Bryan R. Goldsmith

Solvent‐Free Conversion of Linalool to Methylcyclopentadiene Dimers: A Route To Renewable High‐Density Fuels

The development of techniques for the efficient synthesis of custom fuels and chemicals from sustainable natural feedstocks is of fundamental importance to society as the direct and indirect costs of petroleum use continue to increase. For general transportation fuels, complex mixtures of molecules that have somewhat lower utility than petroleum-based analogs may be sufficient; however, for specific applications, such as jet and missile propulsion, a more selective model that produces molecules with defined and specialized properties is required. Well-characterized, single-site catalysis is the basis of elegant synthetic strategies for the production of pure compounds. In particular, ruthenium-based olefin metathesis catalysts are known to catalyze a number of reactions, including self-metathesis, cross-metathesis, ring-closing metathesis (RCM), and ring-opening metathesis polymerization (ROMP). This family of catalysts is ubiquitous in the literature and has been applied in many fields of chemistry, ranging from natural product synthesis to polymer chemistry. The transition of these catalysts to large-scale industrial processes has in the past been hindered by their modest turnover numbers and high cost. To overcome these difficulties, catalytic systems that can efficiently yield pure products while maintaining low catalyst concentrations need to be developed. In this report, we detail a ruthenium-catalyzed method for the synthesis of dimethyldicyclopentadiene from linalool, a linear terpene alcohol. Recent work in our laboratory has focused on the conversion of terpenes into high-density fuel surrogates. Although terpenes are naturally produced by pine trees and a variety of plants, a truly sustainable method may require the utilization of bioengineered microbes to produce specific molecules or families of molecules from waste cellulose. Within the terpene family, linalool is a particularly intriguing feedstock for fuels because of its molecular structure. Although RCM of linalool must proceed through a sterically hindered transition state, the reaction is facilitated by coordination of the allylic alcohol. This results in an efficient method for the synthesis of 1-methylcyclopent-2-enol (1) and isobutylene (Scheme 1). Both products are of significant interest as they can be converted to renewable fuel and polymer products. Isobutylene is a valuable side-product that can be selectively trimerized to produce jet fuel, dimerized or alkylated with C4 raffinate to produce high-octane gasoline, or polymerized to polyisobutylene. Meanwhile, 1 is a promising precursor for the synthesis of methylcyclopentadiene dimer, which can be hydrogenated and isomerized to produce the high-density missile fuel RJ-4 (Scheme 1). NMR-scale conversions of linalool to 1 under dilute conditions and at elevated temperatures have been reported in the literature. Catalysts used for this reaction (Figure 1) have included the first-generation Grubbs catalyst (2), both a second-generation Grubbs (5) and Grubbs–Hoveyda catalyst

A Cu25 Nanocluster with Partial Cu(0) Character

Atomically precise copper nanoclusters (NCs) are of immense interest for a variety of applications, but have remained elusive. Herein, we report the isolation of a copper NC, [Cu25H22(PPh3)12]Cl (1), from the reaction of Cu(OAc) and CuCl with Ph2SiH2, in the presence of PPh3. Complex 1 has been fully characterized, including analysis by X-ray crystallography, XANES, and XPS. In the solid state, complex 1 is constructed around a Cu13 centered-icosahedron and formally features partial Cu(0) character. XANES of 1 reveals a Cu K-edge at 8979.6 eV, intermediate between the edge energies of Cu(0) and Cu(I), confirming our oxidation state assignment. This assignment is further corroborated by determination of the Auger parameter for 1, which also falls between those recorded for Cu(0) and Cu(I).

Synthesis and Characterization of a Cu14 Hydride Cluster Supported by Neutral Donor Ligands

The copper hydride clusters [Cu14 H12 (phen)6(PPh3)4][X]2 (X=Cl or OTf; OTf=trifluoromethanesulfonate, phen=1,10-phenanthroline) are obtained in good yields by the reaction of [(Ph3P)CuH]6 with phen, in the presence of a halide or pseudohalide source. The complex [Cu14H12 (phen)6(PPh3)4][Cl]2 reacts with CO2 in CH2Cl2 , in the presence of excess Ph3 P, to form the formate complex [(Ph3P)2Cu(κ(2)-O2CH)], along with [(phen)(Ph3 P)CuCl].

Uncovering structure-property relationships of materials by subgroup discovery

Subgroup discovery (SGD) is presented here as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data. Specifically, we will be concerned with data generated by density-functional theory calculations. At first, we demonstrate that SGD can identify physically meaningful models that classify the crystal structures of 82 octet binary semiconductors as either rocksalt or zincblende. SGD identifies an interpretable two-dimensional model derived from only the atomic radii of valence s and p orbitals that properly classifies the crystal structures for 79 of the 82 octet binary semiconductors. The SGD framework is subsequently applied to 24 400 configurations of neutral gas-phase gold clusters with 5 to 14 atoms to discern general patterns between geometrical and physicochemical properties. For example, SGD helps find that van der Waals interactions within gold clusters are linearly correlated with their radius of gyration and are weaker for planar clusters than for nonplanar clusters. Also, a descriptor that predicts a local linear correlation between the chemical hardness and the cluster isomer stability is found for the even-sized gold clusters.

Water-catalyzed activation of H2O2 by methyltrioxorhenium: A combined computational-experimental study

The formation of peroxorhenium complexes by activation of H2O2 is key in selective oxidation reactions catalyzed by CH3ReO3 (methyltrioxorhenium, MTO). Previous reports on the thermodynamics and kinetics of these reactions are inconsistent with each other and sometimes internally inconsistent. New experiments and calculations using density functional theory with the ωB97X-D and augmented def2-TZVP basis sets were conducted to better understand these reactions and to provide a strong experimental foundation for benchmarking computational studies involving MTO and its derivatives. Including solvation contributions to the free energies as well as tunneling corrections, we compute negative reaction enthalpies for each reaction and correctly predict the hydration state of all complexes in aqueous CH3CN. New rate constants for each of the forward and reverse reactions were both measured and computed as a function of temperature, providing a complete set of consistent activation parameters. New, independent measurements of equilibrium constants do not indicate strong cooperativity in peroxide ligand binding, as was previously reported. The free energy barriers for formation of both CH3ReO2(η(2)-O2) (A) and CH3ReO(η(2)-O2)2(H2O) (B) are predominantly entropic, and the former is much smaller than a previously reported value. Computed rate constants for a direct ligand-exchange mechanism, and for a mechanism in which a water molecule facilitates ligand-exchange via proton transfer in the transition state, differ by at least 7 orders of magnitude. The latter, water-assisted mechanism is predicted to be much faster and is consequently in much closer agreement with the experimentally measured kinetics. Experiments confirm the predicted catalytic role of water: the kinetics of both steps are strongly dependent on the water concentration, and water appears directly in the rate law.

Rate-enhancing roles of water molecules in methyltrioxorhenium-catalyzed olefin epoxidation by hydrogen peroxide

Olefin epoxidation catalyzed by methyltrioxorhenium (MTO, CH3ReO3) is strongly accelerated in the presence of H2O. The participation of H2O in each of the elementary steps of the catalytic cycle, involving the formation of the peroxo complexes (CH3ReO2(η(2)-O2), A, and CH3ReO(η(2)-O2)2(H2O), B), as well as in their subsequent epoxidation of cyclohexene, was examined in aqueous acetonitrile. Experimental measurements demonstrate that the epoxidation steps exhibit only weak [H2O] dependence, attributed by DFT calculations to hydrogen bonding between uncoordinated H2O and a peroxo ligand. The primary cause of the observed H2O acceleration is the strong co-catalytic effect of water on the rates at which A and B are regenerated and consequently on the relative abundances of the three interconverting Re-containing species at steady state. Proton transfer from weakly coordinated H2O2 to the oxo ligands of MTO and A, resulting in peroxo complex formation, is directly mediated by solvent H2O molecules. Computed activation parameters and kinetic isotope effects, in combination with proton-inventory experiments, suggest a proton shuttle involving one or (most favorably) two H2O molecules in the key ligand-exchange steps to form A and B from MTO and A, respectively.

Isolated catalyst sites on amorphous supports: A systematic algorithm for understanding heterogeneities in structure and reactivity

Methods for modeling catalytic sites on amorphous supports lag far behind methods for modeling catalytic sites on metal surfaces, zeolites, and other crystalline materials. One typical strategy for amorphous supports uses cluster models with arbitrarily chosen constraints to model the rigid amorphous support, but these constraints arbitrarily influence catalyst site activity. An alternative strategy is to use no constraints, but this results in catalytic sites with unrealistic flexibility. We present a systematic ab initio method to model isolated active sites on insulating amorphous supports using small cluster models. A sequential quadratic programming framework helps us relate chemical properties, such as the activation energy, to active site structure. The algorithm is first illustrated on an empirical valence bond model energy landscape. We then use the algorithm to model an off-pathway kinetic trap in olefin metathesis by isolated Mo sites on amorphous SiO2. The cluster models were terminated with basis set deficient fluorine atoms to mimic the properties of an extended silica framework. We also discuss limitations of the current algorithm formulation and future directions for improvement.

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery

Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the mean absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.

CHAPTER 9:Understanding Reactivity with Reduced Potential Energy Landscapes: Recent Advances and New Directions

New algorithms for exploring reduced potential energy surfaces (RPES) have the potential to solve some persistent remaining problems in transition state identification, such as the tendency for eigenvector-following and dimer algorithms to repeatedly discover the same transition states. We outline the RPES framework and some advantages of these new algorithms. We then show how an RPES framework can be used to resolve another long-standing challenge: structure–property relationships for isolated sites on amorphous catalysts. By retaining only the peripheral degrees of freedom in a cluster model, and with the reaction coordinates adiabatically optimized, we can systematically generate a series of isolated site models with varying activity. We do this by combining the RPES framework with a sequential non-linear programming algorithm. The algorithm systematically generates a family of low energy sites with varying reactivity and thereby exposes structural differences between highly active and inactive sites on the catalyst surface. We demonstrate this algorithm on a 2D model energy surface, on a simplified ‘chemical’ model, and lastly for ethene polymerization on the Phillips catalyst (Cr/SiO2). The novel approach for understanding catalysts on amorphous supports exemplifies how the powerful RPES framework can enable calculations that were previously intractable.