journal of the American Chemical Society (2016), 138 (20), 6396-6399CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  Platinum catalysts are extensively used in the chem. industry and as electrocatalysts in fuel cells. Pt is notorious for its sensitivity to poisoning by strong CO adsorption. Here we demonstrate that the single-atom alloy (SAA) strategy applied to Pt reduces the binding strength of CO while maintaining catalytic performance. By using surface sensitive studies, we detd. the binding strength of CO to different Pt ensembles, and this in turn guided the prepn. of PtCu alloy nanoparticles (NPs). The at. ratio Pt:Cu = 1:125 yielded a SAA which exhibited excellent CO tolerance in H2 activation, the key elementary step for hydrogenation and hydrogen electro-oxidn. As a probe reaction, the selective hydrogenation of acetylene to ethene was performed under flow conditions on the SAA NPs supported on alumina without activity loss in the presence of CO. The ability to maintain reactivity in the presence of CO is vital to other industrial reaction systems, such as hydrocarbon oxidn., electrochem. methanol oxidn., and hydrogen fuel cells.
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9. 9
  Xing, F.; Jeon, J.; Toyao, T.; Shimizu, K.; Furukawa, S. A Cu-Pd Single-Atom Alloy Catalyst for Highly Efficient NO Reduction. Chem. Sci. 2019, 10 (36), 8292– 8298, DOI: 10.1039/C9SC03172C
  9
  A Cu-Pd single-atom alloy catalyst for highly efficient NO reduction
  Xing, Feilong; Jeon, Jaewan; Toyao, Takashi; Shimizu, Ken-ichi; Furukawa, Shinya
  Chemical Science (2019), 10 (36), 8292-8298CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)
  A series of Cu-Pd alloy nanoparticles supported on Al2O3 were prepd. and tested as catalysts for deNOx reactions. XRD, HAADF-STEM, XAFS, and FT-IR analyses revealed that a single-atom alloy structure was formed when the Cu/Pd ratio was 5, where Pd atoms were well isolated by Cu atoms. Compared with Pd/Al2O3, Cu5Pd/Al2O3 exhibited outstanding catalytic activity and N2 selectivity in the redn. of NO by CO: for the first time, the complete conversion of NO to N2 was achieved even at 175 °C, with long-term stability for at least 30 h. High catalytic performance was also obtained in the presence of O2 and C3H6 (model exhaust gas), where a 90% decrease in Pd use was achieved with min. evolution of N2O. Kinetic and DFT studies demonstrated that N-O bond breaking of the (NO)2 dimer was the rate-detg. step and was kinetically promoted by the isolated Pd.
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10. 10
  Yang, K.; Yang, B. Surface Restructuring of Cu-Based Single-Atom Alloy Catalysts under Reaction Conditions: The Essential Role of Adsorbates. Phys. Chem. Chem. Phys. 2017, 19 (27), 18010– 18017, DOI: 10.1039/C7CP02152F
  10
  Surface restructuring of Cu-based single-atom alloy catalysts under reaction conditions: the essential role of adsorbates
  Yang, Kunran; Yang, Bo
  Physical Chemistry Chemical Physics (2017), 19 (27), 18010-18017CODEN: PPCPFQ; ISSN:1463-9076. (Royal Society of Chemistry)
  The stabilities and catalytic performances of single-atom alloy (SAA) structures under the reaction conditions of acetylene hydrogenation are thoroughly examd. utilizing d. functional theory (DFT) calcns. Four Cu-based alloys with stable SAA structures reported before, namely PdCu, PtCu, RhCu and NiCu alloys, are investigated here. We find that the SAA structures of PdCu and PtCu are stable during the reaction, while the RhCu-SAA and NiCu-SAA structures are thermodynamically unstable upon acetylene adsorption and surface restructuring through the aggregation of the Rh and Ni atoms on the surfaces may also happen. It is also found that all the investigated structures of RhCu and NiCu alloys may give rise to the further hydrogenation of ethylene. However, desorption of ethylene is favored over the PdCu-SAA and PtCu-SAA surfaces, indicating that acetylene could be selectively hydrogenated to ethylene over these two surfaces, which is consistent with the exptl. observations reported in the literature. Our work provides new understandings regarding SAA surface structures under reaction conditions and their catalytic reaction performances upon aggregation of the doped metal atoms.
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11. 11
  Darby, M. T.; Sykes, E. C. H.; Michaelides, A.; Stamatakis, M. Carbon Monoxide Poisoning Resistance and Structural Stability of Single Atom Alloys. Top Catal 2018, 61 (5), 428– 438, DOI: 10.1007/s11244-017-0882-1
  11
  Carbon Monoxide Poisoning Resistance and Structural Stability of Single Atom Alloys
  Darby, Matthew T.; Sykes, E. Charles H.; Michaelides, Angelos; Stamatakis, Michail
  Topics in Catalysis (2018), 61 (5-6), 428-438CODEN: TOCAFI; ISSN:1022-5528. (Springer)
  Pt group metals (PGMs) serve as highly active catalysts in a variety of heterogeneous chem. processes. Unfortunately, their high activity is accompanied by a high affinity for CO and thus, PGMs are susceptible to poisoning. Alloying PGMs with metals exhibiting lower affinity to CO could be an effective strategy toward preventing such poisoning. The authors use d. functional theory to demonstrate this strategy, focusing on highly dil. alloys of PGMs (Pd, Pt, Rh, Ir and Ni) with poison resistant coinage metal hosts (Cu, Ag, Au), such that individual PGM atoms are dispersed at the at. limit forming single atom alloys (SAAs). Compared to the pure metals, CO exhibits lower binding strength on the majority of SAAs studied, and the authors use kinetic Monte Carlo simulation to obtain relevant temp. programed desorption spectra, which are in good agreement with expts. Addnl., the effects of CO adsorption on the structure of SAAs are considered. The authors calc. segregation energies which are indicative of the stability of dopant atoms in the bulk compared to the surface layer, as well as aggregation energies to det. the stability of isolated surface dopant atoms compared to dimer and trimer configurations. The authors' calcns. reveal that CO adsorption induces dopant atom segregation into the surface layer for all SAAs considered here, whereas aggregation and island formation may be promoted or inhibited depending on alloy constitution and CO coverage. This observation suggests the possibility of controlling ensemble effects in novel catalyst architectures through CO-induced aggregation and kinetic trapping.
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12. 12
  Kim, H. Y.; Henkelman, G. CO Adsorption-Driven Surface Segregation of Pd on Au/Pd Bimetallic Surfaces: Role of Defects and Effect on CO Oxidation. ACS Catal. 2013, 3 (11), 2541– 2546, DOI: 10.1021/cs4006259
  12
  CO Adsorption-Driven Surface Segregation of Pd on Au/Pd Bimetallic Surfaces: Role of Defects and Effect on CO Oxidation
  Kim, Hyun You; Henkelman, Graeme
  ACS Catalysis (2013), 3 (11), 2541-2546CODEN: ACCACS; ISSN:2155-5435. (American Chemical Society)
  We use d. functional theory (DFT) to study CO-adsorption-induced Pd surface segregation in Au/Pd bimetallic surfaces, dynamics of Pd-Au swapping, effect of defects on the swapping rate, CO-induced Pd clustering, and the reaction mechanism of CO oxidn. The strong CO-philic nature of Pd atoms supplies a driving force for the preferential surface segregation of Pd atoms and Pd cluster formation. Surface vacancies are found to dramatically accelerate the rate of Pd-Au swapping. We find that Pd clusters consisting of at least four Pd atoms prefer to bind O2 rather than CO. These clusters facilitate the rapid dissocn. of O2 and supply reactive oxygen species for CO oxidn. Our findings suggest that geometric, electronic, and dynamic effects should be considered in the function of bimetallic alloys or nanoparticles whose components asym. interact with reacting mols.
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13. 13
  Papanikolaou, K. G.; Darby, M. T.; Stamatakis, M. CO-Induced Aggregation and Segregation of Highly Dilute Alloys: A Density Functional Theory Study. J. Phys. Chem. C 2019, 123 (14), 9128– 9138, DOI: 10.1021/acs.jpcc.9b00649
  13
  CO-induced aggregation and segregation of highly dilute alloys: Density functional theory study
  Papanikolaou, Konstantinos G.; Darby, Matthew T.; Stamatakis, Michail
  Journal of Physical Chemistry C (2019), 123 (14), 9128-9138CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)
  Highly dil. binary alloys composed of an active platinum group metal (PGM) and a more inert coinage metal are important in the field of catalysis, as they function as active and selective catalysts. Their catalytic properties depend on the surface "ensemble" of PGM atoms, whose size may be altered under reactive conditions. We use d. functional theory and investigate the interaction of CO, a mol. common in numerous industrially important chemistries, with alloys that are composed of a PGM (Pt, Pd, Rh, Ir, and Ni) doped in coinage metal hosts (Cu, Au, and Ag). We study the adsorption of CO on the (211) step and (100) facet and compare our results to those previously obtained on the (111) facet. We det. strong correlations between the adsorption energies of CO across the facets and highlight the corresponding thermochem. scaling relations. Finally, we study the stability of isolated surface dopant atoms with respect to aggregation into clusters and segregation into the bulk, both in the presence and absence of CO. We find that strong CO-dopant interactions significantly influence the morphol. of the catalyst surface, suggesting that it may be possible to establish control over the ensemble size of the dopant by tuning Pco.
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14. 14
  Wang, Q.; Zhu, B.; Tielens, F.; Tichit, D.; Guesmi, H. Mapping Surface Segregation of Single-Atom Pt Dispersed in M Surfaces (M = Cu, Ag, Au, Ni, Pd, Co, Rh and Ir) under Hydrogen Pressure at Various Temperatures. Appl. Surf. Sci. 2021, 548, 149217, DOI: 10.1016/j.apsusc.2021.149217
  14
  Mapping surface segregation of single-atom Pt dispersed in M surfaces (M = Cu, Ag, Au, Ni, Pd, Co, Rh and Ir) under hydrogen pressure at various temperatures
  Wang, Qing; Zhu, Beien; Tielens, Frederik; Tichit, Didier; Guesmi, Hazar
  Applied Surface Science (2021), 548 (), 149217CODEN: ASUSEE; ISSN:0169-4332. (Elsevier B.V.)
  Single-atom alloys (SAAs) are emerging materials contg. isolated metal atoms dispersed on host metal surfaces, exhibiting unique reactivity compared with the corresponding monometallic counterparts. However, the stability of the isolated atoms in the host metal has hardly been studied, although, metal segregation has been commonly obsd. in bimetallic nanoparticles under reaction conditions. In this work we focus on single-atom Pt anchored on various metallic support surfaces. D. Functional Theory (DFT) calcns. coupled with environmental segregation energy anal. are performed to map the segregation trends of 22 different Pt-SAA surfaces under various hydrogen conditions. The results show the high stability of single-atom Pt in Ni, Co, Rh and Ir host metallic surfaces while no stability is predicted on Au and Ag surfaces. For Pd and Cu host supports, the single-atom Pt is found to be stable on sp. surface facets and within definite temp. and pressure conditions. This work brings an important understanding of SAA systems through the prediction of surface at. ordering changes under operating conditions which related to the reactivity will ultimately allow the design of more efficient catalysts.
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15. 15
  Farsi, L.; Deskins, N. A. First Principles Analysis of Surface Dependent Segregation in Bimetallic Alloys. Phys. Chem. Chem. Phys. 2019, 21 (42), 23626– 23637, DOI: 10.1039/C9CP03984H
  15
  First principles analysis of surface dependent segregation in bimetallic alloys
  Farsi, Lida; Deskins, N. Aaron
  Physical Chemistry Chemical Physics (2019), 21 (42), 23626-23637CODEN: PPCPFQ; ISSN:1463-9076. (Royal Society of Chemistry)
  Stability is an important aspect of alloys, and proposed alloys may be unstable due to unfavorable at. 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 d. 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).
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16. 16
  Han, Z.-K.; Sarker, D.; Ouyang, R.; Mazheika, A.; Gao, Y.; Levchenko, S. V. Single-Atom Alloy Catalysts Designed by First-Principles Calculations and Artificial Intelligence. Nat. Commun. 2021, 12 (1), 1833, DOI: 10.1038/s41467-021-22048-9
  16
  Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence
  Han, Zhong-Kang; Sarker, Debalaya; Ouyang, Runhai; Mazheika, Aliaksei; Gao, Yi; Levchenko, Sergey V.
  Nature Communications (2021), 12 (1), 1833CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
  Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissocn. and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large no. of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with d.-functional inputs. Besides consistently predicting efficiency of the exptl. studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qual. anal. of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.
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17. 17
  Wang, L.-L.; Johnson, D. D. Predicted Trends of Core-Shell Preferences for 132 Late Transition-Metal Binary-Alloy Nanoparticles. J. Am. Chem. Soc. 2009, 131 (39), 14023– 14029, DOI: 10.1021/ja903247x
  17
  Predicted trends of core-shell preferences for 132 late transition-metal binary-alloy nanoparticles
  Wang, Lin-Lin; Johnson, Duane D.
  Journal of the American Chemical Society (2009), 131 (39), 14023-14029CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
  Transition metal-alloyed nanoparticles with core-shell features (shell enrichment by one of the metals) are becoming ubiquitous, from (electro-)catalysis to biomedical applications, due to their size control, performance, biocompatibility, and cost. Many binary-alloyed nanoparticle systems from groups 8-11 in the Periodic Table were investigated by using d. functional theory, and their segregation energies were systematically explored to det. their core-shell preferences. The core-shell preferences are generally described by two independent factors (1) cohesive energy related to vapor pressure (2) at. size quantified by the Wigner-Seitz radius, and the interplay between them. These independent factors provide general trends for the surface segregation preference for atoms in nanoparticles as well as semi-infinite surfaces, and give a simple correlation or design map for the alloying and catalytic behavior. A universal description of the core-shell preference is provided by the tight-binding theory involving band-energy differences that (1) quant. reproduces the d. functional theory segregation energies (2) confirms the electronic origins and correlations for core-shell behavior.
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18. 18
  Tréglia, G.; Legrand, B.; Ducastelle, F. Segregation and Ordering at Surfaces of Transition Metal Alloys: The Tight-Binding Ising Model. Europhysics Letters (EPL) 1988, 7 (7), 575– 580, DOI: 10.1209/0295-5075/7/7/001
  There is no corresponding record for this reference.
19. 19
  Salem, M.; Cowan, M. J.; Mpourmpakis, G. Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning. ACS Omega 2022, 7 (5), 4471– 4481, DOI: 10.1021/acsomega.1c06337
  19
  Predicting Segregation Energy in Single Atom Alloys Using Physics and Machine Learning
  Salem, Maya; Cowan, Michael J.; Mpourmpakis, Giannis
  ACS Omega (2022), 7 (5), 4471-4481CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)
  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 calc. surface segregation energy. D. 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 methodol., we expanded our anal. 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 phys. 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 Eseg 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 physicochem. properties driving thermodn. stabilization of SAAs.
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20. 20
  Yan, Z.; Taylor, M. G.; Mascareno, A.; Mpourmpakis, G. Size-, Shape-, and Composition-Dependent Model for Metal Nanoparticle Stability Prediction. Nano Lett. 2018, 18 (4), 2696– 2704, DOI: 10.1021/acs.nanolett.8b00670
  20
  Size-, shape-, and composition-dependent model for metal nanoparticle stability prediction
  Yan, Zihao; Taylor, Michael G.; Mascareno, Ashley; Mpourmpakis, Giannis
  Nano Letters (2018), 18 (4), 2696-2704CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)
  Although tremendous applications for metal nanoparticles have been found in modern technologies, the understanding of their stability as related to morphol. (size and shape) and chem. ordering (e.g., in bimetallics) remains limited. First-principles methods such as d. functional theory (DFT) are capable of capturing accurate nanoalloy energetics; however, they are limited to very small nanoparticle sizes (<2 nm in diam.) due to their computational cost. Herein, we propose a bond-centric (BC) model able to capture cohesive energy trends over a range of monometallic and bimetallic nanoparticles and mixing behavior (excess energy) of nanoalloys, in great agreement with DFT calcns. We apply the BC model to screen the energetics of a recently reported 23 196-atom FePt nanoalloys (Yang et al. Nature 2017, 542, 75-79), offering insights into both segregation and bulk-chem. ordering behavior. Because the BC model utilizes tabulated data (diat. bond energies and bulk cohesive energies) and structural information on nanoparticles (coordination nos.), it can be applied to calc. the energetics of any nanoparticle morphol. and chem. compn., thus significantly accelerating nanoalloy design.
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21. 21
  Dhouib, A.; Guesmi, H. DFT Study of the M Segregation on MAu Alloys (M = Ni, Pd, Pt) in Presence of Adsorbed Oxygen O and O2. Chem. Phys. Lett. 2012, 521, 98– 103, DOI: 10.1016/j.cplett.2011.11.050
  21
  DFT study of the M segregation on MAu alloys (M = Ni, Pd, Pt) in presence of adsorbed oxygen O and O2
  Dhouib, Adnene; Guesmi, Hazar
  Chemical Physics Letters (2012), 521 (), 98-103CODEN: CHPLBC; ISSN:0009-2614. (Elsevier B.V.)
  Segregation phenomena of Group 10 (M = Ni, Pd, Pt) transition-metals substituted in Au(111) surface and sub-surface layers are studied by DFT periodic calcns. in presence of adsorbed at. and mol. oxygen. In contrast with vacuum conditions, where the metal impurities M prefer to be in the bulk of gold, in the presence of adsorbed O or O2, the impurities mainly segregate to the surface. The anal. of oxygen adsorption trends and electronic surface structures explain the change in the local at. arrangement which is expected to occur on the surface of alloys during reaction conditions.
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22. 22
  Løvvik, O. M.; Opalka, S. M. Reversed Surface Segregation in Palladium-Silver Alloys Due to Hydrogen Adsorption. Surf. Sci. 2008, 602 (17), 2840– 2844, DOI: 10.1016/j.susc.2008.07.016
  22
  Reversed surface segregation in palladium-silver alloys due to hydrogen adsorption
  Lovvik, O. M.; Opalka, Susanne M.
  Surface Science (2008), 602 (17), 2840-2844CODEN: SUSCAS; ISSN:0039-6028. (Elsevier B.V.)
  Ag segregates to the surface of pure and ideal Pd-Ag alloy surfaces. By first-principles band-structure calcns. it is shown how this may be changed when H is adsorbed on a Pd-Ag(1 1 1) surface. Due to H binding more strongly to Pd than to Ag, there is a clear energy gain from a reversal of the surface segregation. H-induced segregation may provide a fundamental explanation for the H or reducing treatments that are required to activate H-selective membrane or catalyst performance.
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23. 23
  Dhifallah, M.; Dhouib, A.; Aldulaijan, S.; Renzo, F. D. I.; Guesmi, H. First-Principles Study of Au-Cu Alloy Surface Changes Induced by Gas Adsorption of CO, NO, or O2. J. Chem. Phys. 2016, 145 (2), 024701, DOI: 10.1063/1.4955104
  23
  First-principles study of Au-Cu alloy surface changes induced by gas adsorption of CO, NO, or O2
  Dhifallah, Marwa; Dhouib, Adnene; Aldulaijan, Sarah; Renzo, Francesco D. I.; Guesmi, Hazar
  Journal of Chemical Physics (2016), 145 (2), 024701/1-024701/8CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  The surface compn. of bimetallics can be strongly altered by adsorbing mols. where the metal with the strongest interaction with the adsorbate segregates into the surface. To study the effect of reactive gas on the surface compn. of Au-Cu alloy, the authors examd. by d. functional theory to study the segregation behavior of Cu in Au matrixes. The adsorption mechanisms of CO, NO, and O2 gas mols. on Au, Cu, and Au-Cu low index (111), (100), and (110) surfaces were analyzed from energetic and electronic points of view. The results show a strong segregation of Cu toward the (110) surface in the presence of all adsorbed mols. The Cu segregation toward the (111) and (100) surface could occur only in the presence of CO and at a lower extent in the presence of NO. The anal. of the electronic structure highlights the different binding characters of adsorbates inducing the Cu segregation. (c) 2016 American Institute of Physics.
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24. 24
  Sulley, G. A.; Hamm, J.; Montemore, M. M. Machine Learning Approach for Screening Alloy Surfaces for Stability in Catalytic Reaction Conditions. Journal of Physics: Energy 2023, 5 (1), 015002, DOI: 10.1088/2515-7655/aca122
  There is no corresponding record for this reference.
25. 25
  Heuer-Jungemann, A.; Feliu, N.; Bakaimi, I.; Hamaly, M.; Alkilany, A.; Chakraborty, I.; Masood, A.; Casula, M. F.; Kostopoulou, A.; Oh, E. The Role of Ligands in the Chemical Synthesis and Applications of Inorganic Nanoparticles. Chem. Rev. 2019, 119 (8), 4819– 4880, DOI: 10.1021/acs.chemrev.8b00733
  25
  The Role of Ligands in the Chemical Synthesis and Applications of Inorganic Nanoparticles
  Heuer-Jungemann, Amelie; Feliu, Neus; Bakaimi, Ioanna; Hamaly, Majd; Alkilany, Alaaldin; Chakraborty, Indranath; Masood, Atif; Casula, Maria F.; Kostopoulou, Athanasia; Oh, Eunkeu; Susumu, Kimihiro; Stewart, Michael H.; Medintz, Igor L.; Stratakis, Emmanuel; Parak, Wolfgang J.; Kanaras, Antonios G.
  Chemical Reviews (Washington, DC, United States) (2019), 119 (8), 4819-4880CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
  A review. The authors provide a comprehensive review on the role of the ligands with respect to the nanoparticle morphol., stability, and function. The design of nanoparticles is crit. for their efficient use in many applications ranging from biomedicine to sensing and energy. While shape and size are responsible for the properties of the inorg. nanoparticle core, the choice of ligands is of utmost importance for the colloidal stability and function of the nanoparticles. Moreover, the selection of ligands employed in nanoparticle synthesis can det. their final size and shape. Ligands added after nanoparticle synthesis infer both new properties as well as provide enhanced colloidal stability. The authors analyze the interaction of nanoparticle surface and ligands with different chem. groups, the types of bonding, the final dispersibility of ligand-coated nanoparticles in complex media, their reactivity, and their performance in biomedicine, photodetectors, photovoltaic devices, light-emitting devices, sensors, memory devices, thermoelec. applications, and catalysis.
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26. 26
  Chen, T.; Luo, Z.; Yao, Q.; Yeo, A. X. H.; Xie, J. Synthesis of Thiolate-Protected Au Nanoparticles Revisited: U-Shape Trend between the Size of Nanoparticles and Thiol-to-Au Ratio. Chem. Commun. 2016, 52 (61), 9522– 9525, DOI: 10.1039/C6CC04433F
  26
  synthesis of thiolate-protected Au nanoparticles revisited: U-shape trend between size of nanoparticles and thiol-to-Au ratio
  Chen, Tiankai; Luo, Zhentao; Yao, Qiaofeng; Yeo, Andrea Xin Hui; Xie, Jianping
  Chemical Communications (Cambridge, United Kingdom) (2016), 52 (61), 9522-9525CODEN: CHCOFS; ISSN:1359-7345. (Royal Society of Chemistry)
  We report a new understanding of the factors controlling the size of thiolate-protected gold nanoparticles (Au NPs): the formation and the state of a protecting layer (Au(I)-thiolate motifs) outside the gold core det. the size of Au NPs, depending on the feeding thiol-to-Au ratio. As a result, a U-shape trend is identified between the size of Au NPs and the thiol-to-Au ratio as opposed to the commonly expected decreasing trend.
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27. 27
  Frattini, A.; Pellegri, N.; Nicastro, D.; Sanctis, O. de. Effect of Amine Groups in the Synthesis of Ag Nanoparticles Using Aminosilanes. Mater. Chem. Phys. 2005, 94 (1), 148– 152, DOI: 10.1016/j.matchemphys.2005.04.023
  27
  Effect of amine groups in the synthesis of Ag nanoparticles using aminosilanes
  Frattini, A.; Pellegri, N.; Nicastro, D.; de Sanctis, O.
  Materials Chemistry and Physics (2005), 94 (1), 148-152CODEN: MCHPDR; ISSN:0254-0584. (Elsevier B.V.)
  This work presents a simple method to produce Ag nanoparticles through AgNO3 chem. redn. in a continuous media. Aminosilanes act as catalytic reactors and superficial modifiers of Ag nanoparticles, inhibiting their growth and avoiding aggregation. Nanoparticle formation was studied by UV-vis spectroscopy, AFM, and dynamic light scattering (DLS) techniques. The extent of the redn. reaction increases with either a higher aminosilane concn. or with aminosilanes with a higher no. of amine groups. The no. of amine groups in the aminosilane has also a strong effect on the size of the resulting Ag particles. The morphol. of the Ag nanoparticles obtained is spherical and the mean size is of ≈5 nm.
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28. 28
  Frenkel, A. I.; Nemzer, S.; Pister, I.; Soussan, L.; Harris, T.; Sun, Y.; Rafailovich, M. H. Size-Controlled Synthesis and Characterization of Thiol-Stabilized Gold Nanoparticles. J. Chem. Phys. 2005, 123 (18), 184701, DOI: 10.1063/1.2126666
  28
  Size-controlled synthesis and characterization of thiol-stabilized gold nanoparticles
  Frenkel, A. I.; Nemzer, S.; Pister, I.; Soussan, L.; Harris, T.; Sun, Y.; Rafailovich, M. H.
  Journal of Chemical Physics (2005), 123 (18), 184701/1-184701/6CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  Size-controlled synthesis of nanoparticles of less than a few nanometers in size is a challenge due to the spatial resoln. limit of most scattering and imaging techniques used for their structural characterization. The authors present the self-consistent anal. of the extended x-ray absorption fine-structure (EXAFS) spectroscopy data of ligand-stabilized metal nanoclusters. Method employs the coordination no. truncation and the surface-tension models to measure the av. diam. and analyze the structure of the nanoparticles. EXAFS anal. was performed on the 2 series of dodecanethiol-stabilized Au nanoparticles prepd. by 1-phase and 2-phase syntheses where the only control parameter was the Au/thiol ratio ξ, varied between 6:1 and 1:6. The 2-phase synthesis resulted in the smaller particles whose size decreased monotonically and stabilized at 16 Å when ξ was lowered <1:1. This behavior is consistent with the theor. predicted thermodn. limit obtained previously in the framework of the spherical drop model of Au nanoparticles.
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29. 29
  Lv, C.-Q.; Li, J.; Ling, K.-C.; Shang, Z.-F.; Wang, G.-C. Methylamine Decomposition on Nickel Surfaces: A Density Functional Theory Study. Surf. Sci. 2010, 604 (9), 779– 787, DOI: 10.1016/j.susc.2010.01.027
  29
  Methylamine decomposition on Ni surfaces. A density functional theory study
  Lv, Cun-Qin; Li, Jun; Ling, Kai-Cheng; Shang, Zhen-Feng; Wang, Gui-Chang
  Surface Science (2010), 604 (9-10), 779-787CODEN: SUSCAS; ISSN:0039-6028. (Elsevier B.V.)
  The adsorption and decompn. of methylamine on Ni(1 1 1), Ni(1 0 0), stepped Ni(1 1 1), and N atom modified Ni(1 0 0) (denoted N-Ni(1 0 0)) were studied with the DFT-GGA method using the periodic slab models. The initial scissions of C-H, N-H and C-N bond are considered. The adsorption energies under the most stable configurations for the possible species and the activation energies for the possible initial elementary reactions involved are obtained in the present work. Through systematic exploring of the kinetics mechanism of methylamine decompn. on these 4 surfaces, it is found that the reactivity of these surfaces decreased with the order of stepped Ni(1 1 1) > Ni(1 0 0) > Ni(1 1 1) > N-Ni(1 0 0). The reactivity is related to the openness of the surface, and the presence of N atom reduces the reactivity of the Ni(1 0 0). For the 3 reactions, the barriers decreased with the order of C-N > N-H > C-H on Ni(1 1 1) and Ni(1 0 0), whereas they decreased with the order of C-N > C-H > N-H on stepped Ni(1 1 1) and N-Ni(1 0 0).
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30. 30
  Vandevondele, J.; Krack, M.; Mohamed, F.; Parrinello, M.; Chassaing, T.; Hutter, J. Quickstep: Fast and Accurate Density Functional Calculations Using a Mixed Gaussian and Plane Waves Approach. Comput. Phys. Commun. 2005, 167 (2), 103– 128, DOI: 10.1016/j.cpc.2004.12.014
  30
  QUICKSTEP: fast and accurate density functional calculations using a mixed Gaussian and plane waves approach
  VandeVondele, Joost; Krack, Matthias; Mohamed, Fawzi; Parrinello, Michele; Chassaing, Thomas; Hutter, Juerg
  Computer Physics Communications (2005), 167 (2), 103-128CODEN: CPHCBZ; ISSN:0010-4655. (Elsevier B.V.)
  We present the Gaussian and plane waves (GPW) method and its implementation in which is part of the freely available program package CP2K. The GPW method allows for accurate d. functional calcns. in gas and condensed phases and can be effectively used for mol. dynamics simulations. We show how derivs. of the GPW energy functional, namely ionic forces and the Kohn-Sham matrix, can be computed in a consistent way. The computational cost of computing the total energy and the Kohn-Sham matrix is scaling linearly with the system size, even for condensed phase systems of just a few tens of atoms. The efficiency of the method allows for the use of large Gaussian basis sets for systems up to 3000 atoms, and we illustrate the accuracy of the method for various basis sets in gas and condensed phases. Agreement with basis set free calcns. for single mols. and plane wave based calcns. in the condensed phase is excellent. Wave function optimization with the orbital transformation technique leads to good parallel performance, and outperforms traditional diagonalisation methods. Energy conserving Born-Oppenheimer dynamics can be performed, and a highly efficient scheme is obtained using an extrapolation of the d. matrix. We illustrate these findings with calcns. using commodity PCs as well as supercomputers.
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31. 31
  Grimme, S. Semiempirical GGA-Type Density Functional Constructed with a Long-Range Dispersion Correction. J. Comput. Chem. 2006, 27 (15), 1787– 1799, DOI: 10.1002/jcc.20495
  31
  Semiempirical GGA-type density functional constructed with a long-range dispersion correction
  Grimme, Stefan
  Journal of Computational Chemistry (2006), 27 (15), 1787-1799CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
  A new d. functional (DF) of the generalized gradient approxn. (GGA) type for general chem. applications termed B97-D is proposed. It is based on Becke's power-series ansatz from 1997 and is explicitly parameterized by including damped atom-pairwise dispersion corrections of the form C6·R-6. A general computational scheme for the parameters used in this correction has been established and parameters for elements up to xenon and a scaling factor for the dispersion part for several common d. functionals (BLYP, PBE, TPSS, B3LYP) are reported. The new functional is tested in comparison with other GGAs and the B3LYP hybrid functional on std. thermochem. benchmark sets, for 40 noncovalently bound complexes, including large stacked arom. mols. and group II element clusters, and for the computation of mol. geometries. Further cross-validation tests were performed for organometallic reactions and other difficult problems for std. functionals. In summary, it is found that B97-D belongs to one of the most accurate general purpose GGAs, reaching, for example for the G97/2 set of heat of formations, a mean abs. deviation of only 3.8 kcal mol-1. The performance for noncovalently bound systems including many pure van der Waals complexes is exceptionally good, reaching on the av. CCSD(T) accuracy. The basic strategy in the development to restrict the d. functional description to shorter electron correlation lengths scales and to describe situations with medium to large interat. distances by damped C6·R-6 terms seems to be very successful, as demonstrated for some notoriously difficult reactions. As an example, for the isomerization of larger branched to linear alkanes, B97-D is the only DF available that yields the right sign for the energy difference. From a practical point of view, the new functional seems to be quite robust and it is thus suggested as an efficient and accurate quantum chem. method for large systems where dispersion forces are of general importance.
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32. 32
  VandeVondele, J.; Hutter, J. Gaussian Basis Sets for Accurate Calculations on Molecular Systems in Gas and Condensed Phases. J. Chem. Phys. 2007, 127 (11), 114105, DOI: 10.1063/1.2770708
  32
  Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases
  VandeVondele, Joost; Hutter, Jurg
  Journal of Chemical Physics (2007), 127 (11), 114105/1-114105/9CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
  We present a library of Gaussian basis sets that has been specifically optimized to perform accurate mol. calcns. based on d. functional theory. It targets a wide range of chem. environments, including the gas phase, interfaces, and the condensed phase. These generally contracted basis sets, which include diffuse primitives, are obtained minimizing a linear combination of the total energy and the condition no. of the overlap matrix for a set of mols. with respect to the exponents and contraction coeffs. of the full basis. Typically, for a given accuracy in the total energy, significantly fewer basis functions are needed in this scheme than in the usual split valence scheme, leading to a speedup for systems where the computational cost is dominated by diagonalization. More importantly, binding energies of hydrogen bonded complexes are of similar quality as the ones obtained with augmented basis sets, i.e., have a small (down to 0.2 kcal/mol) basis set superposition error, and the monomers have dipoles within 0.1 D of the basis set limit. However, contrary to typical augmented basis sets, there are no near linear dependencies in the basis, so that the overlap matrix is always well conditioned, also, in the condensed phase. The basis can therefore be used in first principles mol. dynamics simulations and is well suited for linear scaling calcns.
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33. 33
  Mentel, L. M. Mendeleev - A Python Resource for Properties of Chemical Elements, Ions and Isotopes, 2014.
  There is no corresponding record for this reference.
34. 34
  Clementi, E.; Raimondi, D. L.; Reinhardt, W. P. Atomic Screening Constants from SCF Functions. II. Atoms with 37 to 86 Electrons. J. Chem. Phys. 1967, 47 (4), 1300– 1307, DOI: 10.1063/1.1712084
  34
  Atomic screening constants from S.C.F. [self-con-sistent field] functions. II. Atoms with 37 to 86 electrons
  Clementi, Enrico; Raimondi, Donald L.; Reinhardt, W. P.
  Journal of Chemical Physics (1967), 47 (4), 1300-7CODEN: JCPSA6; ISSN:0021-9606.
  cf. CA 59: 118f. Minimal basis-set at. functions for the ground-state atoms from Rb (Z = 37) to Rn(Z = 86) are presented. These functions are analyzed in order to obtain systematic data for the screening consts. and at. radii following the work initiated by Slater.
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35. 35
  Ruban, A.; Hammer, B.; Stoltze, P.; Skriver, H. L.; Nørskov, J. K. Surface Electronic Structure and Reactivity of Transition and Noble Metals1Communication Presented at the First Francqui Colloquium, Brussels, 19–20 February 1996.1. J. Mol. Catal. A Chem. 1997, 115 (3), 421– 429, DOI: 10.1016/S1381-1169(96)00348-2
  35
  Surface electronic structure and reactivity of transition and noble metals
  Ruban, A.; Hammer, B.; Stoltze, P.; Skriver, H. L.; Norskov, J. K.
  Journal of Molecular Catalysis A: Chemical (1997), 115 (3), 421-429CODEN: JMCCF2; ISSN:1381-1169. (Elsevier)
  A review with 38 refs.; we present self-consistent d. functional calcns. using the LMTO-ASA method of the variations in the surface electronic structure for pseudomorphic overlayers and impurities of Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, and Au on the other metals. Knowledge of these variations is of importance in understanding trends in the reactivity of metal surfaces. A simple model is presented which gives a description of the overall trends in the self-consistently calcd. results.
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36. 36
  Gregorutti, B.; Michel, B.; Saint-Pierre, P. Correlation and Variable Importance in Random Forests. Stat Comput 2017, 27 (3), 659– 678, DOI: 10.1007/s11222-016-9646-1
  There is no corresponding record for this reference.
37. 37
  Hornung, R.; Boulesteix, A.-L. Interaction Forests: Identifying and Exploiting Interpretable Quantitative and Qualitative Interaction Effects. Comput. Stat Data Anal 2022, 171, 107460, DOI: 10.1016/j.csda.2022.107460
  There is no corresponding record for this reference.
38. 38
  Alsmadi, M. k.; Omar, K. B.; Noah, S. A.; Almarashdah, I. Performance Comparison of Multi-Layer Perceptron (Back Propagation, Delta Rule and Perceptron) Algorithms in Neural Networks. In 2009 IEEE International Advance Computing Conference ; 2009; pp 296– 299.
  There is no corresponding record for this reference.
39. 39
  Vovk, V. Kernel Ridge Regression. In Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik; Schölkopf, B., Luo, Z., Vovk, V., Eds.; Springer: Berlin, 2013; pp 105– 116.
  There is no corresponding record for this reference.
40. 40
  Awad, M.; Khanna, R. Support Vector Regression 2015, 67– 80, DOI: 10.1007/978-1-4302-5990-9_4
  There is no corresponding record for this reference.
41. 41
  Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5– 32
  There is no corresponding record for this reference.
42. 42
  Chen, T.; He, T.; Benesty, M.; Khotilovich, V.; Tang, Y.; Cho, H.; Chen, K. Xgboost: Extreme Gradient Boosting. R package version 0.4-2 2015, 1 (4), 1– 4
  There is no corresponding record for this reference.
43. 43
  Paper, D. Scikit-Learn Classifier Tuning from Simple Training Sets. In Hands-on Scikit-Learn for Machine Learning Applications; Apress: Berkeley, CA, 2020; pp 137– 163.
  There is no corresponding record for this reference.
44. 44
  Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V. Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research 2011, 12, 2825– 2830
  There is no corresponding record for this reference.
45. 45
  Kumar, V. Segregation at Alloy Surfaces. Surf. Sci. 1979, 84 (1), L231– L234, DOI: 10.1016/0039-6028(79)90294-2
  45
  Segregation at alloy surfaces
  Kumar, Vijay
  Surface Science (1979), 84 (1), L231-L234CODEN: SUSCAS; ISSN:0039-6028.
  A theory is presented for segregation at alloy surfaces, where both the bond breaking and strain theories have been combined into one. The formulation can be applied to alloys of any concn. In addn., some ideas are described which should be considered while dealing with polycryst. samples. Good agreement is obtained with AES data on a Ni-Au system.
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46. 46
  Dean, J.; Taylor, M. G.; Mpourmpakis, G. Unfolding Adsorption on Metal Nanoparticles: Connecting Stability with Catalysis. Sci. Adv. 2019, 5 (9), eaax5101 DOI: 10.1126/sciadv.aax5101
  There is no corresponding record for this reference.
Supporting Information
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcc.3c05827.
- Calculation details of ΔBE/CN_ads term; DFT calculated bulk cohesive energy (CE_bulk); binding energy of the ligands on SAA surfaces; assessing multicollinearity using variance inflation factor; descriptors used in the feature importance analysis; extended variable importance plot; tuned hyperparameters used in regression models; parity plots of the predicted vs calculated E_seg using different regression models; MAE and RMSE scores of train, validation, and test sets of different regression models; bootstrapping analysis and related MAE scores; experimental observations from the literature against NN MLP E_seg predictions; architecture of the NN MLP; DFT electronic energy of single metal atoms, ligands, a single atom bonded to ligands, and the binding energy of the latter (PDF)
- Optimized surfaces from DFT (ZIP)
- jp3c05827_si_001.pdf (1.6 MB)
- jp3c05827_si_002.zip (806.05 kb)
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Single Atom Alloys Segregation in the Presence of Ligands
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