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Cu-Exchanged zeolites are promising materials for the selective conversion of methane to methanol. Their activity is attributed to the presence of small Cu-oxo and Cu-hydroxy clusters, but the nature of active centers in various zeolite structures is still under debate. In this contribution, we combine time dependent density functional theory with spin–orbit coupling to predict the optical spectra of various Cu monomers and dimers in SSZ-13. We furthermore compare theoretical results to experimental measurements and find that the presence of Cu-hydroxy dimers and Cu monomers could potentially explain the experimentally observed UV-vis-NIR spectra. 

Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

 

The catalytic properties of zeolites are intimately linked to the distribution and relative positions of Al atoms and defects in the pore network. However, characterizing this distribution is challenging, in particular when different local Al arrangements are considered. In this contribution we use a combination of first principles calculations and experimental measurements to develop a model for the Al-distribution in protonated SSZ-13. We furthermore apply this model to understand trends in OH-IR, 27 Al-NMR and 29 Si-NMR spectra. We use a Boltzmann distribution to predict the proton position for a given local Al configuration and show that for each configuration several H positions are occupied. Therefore a multi-peak spectrum in OH-IR vibrational spectroscopy is observed for all Al configurations, which is in line with experimentally measured spectra for zeolites at different Si/Al ratios. From NMR spectroscopy we find that the proton position leads to significant shifts in 27 Al-NMR and 29 Si-NMR spectra due to the modification of the local strain, which is lost when a uniform background charge is introduced. These findings are supported by experimental measurements. Finally we discuss the shortcomings of the presented model in terms of unit cell size and the impact of adjacent unit cells. 

The speciation and structure of Cu ions and complexes in chabazite (SSZ-13) zeolites, which are relevant catalysts for nitrogen oxide reduction and partial methane oxidation, depend on material composition and reaction environment. Ultraviolet-visible (UV-Vis) spectra of Cu-SSZ-13 zeolites synthesized to contain specific Cu site motifs, together with ab initio molecular dynamics and time-dependent density functional theory calculations, were used to test the ability to relate specific spectroscopic signatures to specific site motifs. Geometrically distinct arrangements of two framework Al atoms in six-membered rings are found to exchange Cu 2+ ions that become spectroscopically indistinguishable after accounting for the finite-temperature fluctuations of the Cu coordination environment. Nominally homogeneous single Al exchange sites are found to exchange a heterogeneous mixture of [CuOH] + monomers, O- and OH-bridged Cu dimers, and larger polynuclear complexes. The UV-Vis spectra of the latter are sensitive to framework Al proximity, to precise ligand environment, and to finite-temperature structural fluctuations, precluding the precise assignment of spectroscopic features to specific Cu structures. In all Cu-SSZ-13 samples, these dimers and larger complexes are reduced by CO to Cu + sites at 523 K, leaving behind isolated [CuOH] + sites with a characteristic spectroscopic identity. The various mononuclear and polynuclear Cu 2+ species are distinguishable by their different responses to reducing environments, with implications for their relevance to catalytic redox reactions. 

Chemical vapor deposition of CH 4 on Ge(001) can enable anisotropic growth of narrow, semiconducting graphene nanoribbons with predominately smooth armchair edges and high-performance charge transport properties. However, such nanoribbons are not aligned in one direction but instead grow perpendicularly, which is not optimal for integration into high-performance electronics. Here, it is demonstrated that vicinal Ge(001) substrates can be used to synthesize armchair nanoribbons, of which ∼90% are aligned within ±1.5° perpendicular to the miscut. When the growth rate is slow, graphene crystals evolve as nanoribbons. However, as the growth rate increases, the uphill and downhill crystal edges evolve asymmetrically. This asymmetry is consistent with stronger binding between the downhill edge and the Ge surface, for example due to different edge termination as shown by density functional theory calculations. By tailoring growth rate and time, nanoribbons with sub-10 nm widths that exhibit excellent charge transport characteristics, including simultaneous high on-state conductance of 8.0 μS and a high on/off conductance ratio of 570 in field-effect transistors, are achieved. Large-area alignment of semiconducting ribbons with promising charge transport properties is an important step towards understanding the anisotropic nanoribbon growth and integrating these materials into scalable, future semiconductor technologies.