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Density functional methods are applied to explore the reaction mechanism for CO2 hydrogenation to methanol over low-index Cu surfaces, namely Cu(110) and Cu(100). A detailed reaction network is obtained, examining several different possible mechanistic routes, including methanol formation via formate and hydrocarboxyl bound intermediates, the role of formaldehyde and formic acid as stable intermediary reaction products, as well as exploring the possibility of CO2 dissociation and subsequent hydrogenation of the resultant CO. We find that, in contrast to the dominant Cu(111) facet, the Cu(110) and Cu(100) surfaces facilitate a moderate extent of CO2 activation, which results in lower activation barriers for initial elementary processes involving CO2 hydrogenation and dissociation, opening up reaction pathways considered unfeasible for Cu(111). Consequently, a wider variety of potential mechanistic routes to achieve methanol synthesis is observed and compared to Cu(111), illustrating the essential role of the Cu surface structure in catalytic activity, and providing insights into the mechanism of CO2 hydrogenation over Cu-based catalysts. In providing a thorough and detailed exploration of all of the possible mechanistic pathways for CO2 conversion to methanol, the present work represents a reference point for future studies investigating systems representative of the industrial Cu/ZnO catalyst, enabling a clear identification of the limitations of unsupported Cu catalysts, and thus allowing a more complete understanding of the role of the support material.

The development of affordable bifunctional platinum alloys as electrode materials for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) remains one of the biggest challenges for the transition towards renewable energy sources. Yet, there is very little information on the optimal ratio between platinum and the transition metal used in the alloy and its impact on the electronic properties. Here, we have employed spin-polarised density functional simulations with long-range dispersion corrections [DFT–D3–(BJ)], to investigate the thermodynamics of mixing, as well as the electronic and magnetic properties of the Pt1−xNix solid solution. The Ni incorporation is an exothermic process and the alloy composition Pt0.5Ni0.5 is the most thermodynamically stable. The Pt0.5Ni0.5 solid solution is highly ordered as it is composed mainly of two symmetrically inequivalent configurations of homogeneously distributed atoms. We have obtained the atomic projections of the electronic density of states and band structure, showing that the Pt0.5Ni0.5 alloy has metallic character. The suitable electronic properties of the thermodynamically stable Pt0.5Ni0.5 solid solution shows promise as a sustainable catalyst for future regenerative fuel cells.

With computational resources becoming more efficient and more powerful and at the same time cheaper, computational methods have become more and more popular for studies on biochemical and biomimetic systems. Although large efforts from the scientific community have gone into exploring the possibilities of computational methods for studies on large biochemical systems, such studies are not without pitfalls and often cannot be routinely done but require expert execution. In this review we summarize and highlight advances in computational methodology and its application to enzymatic and biomimetic model complexes. In particular, we emphasize on topical and state-of-the-art methodologies that are able to either reproduce experimental findings, e.g., spectroscopic parameters and rate constants, accurately or make predictions of short-lived intermediates and fast reaction processes in nature. Moreover, we give examples of processes where certain computational methods dramatically fail.