Double-well potential energy surface in the interaction between h-BN and Ni(111)

Density functional theory calculations with non-local correlation functionals, properly accounting for dispersion forces, predict the presence of two minima in the interaction energy between h-BN and Ni(111). These can be described as a physisorbed state with no corrugation of the h-BN structure, and a chemisorbed state exhibiting noticeable corrugation and shorter distance of h-BN to the metallic support. The latter corresponds indeed to the one reported in most experiments. The relative stability of the two minima depends on the specific density functional employed: of those investigated here only the optB86b-vdW yields the correct order of stability. We also demonstrate that the effect of the metal support on the Raman frequency of the chemisorbed boron nitride monolayer cannot be reduced to the associated strain. This is important because the Raman frequency has been proposed as a signature to identify h-BN monolayers from multilayered samples. Our analysis shows that such signatures would be strongly dependent on the nature of the support – h-BN interaction

Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces

The excessive emissions of carbon dioxide (CO$_2$) into the atmosphere threaten to shift the CO$_2$ cycle planet-wide and induce unpredictable climate changes. Using artificial intelligence (AI) trained on high-throughput first principles based data for a broad family of oxides, we develop a strategy for a rational design of catalytic materials for converting CO$_2$ to fuels and other useful chemicals. We demonstrate that an electron transfer to the $\pi^*$-antibonding orbital of the adsorbed molecule and the associated bending of the initially linear molecule, previously proposed as the indicator of activation, are insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI model identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. This finding suggests using the C-O bond elongation as an indicator of CO$_2$ activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO$_2$ conversion, and a recipe to find more

Performance of Minnesota functionals on predicting core-level binding energies of molecules containing main-group elements

Here we explored the performance of M06, M06-L, M11, and M11-L Minnesota functionals on predicting core-level 1s binding energies (BEs) and BE shifts (Delta BEs) for a set of 20 organic molecules containing main-group elements C -> F (39 core levels in total). The broadly used Hartree-Fock (HF) and Becke-Lee-Yang-Parr (B3LYP) methods have also been studied for comparison. A statistical analysis comparing with X-ray photoelectron spectroscopy (XPS) experimental values shows that overall BEs estimations only deviate a small percentage from the experimental values, yet the absolute deviations are generally too large, with the different methods over/underestimating the reported values. However, taking the contribution of relativistic effects of BEs into account leads to larger differences. Overall, the performance of the explored Minnesota functionals is not satisfactory, with errors of up to 1 eV, except for the M06-L meta-GGA functional. In this case, the mean absolute deviation is below 0.1 eV and thus within XPS chemical resolution. Hence, M06-L poses itself as a rather accurate and computational expense-wise method for estimating BEs of organic molecules. Nevertheless, the observed deviations almost cancel when considering Delta BEs with respect to some arbitrary reference, with errors within 0.2-0.3 eV, indicating that these are largely systematic, which in turn implies that the corresponding methods have room for improvement

Understanding the reactivity of metallic nanoparticles: beyond the extended surface model for catalysis

Metallic nanoparticles (NPs) constitute a new class of chemical objects which are used in different fields as diverse as plasmonics, optics, catalysis, or biochemistry. The atomic structure of the NP and its size usually determine the chemical reactivity but this is often masked by the presence of capping agents, solvents, or supports. The knowledge of the structure and reactivity of isolated NPs is a requirement when aiming at designing NPs with a well-defined chemistry. Theoretical models together with efficient computational chemistry algorithms and parallel computer codes offer the opportunity to explore the chemistry of these interesting objects and to understand the effects of parameters such as size, shape and composition allowing one to derive some general trends

Density functional studies of coinage metal nanoparticles: scalability of their properties to bulk

Density functional plane-wave calculations have been carried out for series of Cu n ,Ag n and Au n particles containing up to 146 (Cu, Ag) and 225 (Au) atoms. Full geometry optimization has been performed for all particles starting from the structures created by cuts from the bulk. In line with previous studies, calculated average nearest-neighbour distances and cohesive energies of the particles linearly depend on such size-derived parameters as the average coordination number of metal atoms and the inverse of the mean particle radius, respectively. Rather accurate linear extrapolation of the observables under scrutiny to the bulk values has been achieved. However, we show that the scalability for particles made of various elements of the same d10s1 electron configuration differs, e.g. for bond lengths in Au n species it is noticeably less perfect than that for Cu n and Ag n ones. Implications of encountered structural peculiarities of the nanoparticles for their reactivity are outlined

Tuning Transition Metal Carbides Activity by Surface Metal Alloying: Case Study on CO2 Capture and Activation

CO2 is one of the main actors in the greenhouse effect and its removal from the atmosphere is becoming an urgent need. Thus, CO2 capture and storage (CCS) and CO2 capture and usage (CCU) technologies are intensively investigated as technologies to decrease the concentrationof atmospheric CO2. Both CCS and CCU require appropriate materials to adsorb/release and adsorb/activate CO2, respectively. Recently, it has been theoretically and experimentally shown that transition metal carbides (TMC) are able to capture, store, and activate CO2. To further improve the adsorption capacity of these materials, a deep understanding of the atomic level processes involved is essential. In the present work, we theoretically investigate the possible effects of surface metal doping of these TMCs by taking TiC as a textbook case and Cr, Hf, Mo, Nb, Ta, V, W, and Zr as dopants. Using periodic slab models with largesupercells and state-of-the-art density functional theory based calculations we show that CO2 adsorption is enhanced by doping with metals down a group but worsened along the d series. Adsorption sites, dispersion and coverage appear to play a minor, secondary constant effect. The dopant-induced adsorption enhancement is highly biased by the charge rearrangement at the surface. In all cases, CO2 activation is found but doping can shift the desorption temperature by up to 135 K.</div