Abstract: Seed-mediated growth is the most efficient methodology to control the size and shape of colloidal metal nanoparticles. In this process, the final nanocrystal shape is defined by the crystalline structure of the initial seed as well as by the presence of ligands and other additives that help to stabilize certain crystallographic facets. We analyze here the growth mechanism in aqueous solution of silver shells on presynthesized gold nanoparticles displaying various well-defined crystalline structures and morphologies. A thorough three-dimensional electron microscopy characterization of the morphology and internal structure of the resulting core-shell nanocrystals indicates that {100} facets are preferred for the outer silver shell, regardless of the morphology and crystallinity of the gold cores. These results are in agreement with theoretical analysis based on the relative surface energies of the exposed facets in the presence of halide ions.

Abstract: Recent findings revealed that surface oxygen can participate in the oxygen evolution reaction (OER) for the most active catalysts, which eventually triggers a new mechanism for which the deprotonation of surface intermediates limits the OER activity. We propose in this work a “dual strategy” in which tuning the electronic properties of the oxide, such as La1-xSrxCoO3-delta, can be dissociated from the use of surface functionalization with phosphate ion groups (P-i) that enhances the interfacial proton transfer. Results show that the P-i functionalized La0.5Sr0.5CoO3-delta gives rise to a significant enhancement of the OER activity when compared to La0.5Sr0.5Co3-delta and LaCoO3. We further demonstrate that the P-i surface functionalization selectivity enhances the activity when the OER kinetics is limited by the proton transfer. Finally, this work suggests that tuning the catalytic activity by such a “dual approach” may be a new and largely unexplored avenue for the design of novel high-performance catalysts.

Abstract: Dislocations are always observed during crystal growth, and it is usually desirable to reduce the dislocation density in high-quality crystals. Here, the annihilation process of the 30 degrees Shockley partial dislocation pairs in CdTe is studied by first-principles calculations. We found that the dislocations can glide relatively easily due to the weak local bonding. Our systematic study of the slipping mechanism of the dislocations suggests that the energy barrier for the annihilation process is low. Band structure calculations reveal that the band bending caused by the charge transfer between the two dislocation cores depends on the core-core distance. A simple linear model is proposed to describe the mechanism of formation of the dislocation pair. More importantly, we demonstrate that hole injection can affect the core structure, increase the mobility, and eventually trigger a spontaneous mutual annihilation, which could be employed as a possible facile way to reduce the dislocation density.

Abstract: A multiscale modeling and simulation approach, including first-principles calculations, ab initio molecular dynamics simulations, and a tight binding approach, is employed to study band flattening of the electronic band structure of oxidized monolayer graphene. The width offlat bands can be tuned by strain, the external electric field, and the density of functional groups and their distribution. A transition to a conducting state is found for monolayer graphene with impurities when it is subjected to an electric field of similar to 1.0 V/angstrom. Several parallel impurity-induced flat bands appear in the low-energy spectrum of monolayer graphene when the number of epoxy groups is changed. The width of the flat band decreases with an increase in tensile strain but is independent of the electric field strength. Here an alternative and easy route for obtaining band flattening in thermodynamically stable functionalized monolayer graphene is introduced. Our work discloses a new avenue for research on band flattening in monolayer graphene.

Abstract: CsPbI3 is a promising perovskite material for photovoltaic applications in its photoactive perovskite or black phase. However, the material degrades to a photovoltaically inactive or yellow phase at room temperature. Various mitigation strategies are currently being developed to increase the lifetime of the black phase, many of which rely on inducing strains in the material that hinder the black-to-yellow phase transition. Physical insight into how these strategies exactly induce strain as well as knowledge of the spatial extent over which these strains impact the material is crucial to optimize these approaches but is still lacking. Herein, we combine machine learning potential-based molecular dynamics simulations with our in silico strain engineering approach to accurately quantify strained large-scale atomic structures on a nanosecond time scale. To this end, we first model the strain fields introduced by atomic substitutions as they form the most elementary strain sources. We demonstrate that the magnitude of the induced strain fields decays exponentially with the distance from the strain source, following a decay rate that is largely independent of the specific substitution. Second, we show that the total strain field induced by multiple strain sources can be predicted to an excellent approximation by summing the strain fields of each individual source. Finally, through a case study, we illustrate how this additive character allows us to explain how complex strain fields, induced by spatially extended strain sources, can be predicted by adequately combining the strain fields caused by local strain sources. Hence, the strain additivity proposed here can be adopted to further our insight into the complex strain behavior in perovskites and to design strain from the atomic level onward to enhance their sought-after phase stability.

Abstract: Density functional theory is widely used to gain insights into molecule−metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA)
density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the selfinteraction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface
reaction network, namely, CO2 hydrogenation on Cu. We show that the mGGA DF used, namely, rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals and molecules, as well as molecule−metal surface adsorption
and activation energies. Hence, it is a better choice for constructing molecule−metal surface reaction networks.