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Assessment of Catalytic Activities of Gold Nanoclusters with Simple Structure Descriptors Haoxiang Xu, Daojian Cheng, Yi Gao, and Xiao Cheng Zeng ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.8b02423 • Publication Date (Web): 11 Sep 2018 Downloaded from http://pubs.acs.org on September 11, 2018
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ACS Catalysis
Assessment of Catalytic Activities of Gold Nanoclusters with Simple Structure Descriptors Haoxiang Xua, Daojian Chenga*, Yi Gaob,*, and Xiao Cheng Zenga,c* a
Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State
Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing100029, China E-mail:
[email protected] b
Division of Interfacial Water and Key Laboratory of Interfacial Physics and
Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China E-mail:
[email protected] c
Department of Chemistry and Department Biomolecular & Chemical Engineering, University of Nebraska, Lincoln, NE 68588, USA E-mail:
[email protected] 1
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Abstract: De novo design of nanocatalysts with high activity is a challenge task since prediction of catalytic activities of nanoclusters based on simple descriptors is still a frontier
of
research.
Herein,
we
present
a
simple
model
to
build
a
geometry-adsorption-activity relationship for the gold nanoclusters using CO oxidation as the benchmark probe. Based on the extensive density functional theory calculations, the geometry indices (generalized local coordination number and curvature angle of the surface Au atoms) of numerous Au nanoclusters are found to be well correlated with the binding strength of CO and O2, as well as the activation barriers of CO oxidation by using Brønsted-Evans-Polanyi (BEP) relationship and the Sabatier analysis. In particular, this predictive model with simple structure descriptors can be extended to Au nanoparticles (NPs) with larger sizes and various shapes. Such predictive model can provide a useful rule of thumb for experimentalists to quickly assess catalytic activity from only gathering the structural characteristics of Au NPs before performing more involved catalytic measurement. This model may also offer cost-effective way for rational design of nanocatalysts, for example, to assist experimentalists in making Au nanoclusters with maximum active sites.
KEYWORDS: Au nanoparicles, CO oxidation, geometry descriptor, density functional theory, activity prediction, catalyst design
2
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ACS Catalysis
Introduction Issues such as high costs with moderate efficiency still hinder the widespread implementation of the nanocatalyst-based technologies, e.g., fuel cells, carbon-neutral processes and electrolyzers. The low-temperature oxidation of CO and hydrocarbons catalyzed by gold nanoparticles (Au NPs), for example, has been widely investigated in fuel cells and other industrial processes1-11. However, how to identify the most active sites for Au nanoparticles of different sizes and shapes is still not fully resolved, thus impeding the rational design of special gold nanocatalysts with the maximum number of active sites. Hence, it is timely to devise a simple structure-activity relationship that can benefit experimentalists to quickly assess the catalytic activity on a given catalytic center by using a simple and robust model before performing much involved catalytic measurements. Recently, volcano-type activity plots derived based on the Sabatier principle have been applied for the rational design of Au nanocatalysts for CO oxidation by analyzing trends of catalytic activity. The surface adsorption energies of CO and O2 are shown to be correlated with estimated catalytic activity via the volcano plots12-14. However, Sabatier analysis alone cannot provide guidelines for finding optimal active sites on Au NPs, since the Sabatier analysis merely provides a correlation between adsorption energy and activation energy. In other words, even if the volcano-type activity plots can illustrate optimal energetic properties, it is still difficult to identify the specific geometry index of active sites that give rise to optimal adsorption energies of CO and O2. From a theoretical perspective, a simple and effective structure descriptor with sound physical-chemical foundations and reliable predictive capability is still absent for describing geometry adsorption relationship. Several theoretical models have provided atomic-level insight into how different geometric structure can affect the binding strength of adsorbates. The most successful example of such descriptor is perhaps the d-band center, which is correlated with the adsorption energy of CO on metal surface15. Nevertheless, quantitative computation of
the
d-band
center
requires
the
use
of
computationally
demanding
density-functional-theory (DFT) calculations for every new case under investigation. 3
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The exact d-band energy center for metal NPs cannot be easily measured experimentally. Moreover, the notion of d-band center may not work so well for the subnanometer catalysts16-18. Another example is orbital roughness rules introduced by Horia Metiu, which demonstrates that shape of LUMO (lowest unoccupied molecular orbital) of Au clusters regulates the adsorption site and the adsorption energy of adsorbates19. Besides, recently, a scaling relation between the generalized ̅̅̅̅) of the surface sites and adsorption energies of oxygencoordination numbers (CN and hydrogen-containing adsorbates have been identified for Pt nanoparticles18, 20. However, whether the notion of ̅̅̅̅ CN can serve as a simple and effective structure ̅̅̅̅ does not include the descriptor for other metals is still an open question. Besides, CN information of curvature angle of the surface sites of Au NPs, which has been previously shown as another crucial parameter for correlating with site activity21, 22. Thus, a practical and effective structure descriptor that can quantify the local geometry difference among the corner, edge and facet sites of Au NPs would be highly desirable, while its variation can be quantitatively linked to changes in adsorption energies and catalytic activities. In this article, we show a systematic study of site-dependent adsorption energies and reaction barriers for the CO oxidation on the freestanding and supported Au nanoclusters, respectively. It is known that the reducible metal oxides tend to transfer electrons to Au NPs2, 23-25. Thus, we use freestanding anionic Au nanoclusters as simple model systems to mimic supported Au nanoclusters on reduced metal oxides. In addition, to investigate the (non-reducible) support effect, MgO-supported Au32 hollow cage26 was also taken into account. Based on the Loẅdin charge analysis on Au32 hollow cage supported on MgO, Au32 cage is negatively charged with one electron from the MgO slab. To indirectly account for the charge transfer effects on the adsorption strength and catalytic activity of supported nanoclusters, free-standing clusters in this study are charged with one electron. Importantly, we build a geometry-adsorption-activity relationship based on the geometry indices (generalized coordination number and curvature angle of the surface Au atoms), BEP relationship and Sabatier analysis. Such a relationship can quantify structure sensitivity, that is, a 4
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slight change of the surface structure may have notable impact on the adsorption properties and catalytic activity. Based on the newly derived relationship, we predict adsorption energies and catalytic activity for larger-sized Au NPs (0.5 nm-3.5 nm) with various shapes. Comparative computation indicates reliability of the newly derived geometry-adsorption-activity relationship, which can be exploited for guidance of future catalytic experiments.
Computational Details DFT calculations are performed using the PWSCF (Plane-Wave Self-Consistent Field) code in the Quantum ESPRESSO package27. All the calculations are carried out using the spin-polarized Perdew–Burke–Ernzerhof (PBE) xc-functional28 with ultrasoft pseudopotentials29. The regular MgO (100) surface is modeled with a two-layer slab. Each layer contains 36 Mg and 36 O atoms (6×6cell), fixed during structural optimization at the equilibrium lattice positions (with lattice parameter setting as the experimental value of 4.208 Å). The periodically replicated surfaces are separated along the (100) direction by a vacuum region of 20 Å. All the atoms of the Au32-cage and reactant are allowed to relax. Because of the large super cells, a gamma k-point sampling of the Brillouin zone is chosen. All calculations on cluster models are performed in a cubic supercell with appropriate side length to make sure that a large vacuum slab of 10 Å is inserted in three directions for cluster isolation to prevent interaction between neighboring image clusters. The Brillouin zone is sampled at the gamma point. The kinetic energy cutoffs are fixed to be 40 Ry and 400 Ry, respectively, for the wave function and electronic density. The convergence threshold for the iteration in self-consistent-field (SCF) is set to be 10-6 Ry, and the geometry optimizations by using the BFGS algorithm are stopped when the maximum force on the atoms is less than 10-3 Ry/Bohr. A Gaussian smearing procedure (with a smearing parameter of 0.002 Ry) is applied. The adsorption energies are defined as Eads = Eadsorbate+cluster-Ecluster-Eadsorbate, where Eadsorbate+cluster is the total energy of the composite cluster + absorbate system, Ecluster is the energy of the freestanding cluster, and Eadsorbate is the energy of an isolated 5
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molecule. The climbing-image nudged elastic band (CI-NEB) method30 is used to determine reaction paths for CO oxidation. Through vibrational frequency analyses, all transition states have been verified by ensuring that the transition state has only one significant imaginary vibrational frequency. All linear relationships are obtained by least-squares fits and related statistics are also given. Results and Discussion Figure 1 shows the configurations for the supported Au32 hollow cage, anionic freestanding Au16-~Au18-, Au27-, Au28-, Au30-, Au32-, Au33-, Au35-, Au38-, Au44-, Au46-, Au47-, and Au49- clusters. The global-minimum atomic structures of these anionic clusters have been determined through combination of anion photoelectron spectroscopy experiment and density functional theory calculation31, 32. Geometry-Adsorption Relationship. The adsorption energies of CO and O2 on supported Au32-cage, freestanding anionic Au NPs, and extended surface of gold (Figure S1) are listed in Tables S1-S3. Based on previous works on Au NPs, several descriptors have been proposed to correlate with the adsorption ability of surface sites. For instance, coordination numbers have been used as an approximation to describe the electronic environment of an atom. For a gold crystal, the maximum coordination is obtained in the bulk, where twelve nearest neighbors surround each atom. Atoms with coordination numbers below twelve have tendencies towards the formation of bonds to compensate the reduced coordination. Therefore, trends in binding strength for adsorbates on extended transition metal surfaces are well described by the traditional coordination number (cn) of the adsorption sites. In this work, the cutoff of bond length is defined to be 3.3 Å in order to count the coordination number. However, as seen in Figure S2a and S2b, a widespread in the CO and O2 adsorption energies are observed at the same cn. This spread indicates that cn loses its accuracy when nanoparticles are considered and cannot be an accurate structure descriptor for correlating with adsorption energy on Au NPs due to the “finite-size effects”. So more sophisticated structure descriptors are needed. ̅̅̅̅) as descriptors: We next choose the generalized coordination number (CN 6
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𝒏𝒊
̅̅̅̅(𝐢) = ∑ 𝐜𝐧(𝐣)𝐧𝐣 /𝐜𝐧𝐦𝐚𝐱 𝐂𝐍 𝐣=𝟏
̅̅̅̅) of an atom i with ni nearest To estimate the generalized coordination number (CN neighbors, the neighbors are weighted by their own usual coordination numbers. Therefore, every neighbor of atom i is accounted for with a weight of nj/cnmax (cnmax=12 for an fcc crystal). For instance, site 1 of Au32 cage supported on MgO has 5 nearest neighbors, one of which has cn = 5, four have cn = 6. Thus, ̅̅̅̅ CN(1) = (1×5 + 4×6 )/12 =2.42. The detailed values of ̅̅̅̅ CN are given in Tables S1-S3. We find that ̅̅̅̅ via formula (1，2) for ̅̅̅̅ Ead(CO) and Ead(O2) can be correlated with CN CN ≥ 4. ̅̅̅̅ - 1.38 (1) Ead(CO) = 0.14 * 𝐂𝐍
̅̅̅̅≥4) (𝐂𝐍
(2) Ead(O2) = 0.15 * ̅̅̅̅ 𝐂𝐍 - 0.93
̅̅̅̅≥4). (𝐂𝐍
The linear relationships between ̅̅̅̅ CN and adsorption energies of CO and O2 are shown in Figure 2a and b, suggesting that a decrease in ̅̅̅̅ CN corresponds to an increase in binding strength, as intuitively expected. However, no linear correlation ̅̅̅̅ and adsorption energy is found for CN ̅̅̅̅ 0.7, which suggests that ̅̅̅̅ CN cannot be used to reflect the structure change and, accordingly, cannot quantify the structure effect on adsorption and activity for ̅̅̅̅ < 4 and CAr > 0.7. This is a reason why we choose the relative curvature angle as CN another geometric indicator. Figure 2c shows that CO adsorption energy is monotone increasing function of CAr of the Au atoms, and the data is fitted by linear regression. Ead(CO) can be correlated with CAr via the formula (3): (3)
Ead(CO)=1.55*𝐂𝐀 𝐫 -1.92
̅̅̅̅
