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The effect of catalyst hydrophobicity on the kinetics of hydrogenation of aqueous phenol was investigated. The hydrophobicity of a Pd/SBA-15 catalyst was altered by synthesizing an organosilane with biphenylene framework linkers. Partitioning of phenol between the aqueous solution and the pores favors the hydrophobic catalyst by an order of magnitude at room temperature, relative to the hydrophilic catalyst. The rate of hydrogenation at 75 °C is higher in the hydrophobic catalyst, as is the selectivity for the partial hydrogenation product, cyclohexanone. Analysis of kinetic profiles measured using operando 13C NMR reveals that the hydrophobic catalyst has a larger apparent (i.e., composite) adsorption constant for phenol, which results in higher phenol surface coverage and, consequently, faster and more selective hydrogenation to cyclohexanone. 

Rational catalyst design and optimal solvent selection are key to advancing biorefining. Here, we explored the organocatalytic isomerization of d-fructose to a valuable rare monosaccharide, d-allulose, as a function of solvent. The isomerization of d-fructose to d-allulose competes with its isomerization to d-glucose and sugar degradation. In both water and DMF, the catalytic activity of amines towards d-fructose is correlated with their basicity. Solvents impact the selectivity significantly by altering the tautomeric distribution of d-fructose. Our results suggest that the furanose tautomer of d-fructose is isomerized to d-allulose, and the fractional abundance of this tautomer increases as follows: water < MeOH < DMF ≈ DMSO. Reaction rates are also higher in aprotic than in protic solvents. The best d-allulose yield, 14 %, was obtained in DMF with 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) as the catalyst. The reaction kinetics and mechanism were explored using operando NMR spectroscopy. 

Surface polarity plays a key role in controlling molecular adsorption at solid–liquid interfaces, with major implications for reactions and separations. In this study, the chemical composition of periodic mesoporous organosilicas (PMOs) was varied by co-condensing Si(OEt) 4 with organodisilanes, to create a homologous series of materials with similar surface areas, pore volumes, and hydroxyl contents. Their relative surface polarities, obtained by measuring the fluorescence of a solvatochromic dye, cover a wide range. In this series of PMO materials, EPR spectra of tethered nitroxide radicals show monotonically decreasing mobility as larger fractions of the radicals interact strongly with increasingly non-polar surfaces. The surface properties of the materials also correlate with their affinities for organic molecules dissolved in various solvents. The most polar PMO has negligible affinity for phenol, p -cresol, or furfural when these molecules are dissolved in water. However, stronger solute–surface interactions and favor adsorption as the surface polarity decreases. The trend is reversed for furfural in benzene, where weaker solvent–surface interactions result in higher adsorption on polar surfaces. In DMSO, furfural adsorption is suppressed due to the similar strengths of solute-surface and solvent–surface interactions. Thus, the polarity of the surface relative to the solvent is critical for molecular adsorption. These findings show how adsorption/desorption can be precisely and systematically tuned by appropriate choice of both solvent and surface, and contribute to a predictive strategy for the design of catalytic and separations processes. 

Ab initio calculations have greatly advanced our understanding of homogeneous catalysts and crystalline heterogeneous catalysts. In contrast, amorphous heterogeneous catalysts remain poorly understood. The principal difficulties include (i) the nature of the disorder is quenched and unknown; (ii) each active site has a different local environment and activity; (iii) active sites are rare, often less than ∼20% of potential sites, depending on the catalyst and its preparation method. Few (if any) studies of amorphous heterogeneous catalysts have ever attempted to compute site-averaged kinetics, because the exponential dependence on variable activation energy requires an intractable number of ab initio calculations to converge. We present a new algorithm using machine learning techniques (metric learning kernel regression) and importance sampling to efficiently learn the distribution of activation energies. We demonstrate the algorithm by computing the site-averaged activity for a model amorphous catalyst with quenched disorder. 

Ab initio computational studies have made tremendous progress in describing the behavior of molecular (homogeneous) catalysts and crystalline versions of heterogeneous catalysts, but not for amorphous heterogeneous catalysts. Even widely used industrial amorphous catalysts like atomically dispersed Cr on silica remain poorly understood and largely intractable to computational investigation. The central problems are that (i) the amorphous support presents an unknown quenched disordered structure, (ii) metal atoms attach to various surface grafting sites with different rates, and (iii) the resulting grafted sites have different activation and catalytic reaction kinetics. This study combines kernel regression and importance sampling techniques to efficiently model grafting of metal ions onto a non-uniform ensemble of support environments. Our analysis uses a simple model of the quenched disordered support environment, grafting chemistry, and catalytic activity of the resulting grafted sites.