Shape and Surface Morphology of Copper Nanoparticles under CO₂ Hydrogenation Conditions from First Principles

Abstract

Predicting the state of Cu under a broad range of reaction conditions (pressure and temperature with various adsorbates: CO 2 , CO, H 2 O, H*, and O*) is an important property to understand CO 2 hydrogenation catalysts. Here, unsupported copper (Cu) nanoparticles (NPs) were modeled in vacuum and under conditions relevant for CO 2 hydrogenation conditions from first principles using density functional theory calculations; such models allow precise prediction of particle shapes and surface coverage of the relevant facets of Cu NPs over a large range of conditions relevant to CO 2 hydrogenation. This model predicts that the Cu surfaces are fully reduced (in line with experimental results) and free of adsorbed oxygen (O*), H 2 O*, and CO 2 * under typical reaction conditions. Furthermore, the Cu( 111) facet is at least partially covered with hydrogen (H*) and the Cu(110) facet is partially covered with adsorbed CO* at high reverse-water−gas-shift (RWGS) conversions, while the Cu(100) and Cu(211) facet remain adsorbate-free. Overall, the particle shape of Cu NPs under CO 2 hydrogenation conditions is dominated by the (111) facet with a small area of the (100) facet being present (among all the facets considered). The final equilibrium particle shape is set during the initialization of the CO 2 hydrogenation reaction and does not change even when the WGS equilibrium is reached.

Microkinetic Modeling of the CO2 Desorption from Supported Multifaceted Ni Catalysts

“…The (110) surface forms the edges of the octahedral shape and is a boundary between different (111) facets. The equilibrium shape of a metal nanoparticle can depend on surface coverage, adsorbate, and surrounding gas atmosphere. , Therefore, it can be possible that the shape of the Ni nanoparticle and with it the facet distribution change during the TPD, when the surface coverage decreases due to desorption. This could induce changes in the nanoparticle shape and consequently affect the desorption profile.…”

Understanding and Application of Strong Metal–Support Interactions in Conversion of CO2 to Methanol: A Review

“…Even though great progresses in structure characterization have been achieved, there are still obstacles for us to uncover the real structure of catalysts under reaction conditions as a result of the "pressure gap" and "materials gap". 135 Thanks to the persistent efforts of scientists, much useful information on the reaction mechanism and catalyst structure have been gained by theoretical calculations, 136 yet limitations still cannot be underestimated as a result of the particle size effects, which exert a great effect on catalytic performance. 137…”

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

“…Such strategies will ultimately be key to understanding adsorption configurations on highly complex catalysts, such as polycrystalline nanoparticles, which encompass a variety of different catalyst morphologies. 44,45 The overall workflow is summarized here and described in more detail in The OH* configurations are generated using a modified SurfGraph code that accounts for directional hydrogen bonds among different OH* species (see Figure 2 (211)), suggest high ORR activity on these surfaces, as well. 33,43,46 A mechanistic analysis incorporating the effects of catalyst morphology and OH* coverages is, in turn, needed to understand these experimentally observed trends.…”