The exothermic oxidative dehydrogenation of propane reaction to generate propene has the potential to be a game-changing technology in the chemical industry. However, even after decades of research, selectivity to propene remains too low to be commercially attractive because of overoxidation of propene to thermodynamically favored CO Here, we report that hexagonal boron nitride and boron nitride nanotubes exhibit unique and hitherto unanticipated catalytic properties, resulting in great selectivity to olefins. As an example, at 14% propane conversion, we obtain selectivity of 79% propene and 12% ethene, another desired alkene. Based on catalytic experiments, spectroscopic insights, and ab initio modeling, we put forward a mechanistic hypothesis in which oxygen-terminated armchair boron nitride edges are proposed to be the catalytic active sites.

Rh/SiO 2 catalysts promoted with Fe and Mn are selective for synthesis gas conversion to oxygenates and light hydrocarbons at 523 K and 580 psi. Selective anchoring of Fe and Mn species on Rh nanoparticles was achieved by controlled surface reactions and was evidenced by ultraviolet−visible absorption spectroscopy, scanning transmission electron microscopy, and inductively coupled plasma absorption emission spectroscopy. The interaction between Rh and Fe promotes the selective production of ethanol through hydrogenation of acetaldehyde and enhances the selectivity toward C 2 oxygenates, which include ethanol and acetaldehyde. The interaction between Rh and Mn increases the overall reaction rate and the selectivity toward C 2+ hydrocarbons. The combination of Fe and Mn on Rh/SiO 2 results in trimetallic Rh-Fe-Mn catalysts that surpass the performance of their bimetallic counterparts. The highest selectivities toward ethanol (36.9%) and C 2 oxygenates (39.6%) were achieved over the Rh-Fe-Mn ternary system with a molar ratio of 1:0.15:0.10, as opposed to the selectivities obtained over Rh/SiO 2 , which were 3.5% and 20.4%, respectively. The production of value-added oxygenates and C 2+ hydrocarbons over this trimetallic catalyst accounted for 55% of the total products. X-ray photoelectron spectroscopy measurements suggest that significant fractions of the Fe and Mn species exist as metallic iron and manganese oxides on the Rh surface upon reduction. These findings are rationalized by density functional theory (DFT) calculations, which reveal that the exact state of metals on the surfaces is condition-dependent, with Mn present as Mn(I) and Mn(II) oxide on the Rh (211) step edges and Fe present as Fe(I) oxide on the step edge and metallic subsurface iron on both Rh steps and terraces. CO Fourier transform infrared spectroscopy and DFT calculations suggest that the binding of CO to Rh (211) step edges modified by Fe and/or manganese oxide is altered in comparison to CO adsorption on a clean Rh (211) surface. These results suggest that Mn 2 O x species and Fe and Fe 2 O modify bonding at Rh step edges and shift reaction selectivity away from CH 4 .

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.

Graphene nanoribbons must have smooth, armchair edges, sub-10nm widths, lengths greater than 100nm, controlled placement and orientation to achieve easy integration into semiconductor electronics. However, fabricating ribbons possessing all of these properties has eluded current methods in literature. Here, we demonstrate the seed-mediated growth of graphene nanoribbons on Ge (001) through chemical vapor deposition. Through the use of graphene seeds, aligned, registered arrays of graphene nanoribbons are fabricated with pitches (seed to seed distance) varying from 50 to 500 nm. Ribbon width and length show no relation with pitch, indicating growth is attachment limited and does not depend on nanoribbon separation. Arrays of unidirectionally aligned, armchair edged nanoribbons with lengths greater than 200 nm and sub-10 nm widths are fabricated for the first time.