Hector Barron

Elasticity of MoS2 sheets by mechanical deformation observed by in situ electron microscopy

MoS2 has been the focus of extensive research due to its potential applications. More recently, the mechanical properties of MoS2 layers have raised interest due to applications in flexible electronics. In this article, we show in situ transmission electron microcsopy (TEM) observation of the mechanical response of a few layers of MoS2 to an external load. We used a scanning tunneling microscope (STM) tip mounted on a TEM stage to induce deformation on nanosheets of MoS2 containing few layers. The results confirm the outstanding mechanical properties on the MoS2. The layers can be bent close to 180°. However, when the tip is retrieved the initial structure is recovered. Evidence indicates that there is a significant bond reconstruction during the bending with an outstanding capability to recover the initial bond structure. The results show that flexibility of three layers of MoS2 remains the same as a single layer while increasing the bending modulus by 3 orders of magnitude. Our findings are consistent with theoretical calculations and confirm the great potential of MoS2 for applications.

Using structural diversity to tune the catalytic performance of Pt nanoparticle ensembles

Recent developments of metallic nanoparticle catalysts have been largely based on the assumption and evidence that exquisite control over the size or shape (or both) is critically important to the economic efficiency of future products. However, the cost associated with reducing polydispersivity on the industrial scale is also a limiting factor, and at this stage it is unclear if samples that are monodispersed in size or shape are more desirable. In this study we use a combination of thermodynamic and statistical models to explore how restricting different types of structural polydispersivity impacts the performance of platinum electrocatalysts, characterized by the molar density of surface defects, and their respective degree of under-coordination. We find that a combination of simultaneous size and shape control is advantageous, but attention and resources should be directed toward producing shape control. More specifically, a sample containing particles entirely enclosed by {111} facets, regardless of the geometric shape, will always outperform samples where other crystallographic facets are present; but perfect monodispersivity is unnecessary. Distributions in both size and shape are acceptable (and can even be useful), provided they are predictable and reproducible.

Dynamic evolution of specific catalytic sites on Pt nanoparticles

Platinum nanoparticles are widely used catalysts in many important industrial applications, in the chemical, petrochemical, automotive and energy sectors. Due to the extremely high cost and the limited abundance of platinum, improving the efficiency of platinum-based nanocatalysts is key to the economic development of these materials, as well as being a challenge for basic research. Ultimately we seek to increase the active surfaces area, per unit volume, and to preserve the activity and selectivity over the functional lifetime of the product. In this work the formation of platinum nanoparticles is investigated by molecular dynamic simulations under different conditions of temperature and atomic deposition rates to identify the conditions that give rise to a greater density of different types of surface active sites. By tuning the growth conditions we obtained highly non-equilibrium morphologies with branches that expose larger surface areas that are consistent with experimental observations. The results are also used to clarify the relationship between growth conditions, surface structure and catalytic functionality based on a simple surface defect classification model, which differentiates between CO oxidation reactions, hydrogen evolution reactions (HER) and hydrogen oxidation reactions (HOR).

Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles

Even using high throughput methods, data-driven predictions of nanomaterials properties from first principles simulations can be impractical. In this work, machine learning models are developed to map the catalytic efficiency of Pt nanocrystals to structural features, such as nanoparticle diameter, surface area, sphericity, facet configuration and type of surface defects, using a theoretically derived big data set of over three hundred thousand nanoparticles. Artificial Neural Networks (ANNs) were calibrated with 50% of a data set including structural features of symmetric Pt nanoparticles; and catalytic activity, selectivity and thermodynamic stability. Surface response analysis was applied to two-inputs ANNs with squared correlation coefficient > 0.9, yielding a region of optimal catalytic efficiency for the less spherical nanocatalysts and {110} facets lower than 20%. Binary decision tree models reveal the optimal three-property combinations for high catalytic efficiency. In addition, ANN models built for non-symmetric nanoparticles predict the catalytic efficiency and stability with accuracy >0.93. In general, we show the combination of machine learning models can rapidly estimate functional properties of hypothetical nanomaterials at a resolution that is inaccessible to both computation and experimental methods, as well as identifying principles or rules that could guide rational nanomaterial design in the near future.

Highly active catalyst for CO2 methanation derived from a metal organic framework template

The conversion of CO2 into chemicals of commercial interest is a rapidly expanding area of research. Here, we present a highly active and stable CO2 methanation catalyst that is derived from a Ru-impregnated zirconium-based metal–organic framework (MOF) material. The Ru-doped MOF is transformed, under reaction conditions, into an active catalyst which yields CO2 conversions of 96% and a CH4 selectivity of 99%. We demonstrate that the final catalyst was composed of a mixture of Ru-nanoparticles supported on monoclinic and tetragonal ZrO2 nanoparticles. Notably, such catalytic activity has only been achieved using the MOF templating strategy. Catalysts of the same composition were synthesized via different methods but were less active for CO2 methanation.

Three-Dimensional Branched and Faceted Gold-Ruthenium Nanoparticles: Using Nanostructure to Improve Stability in Oxygen Evolution Electrocatalysis

Achieving stability with highly active Ru nanoparticles for electrocatalysis is a major challenge for the oxygen evolution reaction. As improved stability of Ru catalysts has been shown for bulk surfaces with low-index facets, there is an opportunity to incorporate these stable facets into Ru nanoparticles. Now, a new solution synthesis is presented in which hexagonal close-packed structured Ru is grown on Au to form nanoparticles with 3D branches. Exposing low-index facets on these 3D branches creates stable reaction kinetics to achieve high activity and the highest stability observed for Ru nanoparticle oxygen evolution reaction catalysts. These design principles provide a synthetic strategy to achieve stable and active electrocatalysts.

Catalytic Efficiency in Metallic Nanoparticles: A Computational Approach

The use of computational methods to characterise and describe different properties in the nanoscale has increased considerably in the recent decades. Catalysis has risen as one of the major focuses in different technological fields since the use of nanostructured materials becomes more common in many industrial processes. Different computational methods have been developed to complement the experimental effort in the design of novel nanocatalysts. To date, density functional (DFT), kinetic Monte Carlo (KMC) and classical molecular dynamics (CMD) simulations allow one to describe catalytic activity for a wide diversity of reactions in different materials. Computational simulations could provide a theoretical guideline for the choice of conditions and nanomaterials to improve a specific catalytic reaction. In this work, we review the most common computational methods used to describe catalytic activity highlighting their applicability and failures. We also examine different cases in which the combination of methods improves the accuracy of the simulations. We also provide a study case in which highly active catalytic nanoparticles can be produced by using CMD simulations.

OPTICAL PROPERTIES OF BIMETALLIC NANOALLOYS FROM TDDFT – ABSORPTION SPECTRA OF 13-ATOM NOBLE METAL CLUSTERS

We present calculations of optical absorption spectra of 13-atom bimetallic Ag-Au clusters. All possible chemical configurations of the icosahedral 13-atom cluster are used as starting structures. The spectra are calculated for the lowest energy structures of each composition. On the gold-rich side of the composition spectrum, the absorption is extremely sensitive to addition of Ag. With two Ag atoms, the characteristic peaks disappear. The Ag-rich side is slightly less sensitive to addition of gold. For intermediate compositions, the clusters do not show characteristic peaks, due to both the chemical disorder and the distortion of the structures.