To acquire a crystal-clear structure-performance relation for heterogeneous nanocatalysts, precise tracking of the structural evolution from the precursor to final product in the real time and space domain is important but also extremely challenging. In archetype preparation, thermal treatment is a crucial step for activation. Especially, for colloidally synthesized nanoparticles, the ligand detachment process to expose surfaces is completed with thermal treatment. Here, we track the three-dimensional atomic structural change of ligand-protected platinum nanoparticles under ligand detachment and thermal annealing processes. The structural transformation with time is shown by experimental decoupling and tracking of each atomic coordinate through molecular dynamics simulations with machine-learning potentials. Acquisition of structural data is integrated with a computational catalysis framework to identify a structure-activity correlation in the CO oxidation reaction. We find that colloidally synthesized platinum nanoparticles create unique atomic tensile strain and that varying the particle size tunes the ensemble effect of active sites.
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Tracking the 3D atomic structures during thermal treatment and catalytic activity of individual Pt nanoparticles
https://doi.org/10.21203/rs.3.rs-1441062/v1
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Heterogenous catalysis has been widely utilized in the majority of industrial processes to sustain human society, and nanoparticles (NPs) hold key roles in the success1,2. In modern society, NP catalysts are specifically targeted at efficiently producing green renewable fuels and powering electric vehicles to avoid environmental degradation induced by carbon-based energy systems. They are fabricated through manyfold reactions and post-processes before reaching their final form. This fact implies that the structures of NPs dynamically fluctuate in the chemical steps and are not static properties. More specifically, understanding the structural evolution of NP catalysts over various reaction steps and post-processes is essential, in which the thermal condition and reaction medium can be different from those for the precursor state. Typically, NP catalysts anchored on support materials are activated by thermal treatment to expose desired nanofacets and realize stability and quantum effects3–5, whereby substantial deviation from the initial structures is introduced. Thus, regulating and tracking the structural evolution in the real time and space domain to extract a precise structure-activity map is important.
A colloidal method to prepare NPs as precursors overlaying support materials is emerging as a promising approach for functional catalyst design6–8. This is because effective control of complex parameters, such as the size and shape of NPs and spatial distribution on the support, is easier than with conventional methods. Moreover, the structural ensemble effect of NPs is easily engineered to facilitate the catalytic performance9–11. A key step in colloidal synthesis of NPs towards efficient heterogeneous catalysts is to expose desired surfaces for access by reaction intermediates. This is accomplished by ligand removal from supported colloidal nanocatalysts, e.g., via UV-ozone treatment12, a solvent washing step13,14, an electrochemical step, and fast thermal treatment4. Then, typically, the exposed surfaces are activated by thermal annealing6,15, whereby atomic positions are rearranged due to the high thermodynamic driving force. The structures produced by the processes show substantial heterogeneity in atomic coordination environments or atomic strain16. The nonuniform structures in both single and ensemble NPs can influence the catalytic performance. Due to the dynamic nature of the surface atoms during the ligand removal process and thermal treatment, identifying the activated surface structure and a clear correlation with the catalytic activity is extremely challenging.
The atomic-level structure of an NP can be determined by several microscopy and spectroscopy techniques. For instance, fast characterization of active sites in platinum (Pt) NPs for carbon monoxide (CO) oxidation by infrared spectroscopy was demonstrated17, and in situ field electron microscopy18 was used to resolve the individual site reactivity for hydrogen oxidation. Our recent studies19,20 demonstrated precise resolution of the three-dimensional (3D) atomic structure of colloidally synthesized Pt NPs in solution. Despite all these developments, experimental approaches have several limitations in directly resolving the structural evolution of catalytic NPs and in determining the structure-performance relationship at the atomic level. First-principles-based computational modelling, such as density functional theory (DFT) calculations, can be a good tool for this purpose. It can be effective for decoupling individual variables influencing catalytic reaction energetics, i.e., ensemble (geometric), ligand (electronic), and strain effects21–23. However, most results from first-principles methods largely rely on ideal structural models, e.g., uniform surfaces or magic-number NPs, neglecting the effects of surface nonuniformity and structural evolution in the reactive environments surrounding the NP catalysts.
In this work, we revealed the 3D atomic structural evolution of Pt NPs during the ligand detachment and subsequent thermal annealing process. The 3D atomic structures of the synthesized ligand-protected Pt NPs in solution were experimentally obtained with a 20 pm precision19 and used as input for machine-learning-accelerated large-scale molecular dynamics (MD) simulations that directly elucidated atomistic behaviour of single NPs in the thermal annealing step. We demonstrate that the thermal energy drives NP structures to minimize the surface free energy. In addition, using the CO oxidation reaction, we acquired individual site contributions to the catalytic activity by revealing the kinetic coupling of adsorbates over heterogeneous atomic sites, which could not be captured by ideal modelling. Our results identify a clear correlation between individual atomic sites in the activated 3D surfaces and activity for the CO oxidation reaction.
Structural evolution in the ligand detachment process.
The overall experimental procedure is illustrated in Fig. 1a. Starting with the experimentally resolved 3D atomic structures of ligand-protected Pt NPs in liquid solution19, MD simulations for the ligand detachment process followed by fast thermal annealing were performed for individual Pt NPs with varying sizes (NP1: 2.42 nm, NP2: 2.52 nm, NP3: 2.66 nm, and NP4: 2.92 nm). Tracking of the 3D atomic coordinates was carried out over the whole process to analyse the structural changes. Then, key features of the structures, i.e., the generalized coordination number (GCN)24 and strain, were fed into kinetic Monte Carlo (kMC) simulations of the CO oxidation reaction. Last, the turnover frequency of individual particles was calculated to investigate the per-site activity.
One of the main bottlenecks in simulating the structure-property relation of the ligand detachment process in Pt NPs is a careful evaluation of the structural fluctuations. We addressed the issue by integrating a machine-learning neural network potential (NNP) with first-principles data of atomic energy to enable accurate and fast MD simulations. Various strategies have been applied to efficiently construct the NNP such as data augmentation25 and active learning26. Our strategy is innovative in that it involves efficient construction of first-principles data by incorporating the experimentally reconstructed atomic configurations and defective NPs to describe various local atomic environments (Fig. 1b).
We generated local structural heterogeneity by using structures of experimentally resolved Pt NPs and highly defective Pt NPs at various intermediate steps acquired with first-principles calculations. To estimate the reliability of our approach, we compared the local atomic environments in ideal models and experimentally reconstructed structures (Fig. 1c). The ideal magic-number cuboctahedron consisting of 309 atoms shows only specific atomic environments, represented by the values of the radial (G2) and angular (G4) functions, with duplicated information (essentially core atoms), and this issue is still unresolved even with the addition of model structures. In contrast, the structural heterogeneity in the twenty structures of experimentally reconstructed Pt NPs provides much more densely mapped local configurations, which are fed into the feedforward neural network. Thus, we utilized the accelerated data collection method to make a training set for the NNP. Details of the first-principles data generation and the NNP training and validation results are provided in Supplementary Sections 1 and 2.
The structural evolution of individual Pt NPs during the ligand detachment process and thermal annealing was investigated via MD simulations with the constructed NNP. As shown in Fig. 1d, the total simulation time was 300 ps, in which the initial 100 ps corresponded to the thermal treatment with the ligand detachment process at 773 K, the next 100 ps to the quenching process to 303 K with a quenching speed of 4.7 K per ps, and the final 100 ps to equilibration at 303 K. The total energy of each NP becomes thermodynamically more stable as it undergoes the quenching process. We also observed that the larger the particle size is, the more stable the particle becomes (Fig. S5)27,28. The structural changes in NP3, representatively, show qualitative insights into the reduction of surface adatoms to form close-packed surfaces (Fig. 1d). The lattice parameters and averaged strain after the ligand detachment process indicate that the particle-wise structural heterogeneity decreases as Pt NPs approach the thermodynamic energy minima (Fig. S6a). However, the increase in the error magnitude reflects the flattened distributions of individual coordinates, which presumably signifies the diversified values of individual sites after the thermal treatment.
The temporal changes in the 3D atomic coordinates of NP3 obtained using MD simulations were further investigated. Figure 2a shows the changes in the fractions of the coordination number (CN), which is an index to evaluate the local atomic environments. We divided the atomic local environments into four classes in accordance with the CN29: CN = 12, CN = 10–11, CN = 9 − 7, and CN ≤ 6. Each environment shows different behaviour with elapsed time, especially in the initial stage. The fractions of defective cores or subsurfaces of edges (CN = 10–11) do not fluctuate much, while other sites drastically change in the initial stage. The drastic change in the initial stage is mainly attributed to the ligand being instantly removed at the beginning of the simulation. The fraction of atoms with CN = 12, representing the face-centered cubic (fcc) lattice of the bulk, and that with CN ≤ 6, representing high-Miler-index facets, vertices, edges, or irregular islands of adatoms, decreased, whereas the fraction of atoms with CN = 9 − 7, representing (111) or (110) facets, increased. After the detachment process, the fcc lattice sites gradually recovered during the thermal annealing process. The surface atoms (CN ≤ 9) evolve in a way to minimize the surface energy, forming truncated octahedrons with (111) and (110) facets. The results for other Pt NPs were similar in that the number of surface adatoms initially binding ligands was reduced. The thermodynamic driving force of surface minimization can also be confirmed by the increase in the terrace (111) surface areas (Fig. 2b).
Local atomic arrangements were also analysed by the Voronoi tessellation method30. As shown in Fig. 2c, the distributions of the 25 most abundant Voronoi polyhedra (VPs) before and after the thermal treatments were obtained based on the Voronoi index < n3, n4, n5, n6>, where ni denotes the number of i-edged polygons. Before the treatment, most of the VPs are < 0, 0, 12, 0 > regular icosahedrons along with < 0, 11, 0, 0 > defective icosahedrons and < 0, 9, 0, 1 > polyhedral with multitetragonal surfaces. The major peaks for VPs before the treatment clearly indicate the single crystallinity of NP3. However, the crystallinity significantly decreases after the treatment (e.g., from 287 to 165 counts for < 0, 0, 12, 0>). Additionally, many VPs with low-edged polygons (n3) appear, which indicates defective local atomic configurations. The defect identification by Wigner-Seitz analysis shows that all defects are on the surface (Fig. 2d).
As shown in Fig. 2e, the interatomic distances along the < 111>, < 100>, and < 110 > directions imply that the deviation increases as the atomic site moves away from the core of the NP, which is similar to the results of a previous study19. The distributions before and after the thermal treatment demonstrate that the interatomic distances along the < 111 > and < 110 > directions are rather similar (Fig. 2f). The distribution along the < 100 > direction at 300 ps with two peaks substantiates the surface energy difference among the (111), (110), and (100) surfaces in that adatoms preferentially penetrated into the (100) surface with the least stability and the smallest atomic packing factor. Our structural analyses consistently express the structural rearrangement of Pt NPs to minimize the surface energy induced by thermal energy during the ligand detachment and thermal treatment processes. The core crystallinity is mostly preserved, while surface reconstruction is unavoidable affecting the catalytic performance.
Structure-property relation with thermal treatment.
The chemical reaction energy in a single-component system is largely determined by structural features such as the local atomic coordination environment and strain31,32. Thus, we examined the GCN and atomic strain (Supplementary Section 3) at individual atomic sites of Pt NPs. The thermodynamic driving force for surface energy minimization also induced drastic changes in both GCN and strain (Fig. S7 and S8). Initially, all Pt NPs exhibited structural heterogeneity, especially surface atoms29.
As the simulation time elapses, the density peaks for GCNs in the range of 4 to 6 increase, but those for high GCNs decrease. The increase in the density peaks for lower GCNs during the ligand detachment process implies the formation of, e.g., (110) or higher Miller index, facets along with edges and vertices with GCN values in the range of 5.4 to 6.6, and 5.4 or lower, respectively. The vanishing peaks for very low GCNs indicate the penetration of adatoms into the surface lattice sites. The decrease in peaks for higher GCNs, similar to the CN results, signifies the reduction of core crystallinity, mainly due to the high processing temperature (773 K) of the ligand detachment process. The strain distributions of Pt NPs at the beginning also show the heterogeneity, with less correlation with the size after the process, in which NP2 has the lowest deviation of strain from equilibrium (Fig. S6b). One thing to note is that initially imposed tensile strains were preserved over the process, implying that colloidal methods can be beneficial for catalytic activity through an upshift of the d-band centre energy to enhance the binding affinity33.
The structural features of the atomic sites were further analysed by decoupling the surface and core atoms (Fig. 3). Since catalytic reactions occur at the surface, the structural features of surface atoms are of interest. As shown in Fig. 3a, distinctive surface atom peaks (4.5 to 5 and 5.5 to 6 for edge atoms and high-index facets, respectively) are dominant for all Pt NPs, but the peak intensities vary. The core crystallinity is more preserved for larger-sized NPs, with higher peaks for GCNs in the range of 11 to 12. For all NPs, (111) facet formation (6.6 < GCN < 7.5) is clearly observed based on the colour-coded analysis, but some close- packed facets showed smaller GCNs. This is mainly attributed to the adatoms in the interstitial fcc lattice sites, which induced a void space between the surface and core. In contrast, the strain distributions of surface atoms show no distinctive peaks for all NPs (Fig. 3b). The major strain distributions initially resided in the range of 0 to 2 for all Pt NPs, but surface minimization effects resulted in high strain values (greater than \(\pm\)10 ) after the ligand detachment process. Colour-coded results also represent the predominant tensile strain over the surface atoms.
The change in average bond length was also computed as shown in Fig. S9. Core atoms suffer bond elongation, such as tensile atomic strain, but surface atoms suffer overall bond contraction. This is because the nearest neighbours of surface atoms consist of atoms in the same surface with a higher fraction (0.66) and atoms in the subsurface with a lower fraction (0.33) in general. Overall, the change in the structural features of individual Pt NPs after the ligand detachment process shows distinctive characteristics in that the ensemble of individual sites is magnified in terms of the GCN, while it is reduced in terms of the strain.
Individual site contributions to catalytic activity.
To investigate the ensemble effects of active sites on catalytic reactivity, we constructed microkinetic modelling and performed kMC simulations for CO oxidation in Pt NPs as a prototype catalysis process34,35. Overall reaction energetics were adopted from the previous literature16,36, but strain effects on the adsorption energy were revised by assigning individual atomic strain and site-dependent perturbations. Details of the reaction energetics and the kMC simulation are described in Supplementary Section 4.
Figure 4a shows the Arrhenius-type plot of the temperature-dependent turnover frequency (TOF) for different size Pt NPs. In general, the catalytic activity increases as the particle size increases, which is in accordance with a previous report34. Notably, such behaviour were not seen for modelled NPs35. To understand the peculiar size-dependent activity trend, we computed the fraction of surface atoms with respect to the total number of atoms and the fractions of active and inactive sites with respect to the number of surface atoms (Fig. 4b and Fig. S11). The surface atom fraction decreases with increasing particle size due to the reduction in the surface-to-volume ratio. The ensemble of active sites (TOF > 5·104 s− 1 site− 1) is directly correlated with the activity of the particle, and the low fraction of inactive sites for larger particles indicates less kinetic hindrance. Interestingly, NP2 has a higher TOF than NP3 despite its smaller size.
The activities of ideal NPs (561 and 807 atoms) with similar sizes to NP1 to NP4 were computed. The results show that the NPs with high density peaks for the GCN have higher activity due to the ensemble effects of individual sites (Fig. S12 and Fig. S13). To correlate the results of ideal NPs with those from NP1 to NP4, we compared the deviations in the number of atoms for each NP from that for the ideal NP models with (100) and (111) facets (Fig. S14). NP2 has a marginal deviation (0.01), while NP3 has a much higher deviation (0.10). We speculate that the extent of deviation can be a crucial factor; the closer the number of atoms of an NP is to that of the ideal model, the easier the NP can optimize towards the global minimum for facet formation. To summarize, the catalytic activity of NPs is dominated by the coupled effects of particle size and active site formation, increasing the individual site assembly.
To uncover how much different types of sites contribute to the total activity, we further analysed the TOF of Pt NPs through individual site analysis. First, we evaluated the effects of the GCN, as shown in Fig. 5a. In general, GCNs ranging from 4.8 to 5.5 yield high TOF, which indicates that the edge sites are the most reactive. In addition to the GCN, we considered the strain effect on the TOF of individual sites (Fig. 5b). We plotted the individual sites with TOF > 104 s− 1 site− 1. The active sites were gathered in the region of low GCN (4.8 to 5.5) and tensile strain (5 to 10%). Notably, that the results for ideal model NPs are different (Fig. S15). Ideal model NPs exhibit catalytic activity for GCNs ranging from 4.8 to 5.5 and (111) facets, which is attributed to the kinetic coupling of adsorbates diffusing from the active sites35. However, surface structures are more heterogeneous in real conditions, in which the coupling effects are weakened, and thus the site responsiveness towards the target adsorbates becomes more important.
We demonstrated an experimental-computational framework to identify a structure-activity correlation with the thermal process in heterogeneous catalysis. It is based on an extremely reliable and high-throughput structure database established by experimental analysis in the real time-space domain and computational tracking of each atomic coordinate under the thermal process. Interestingly, the surface minimization induced distinctive surface structures, which is dissimilar from ideal NP models. Experimentally resolved 3D atomic coordinates of Pt NPs enabled efficient generation of a first-principles database for machine-learning interatomic potentials and an understanding of catalytic activity with varying size. Structural evolution showed that ligand-protected Pt NPs preserve the tensile strain due to the ligand interaction even after the detachment process induced by thermal annealing. Using the CO oxidation reaction in colloidally synthesized Pt NPs, we characterized the individual site contributions to the overall catalytic activity, revealing that high-Miller-index or edge sites with tensile strain can be beneficial for the CO oxidation reaction. Our approach can be further developed to incorporate the support material effect on the catalytic activity.
The DFT structure-energy data set to train the potential energy surface of Pt was calculated using the Vienna ab-initio simulation package (VASP)37-39. The projector augmented wave40 pseudopotential was used, and the generalized gradient approximation with Perdew-Burke-Ernzerhof (PBE) functional40 was employed to describe the exchange-correlation energy. The cutoff energy for the plane-wave basis set was set to 400 eV. To train the atomistic potentials, we utilized the Behler-Parrinello high-dimensional neural NNP method41 to determine the energy according to the local geometric motif within the cutoff radius of the reference atom as implemented in the Atomistic machine-learning package (Amp)42. The atom-centred symmetry functions (radial G2 and angular G4) based on the cutoff function (fc) were used to represent the local environments.
Rij is the interatomic distance between reference atom i and neighbour atom j, and if Rij is larger than the cutoff radius (Rc), then the cutoff function is zero. A cutoff of 6.5 Å and total of 53 symmetry functions (Table S1) with different parameters for representing the local atomic environments were used. The neural networks were composed of two hidden layers with 32 and 16 nodes (Supplementary Section 2). MD simulation was performed with Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)43. The interaction potential of Pt was parametrized by constructed NNP as described in the previous section. Individual Pt NPs were placed in the simulation cell with 15 Å of vacuum space to make the periodic interactions negligible. The cutoff radius of the pair atomic distance was 6.5 Å. After energy minimization of the system using the Polak-Ribiere version of the conjugate gradient (CG) algorithm, the ligand detachment process was simulated for each Pt NC through NVT Nosé–Hoover thermostats, and atomic configurations of Pt were captured every 0.5 ps. The kMC simulation was performed via MonteCoffee44 with the first reaction method (FRM) algorithm45. The scaling relations of adsorption energies for CO oxidation reaction intermediates as a function of GCN were adopted from the previous literature46. Supplementary Section 4 describes the details of the modification of the adsorption energy by atomic strain and the kMC simulation.
Acknowledgements
H. C., S. J. H., and B. H. acknowledge the Global Frontier Program through the Global Frontier Hybrid Interface Materials (GFHIM) of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Project No. 2013M3A6B1078882) and Creative Materials Discovery Program through the NRF funded by the Ministry of Science and ICT (NRF2017M3D1A1039287). J. K. acknowledges the Ministry of Trade, Industry, and Energy (MOTIE), Korea, under the “Digital manufacturing platform" (No. N0002598) supervised by the Korea Institute for Advancement of Technology (KIAT). H. C. and B. H. K. acknowledge (Project No. NRF- R1C1C11014339). D. K., J. H., and J. P. acknowledge Institute for Basic Science (Project No. IBS-R006-D1) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MIST) (No. NRF-2020R1A2C2101871, No. NRF-2019M3E6A1064877, and No. NRF-2021M3H4A1A02049904).
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