Challenges of modeling nanostructured materials for photocatalytic water splitting

Understanding the water splitting mechanism in photocatalysis is a rewarding goal as it will allow producing clean fuel for a sustainable life in the future. However, identifying the photocatalytic mechanisms by modeling photoactive nanoparticles requires sophisticated computational techniques based on multiscale modeling. In this review, we will survey the strengths and drawbacks of currently available theoretical methods at different length and accuracy scales. Understanding the surface-active site through Density Functional Theory (DFT) using new, more accurate exchange-correlation functionals plays a key role for surface engineering. Larger scale dynamics of the catalyst/electrolyte interface can be treated with Molecular Dynamics albeit there is a need for more generalizations of force fields. Monte Carlo and Continuum Modeling techniques are so far not the prominent path for modeling water splitting but interest is growing due to the lower computational cost and the feasibility to compare the modeling outcome directly to experimental data. The future challenges in modeling complex nano-photocatalysts involve combining different methods in a hierarchical way so that resources are spent wisely at each length scale, as well as accounting for excited states chemistry that is important for photocatalysis, a path that will bring devices closer to the theoretical limit of photocatalytic efficiency.

C2 weakens the turnover frequency during the melting of FexCy: insights from reactive MD simulations

The carbon accumulation in the form of C2 on the surface at high temperatures blocks the surface catalytic active sites, reducing the activity of melted FexCy nanoparticles.

Enhancing classical gold nanoparticle simulations with electronic corrections and machine learning

Classical simulations of materials and nanoparticles have the advantage of speed and scalability but at the cost of precision and electronic properties, while electronic structure simulations have the advantage of accuracy and transferability but are typically limited to small and simple systems due to the increased computational complexity. Machine learning can be used to bridge this gap by providing correction terms that deliver electronic structure results based on classical simulations, to retain the best of both worlds. In this study we train an artificial neural network (ANN) as a general ansatz to predict a correction of the total energy of arbitrary gold nanoparticles based on general (material agnostic) features, and a limited set of structures simulated with an embedded atom potential and the self-consistent charge density functional tight binding method. We find that an accurate model with an overall precision of 14 eV or 8.6% can be found using a diverse range of particles and a large number of manually generated features which were then reduced using automatic data-driven approach to reduce evaluation bias. We found the ANN reduces to a linear relationship if a suitable subset of important features are identified prior to training, and that the prediction can be improved by classifying the nanoparticles into kinetically limited and thermodynamically limited subsets based prior to training the ANN corrections. The results demonstrate the potential for machine learning to enhance classical molecular dynamics simulations without adding significant computational complexity, and provides methodology that could be used to predict other electronic properties which cannot be calculated solely using classical simulations.

Structural Evolution of the Surface and Interface in Bimetallic High-Index Faceted Heterogeneous Nanoparticles

Bimetallic high-index faceted heterostructured nanoparticles represent a new class of high-performance nanocatalysts. In this work, we investigated the structural evolution of PtAu tetrahexahedral heterostructured nanoparticles enclosed by {210} facets using molecular dynamics simulations. The surface and interface were specifically addressed. The results show that the PtAu nanoparticle exhibits a heterogeneous melting pattern, leading to solid-liquid coexistence over a wide temperature range. In terms of particle shape evolution, the critical transformation temperature for the surface structure of the PtAu heterostructured nanoparticle is much lower than the melting point of each domain. In comparison, the interface could be basically retained even when the Au domain completely melts. These results extend our fundamental understanding of the thermally driven structural evolution of the surface and interface in bimetallic high-index faceted heterostructured nanoparticles and provide insight into the design and application of metallic nanoparticles with multifunctional performance.

The pure and representative types of disordered platinum nanoparticles from machine learning

The development of interpretable structure/property relationships is a cornerstone of nanoscience, but can be challenging when the structural diversity and complexity exceeds our ability to characterise it. This is often the case for imperfect, disordered and amorphous nanoparticles, where even the nomenclature can be unspecific. Disordered platinum nanoparticles have exhibited superior performance for some reactions, which makes a systematic way of describing them highly desirable. In this study we have used a diverse set of disorder platinum nanoparticles and machine learning to identify the pure and representative structures based on their similarity in 121 dimensions. We identify two prototypes that are representative of separable classes, and seven archetypes that are the pure structures on the convex hull with which all other possibilities can be described. Together these nine nanoparticles can explain all of the variance in the set, and can be described as either single crystal, twinned, spherical or branched; with or without roughened surfaces. This forms a robust sub-set of platinum nanoparticle upon which to base further work, and provides a theoretical basis for discussing structure/property relationships of platinum nanoparticles that are not geometrically ideal.

Dynamic vs static behaviour of a supported nanoparticle with reaction-induced catalytic sites in a lattice model

Modern literature shows a rapidly growing interest to the supported nanocatalysts with dynamic behaviour under reaction conditions. This new frontier of heterogeneous catalysis is recognized as one of the most challenging and worthy of consideration from all possible angles. In this context, a previously suggested lattice model is used to get an insight, by means of kinetic Monte Carlo, into the influence of the mobility of reaction-induced catalytic sites of a two-dimensional supported nanoparticle on the system behaviour. The results speak in favour of feasibility of dynamic nanocatalysts with self-organized structures capable of robust functioning. This approach, from the macroscopic end, is believed to be a useful complement to ever developing experimental and first principle approaches.

Understanding the effect of interfacial interaction on metal/metal oxide electrocatalysts for hydrogen evolution and hydrogen oxidation reactions on the basis of first-principles calculations

Interfacial M–O and M–TM interactions of M/TMO contribute differently to the surface properties and HER/HOR pathway.

Correlating anisotropy and disorder with the surface structure of platinum nanoparticles

Due to the competition between numerous physicochemical variables during formation and processing, platinum nanocatalysts typically contain a mixture of shapes, distributions of sizes, and a considerable degree of surface imperfection. Structural imperfection and sample polydispersivity are inevitable at scale, but accepting bulk and surface diversity as legitimate design features provides new opportunities for nanoparticle design. In recent years disorder and anisotropy have been embraced as useful design parameters but predicting the impact of uncontrollable imperfection a priori is challenging. In the present work we have created an ensemble of uniquely imperfect nanoparticles extracted from classical molecular dynamics trajectories and applied statistical filters to restrict the ensemble in ways that reflect common industrial design principles. We find that targeting different sizes and size distributions may be an effective way of promoting or suppressing internal disorder or crystallinity (as required), but the degree of anisotropy of the particle as a whole has a greater impact on the population of different types of surface ordering and active sites. These results indicate that tuning of disordered and anisotropic Pt nanoparticles is possible, but it is not as straightforward as geometrically ideal nanoparticles with a high degree of crystallinity. It is unlikely that a synthesis strategy could eliminate this diversity entirely, or ensure this type of structural complexity does not develop post-synthesis under operational conditions, but it may be possible to bias the formation of specific bulk structures and the surface anisotropy.

Escaping the trap of complication and complexity in multiscale microkinetic modelling of heterogeneous catalytic processes

In this feature article, the development of methods to enable a hierarchical multiscale approach to the microkinetic analysis of heterogeneous catalytic processes is reviewed. This methodology is an effective route to escape the trap of complication and complexity in multiscale microkinetic modelling. On the one hand, the complication of the problem is related to the fact that the observed catalyst functionality is inherently a multiscale property of the reacting system and its analysis requires bridging the phenomena at different time and length scales. On the other hand, the complexity of the problem derives from the system dimension of the chemical systems, which typically results in a number of elementary steps and species, that are beyond the limit of accessibility of present-day computational power even for the most efficient implementation of atomistic first-principles simulations. The main idea behind the hierarchical approach is to tackle the problem with methods of increasing accuracy in a dual feed-back loop between theory and experiments. The potential of the methodology is shown in the context of unravelling the WGS and r-WGS catalytic mechanisms on Rh catalysts. As a perspective, the extension to structure-dependent microkinetic modelling is discussed.

Size-Controlled and Optical Properties of Platinum Nanoparticles by Gamma Radiolytic Synthesis

Gamma radiolytic synthesis was used to produce size-controlled spherical platinum nanoparticles from an aqueous solution containing platinum tetraammine and polyvinyl pyrrolidone. The structural characterizations were performed using X-ray diffraction, and transmission electron microscopy. The transmission electron microscopy was used to determine the average particle diameter, which decreased from 4.4nm at 80kGy to 2.8nm at 120kGy. The UV-visible absorption spectrum was measured and found that platinum nanoparticles exhibit two steady absorption maxima in UV regions due to plasmonic excitation of conduction electrons, which blue shifted to lower wavelengths with a decrease in particle size. We consider the conduction electrons of platinum nanoparticles to follow Thomas-Fermi-Dirac-Weizsacker atomic model that they are not entirely free but weakly bounded to particles at lower-energy states {n = 5, l = 2 or 5d} and {n = 6, l = 0 or 6s}, which upon receiving UV photon energy the electrons make intra-band quantum excitations to higher-energy states allowed by the principles of quantum number that results the absorption maxima. We found an excellent agreement between the experimental and theoretical results, which suggest that the optical absorption of metal nanoparticles could be fundamentally described by a quantum mechanical interpretation, which could be more relevant to photo-catalysis and heterogeneous catalysis.

Predicting the role of seed morphology in the evolution of anisotropic nanocatalysts

Controlling the structure of nanocrystals is an effective way to tune their properties and improve performance in a wide variety of applications. However, the atomic pathways for achieving this goal are difficult to identify and exercise, due to competing kinetic and thermodynamic influences during formation. In particular, an understanding of how symmetry, and symmetry breaking, determine nanocrystal morphology would significantly advance our ability to produce nanomaterials with prescribed functions. In this study we present results of a detailed computational study into the atomic structure of platinum nanoparticles at early growth stages of formation, as a function of temperature and atomic deposition rates. We investigate the impact of different types of crystalline seeds and characterize the emergent structures via simulated High Resolution Transmission Electron Microscopy (HRTEM) images. We find that the choice of initial seed is an important driver for symmetry breaking, due to a combination of atomic deposition and etching on different seed facets. A mix of low index facets causes the formation of important surface defects, in addition to the absorption/adsorption of single atoms, which can be correlated with different catalytic reactions as the process perpetuates. These findings provide new insights into nanocrystal shape-control mechanisms and suggest new opportunities for future design of this important class of nanomaterials.

Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles

Accurate predictions of nanocatalyst structure/property relations can be made with large theoretical data sets, rather than limited sets of computational structures, in a fraction of the time using machine learning.