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Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

The ratio of NMR relaxation time constants T 1 / T 2 provides a non-destructive indication of the relative surface affinities exhibited by adsorbates within liquid-saturated mesoporous catalysts. In the present work we provide supporting evidence for the existence of a quantitative relationship between such measurements and adsorption energetics. As a prototypical example with relevance to green chemical processes we examine and contrast the relaxation characteristics of primary alcohols and cyclohexane within an industrial silica catalyst support. T 1 / T 2 values obtained at intermediate magnetic field strength are in good agreement with DFT adsorption energy calculations performed on single molecules interacting with an idealised silica surface. Our results demonstrate the remarkable ability of this metric to quantify surface affinities within systems of relevance to liquid-phase heterogeneous catalysis, and highlight NMR relaxation as a powerful method for the determination of adsorption phenomena within mesoporous solids.

Automatic analysis of competing mechanisms.

Comprehensive mechanistic insights into the aqueous-phase hydrogenolysis of glycerol by the ReO x –Ir catalyst were obtained by combining density functional theory (DFT) calculations with batch reaction experiments and detailed characterization of the catalysts using X-ray diffraction, X-ray photoelectron spectroscopy, and Fourier transform infrared techniques. The role and contribution of the aqueous acidic reaction medium were investigated using NMR relaxometry studies complemented with molecular dynamics and DFT calculations. At higher glycerol concentration, the enhanced competitive interaction of glycerol with the catalyst improved the conversion of glycerol. Sulfuric acid increased the concentration of glycerol within the pores of the catalyst and enhanced the propensity for dissociative adsorption of glycerol on the catalyst, explaining the promotional effect of acid during hydrogenolysis. Partially reduced and dispersed Brønsted acidic ReO x clusters on metallic Ir nanoparticles facilitated dissociative attachment of glycerol and preferential formation of the primary propoxide. The formation of the dominant product, 1,3-propanediol (1,3-PDO), results from the selective removal of the secondary hydroxyl of glycerol, with a comparatively low activation barrier of 123.3 kJ mol–1 in the solid Brønsted acid-catalyzed protonation–dehydration mechanism or 165.2 kJ mol–1 in the direct dehydroxylation mechanism. The formation of 1-propanol (1-PO) is likely to follow a successive dehydroxylation pathway in the early stages of the reaction. Although 1,3-PDO is less reactive than 1,2-propanediol (1,2-PDO), it preferentially adsorbs on the catalyst in a mixture containing glycerol to form 1-PO. The thermodynamically favorable pathway involving dehydrogenation, dehydroxylation, and hydrogenation elementary steps led to the dominant production of 1,2-PDO on pure Ir catalyst with a high C–O bond cleavage barrier of 207.4 kJ mol–1. The optimum ReO x –Ir catalyst with an Ir/Re ratio of 1 exploits the synergy of the sites of both the components. The detailed insights presented here would guide the rational selection of catalysts for the hydrogenolysis of polyols and the optimization of reaction parameters.

Complex chemical reaction environments, such as those found in combustion engines, the upper atmosphere, or the interstellar medium, can contain large numbers of different reactive species participating in similarly large numbers of different chemical reactions. In such settings, identifying the most-likely multistep reaction mechanisms which lead to the production of a particular defined product species is an extremely challenging problem, requiring search and evaluation over a large number of different possible candidate mechanisms while also addressing the permutational challenges posed when considering a large number of reaction routes available to sets of identical molecular species. In this article, the problem of generating candidate reaction mechanisms which form a defined product from a diverse set of reactive molecules is cast as a discrete optimization of a permutationally invariant cost function describing similarity between the target product and the product generated by a trial reaction mechanism. This approach is demonstrated by generating 2230 candidate reaction mechanisms which form benzene from diverse sets of reactive molecules which have been experimentally identified in the interstellar medium. By screening this set of autogenerated mechanisms, using dispersion-corrected DFT to evaluate reaction energies and activation barriers, we identify several candidate barrierless reaction mechanisms (both previously proposed and new) for benzene formation which may operate in the low temperatures found in the interstellar medium and could be investigated further to supplement existing microkinetic models.

Direct MCTDH quantum dynamics simulations, with automatic active coordinate generation, applied to potential molecular sunscreens.

Numerous different algorithms have been developed over the last few years which are capable of generating large, dense chemical reaction networks describing the inherent chemical reactivity of a collection of discrete molecules. For all elementary reactions in a given reaction network, reaction rate calculations, followed by direct micro-kinetic modelling, enables one to predict macroscopic outcomes (e. g. rate laws, product selectivity) based on atomistic input data. However, for chemical reaction networks containing thousands of reactant molecules, such simulations can be extremely time-consuming; in addition, the complex coupled time-dependence of molecular concentrations can present challenges when seeking essential mechanistic features. In this Article, we instead present an algorithm which seeks to predict the "most likely" reaction mechanism, or competing mechanisms, connecting any two user-selected reactant and product species, given a previouslygenerated reaction network as input. The approach is successfully tested for reaction networks (containing tens of thousands of possible reactions) describing the carbon monoxide oxidation on platinum nanoparticles.