Here, copper electrodes, prepared by reduction of oxidized metallic copper, have been reported to exhibit higher activity for the electrochemical reduction of CO<sub>2</sub> and better selectivity toward C<sub>2</sub> and C<sub>3</sub> (C<sub>2+</sub>) products than metallic copper that has not been preoxidized. We report here an investigation of the effects of four different preparations of oxide-derived electrocatalysts on their activity and selectivity for CO<sub>2</sub> reduction, with particular attention given to the selectivity to C<sub>2+</sub> products. All catalysts were tested for CO<sub>2</sub> reduction in 0.1 M KHCO<sub>3</sub> and 0.1 M CsHCO<sub>3</sub> at applied voltages in the range from –0.7 to –1.0 V vs RHE. The best performing oxide-derived catalysts show up to ~70% selectivity to C<sub>2+</sub> products and only ~3% selectivity to C<sub>1</sub> products at –1.0 V vs RHE when CsHCO<sub>3</sub> is used as the electrolyte. In contrast, the selectivity to C<sub>2+</sub> products decreases to ~56% for the same catalysts tested in KHCO<sub>3</sub>. By studying all catalysts under identical conditions, the key factors affecting product selectivity could be discerned. These efforts reveal that the surface area of the oxide-derived layer is a critical parameter affecting selectivity. A high selectivity to C<sub>2+</sub> products is attained at an overpotential of –1 V vs RHE by operating at a current density sufficiently high to achieve a moderately high pH near the catalyst surface but not so high as to cause a significant reduction in the local concentration of CO<sub>2</sub>. On the basis of recent theoretical studies, a high pH suppresses the formation of C<sub>1</sub> relative to C<sub>2+</sub> products. At the same time, however, a high local CO<sub>2</sub> concentration is necessary for the formation of C<sub>2+</sub> products.

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book deals with heterogeneous catalysis and allows several leading practitioners to describe examples of materials and processes in heterogeneous catalysis under investigation by nuclear magnetic resonance (NMR) techniques. Modern solid-state NMR makes it possible to detect signals from distinguishable sites in molecules and materials and to monitor the connectivities, correlations, and dynamics of these sites. Furthermore, NMR spectroscopy is essentially noninvasive and can be carried out in the presence of gases or liquids over a wide range of temperatures and pressures. While the principal use of NMR spectroscopy is to obtain information about the chemical environment of elements in catalysts or species adsorbed on catalysts, the technique can also be used to characterize atomic and molecular motions. The types of information that may be derived from NMR signals include site identification and intersite correlations.

Roughened copper electrodes, including those derived from cuprous oxide, have long been known to exhibit an enhanced Faradaic efficiency to C2+ products during CO2 electroreduction. However, the source of this enhancement has not been rationalized mechanistically. In this work, we present a theoretical study of roughened copper electrodes derived from cuprous oxide, phosphide, nitride, and sulfide. We utilize a carefully benchmarked effective medium theory potential to develop geometric models of the roughened electrodes on an unprecedented scale. Using density functional theory with an implicit electrolyte, we determine applied bias dependent binding energy distributions for critical reaction intermediates. We apply simple thermodynamic models to evaluate the role of surface roughening on selectivity during CO2 electroreduction. We find that the manner of roughening (i.e., starting from oxide, phosphide, sulfide, or nitride) does not significantly affect the binding energy distributions found, and we suggest design rules to maximize selectivity to C2+ products on copper.

Ab initio calculations suggest that partially lithiated layered transforms to spinel in a two-stage process. In the first stage, a significant fraction of the Mn and Li ions rapidly occupy tetrahedral sites, forming a metastable intermediate. The second stage involves a more difficult coordinated rearrangement of Mn and Li ions to form spinel. This behavior is contrasted to The susceptibility of Mn for migration into the Li layer is found to be controlled by oxidation state, which suggests various means of inhibiting the transformation. These strategies could prove useful in the creation of superior Mn-based cathode materials. © 2001 The Electrochemical Society. All rights reserved.

Advances in quantum chemical methods in combination with exponential growth in the computational speed of computers have enabled researchers in the field of catalysis to apply electronic structure calculations to a wide variety of increasingly complex problems. Such calculations provide insights into why and how changes in the composition and structure of catalytically active sites affect their activity and selectivity for targeted reactions. The aim of this review is to survey the recent advances in the methods used to make quantum chemical calculations and to define transition states as well as to illustrate the application of these methods to a selected series of examples taken from the authors' recent work.

The next generation of alternative fuels is being investigated through advanced chemical and biological production techniques for the purpose of finding suitable replacements to diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels provides a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Recent work has shown that predictive models, in this case artificial neural networks (ANN’s) analyzing quantitative structure property relationships (QSPR’s), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel’s CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present work improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5'-(furan-2-ylmethylene)bis(2-methylfuran), 5,5'-((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl)methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-squared error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furan-based molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model’s predictive accuracy to new molecular classes. The approach, bolstered by the improvements presented in this paper, enables chemists to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.

The ability to efficiently locate transition states is critically important to the widespread adoption of theoretical chemistry techniques for their ability to accurately predict kinetic constants. Existing surface walking techniques to locate such transition states typically require an extremely good initial guess that is often beyond human intuition to estimate. To alleviate this problem, automated techniques to locate transition state guesses have been created that take the known reactant and product endpoint structures as inputs. In this work, we present a simple method to build an approximate reaction path through a combination of interpolation and optimization. Starting from the known reactant and product structures, new nodes are interpolated inwards towards the transition state, partially optimized orthogonally to the reaction path, and then frozen before a new pair of nodes is added. The algorithm is stopped once the string ends connect. For the practical user, this method provides a quick and convenient way to generate transition state structure guesses. Tests on three reactions (cyclization of cis,cis-2,4-hexadiene, alanine dipeptide conformation transition, and ethylene dimerization in a Ni-exchanged zeolite) show that this “freezing string” method is an efficient way to identify complex transition states with significant cost savings over existing methods, particularly when high quality linear synchronous transit interpolation is employed.

The B-site cation ordering of Ba(Mg 1/3 Ta 2/3 )O 3 microwave dielectrics with the complex perovskite structure has been studied using a combination of first-principles calculations, a cluster expansion technique, and Monte Carlo simulations. Our calculations confirm the experimentally observed hexagonal superstructure with space group P3m1 (D 3 3d ) as the ground state. The order-disorder transition between the low-temperature 1:2 ordered hexagonal phase (P3m1) and high-temperature simple perovskite phase (Pm3m) is predicted to occur at ∼3770 K. This indicates that Ba(Mg 1/3 Ta 2/3 )O 3 in equilibrium should be fully ordered at all practical temperatures. Sintering at high temperature for a long time or prolonging the anneal should therefore be effective in enhancing the degree of cation order in Ba(Mg 1/3 Ta 2/3 )O 3 . The charge density distribution and one electron density of states (DOS) for the 1:2 ordered structure indicate that Ta and O atoms possess some degree of covalency with some overlap between the O-2 p orbitals and the Ta-5 d orbitals.

Bipolar membranes (BPMs) possess the potential to optimize pH environments for electrochemical synthesis applications when employed in reverse bias. Unfortunately, the performance of BPMs in reverse bias has long been limited by the rate of water dissociation (WD) occurring at the interface of the BPM. Herein, we develop a continuum model of the BPM that agrees with experiment to understand and enhance WD catalyst performance by considering multiple kinetic pathways for WD in the BPM junction catalyst layer. Here, the model reveals that WD catalysts with a more highly alkaline or acidic pH at the point of zero charge (pH<sub>PZC</sub>) exhibit accelerated WD kinetics because the more acidic or alkaline pH<sub>PZC</sub> catalysts possess greater surface charge, enhancing the local electric field and rate of WD. The model is then employed to explore the sensitivity of the BPM performance to various BPM physical parameters. Finally, the model is used to simulate the operation of bimetallic WD catalysts, demonstrating that an optimal bimetallic catalyst has an acidic pH<sub>PZC</sub> catalyst matched with the cation-exchange layer and an alkaline pH<sub>PZC</sub> catalyst matched with the anion-exchange layer. The study provides insight into the operation of BPM WD catalysts and gives direction toward the development of next-generation WD catalysts for optimal BPM performance under water-splitting and related conditions.

Zeolites are widely used as catalysts for the processing of petroleum to produce transportation fuels, the synthesis of a wide variety of chemicals, and for the abatement of automotive emissions. These applications have stimulated an interest in describing the mechanism and kinetics for zeolite-catalyzed reactions using theoretical methods. This Mini-review summarizes the author's efforts towards this goal. It is shown that accurate predictions of adsorption and activation enthalpies and entropies requires that several criteria be met. The first is a correct description of the structure of the catalytically active center, as well as the portion of the zeolite framework immediately surrounding the active center and that located far from the active center. Second, the level of density functional theory (DFT) must be sufficiently high to account for the effects of dispersive interactions between the adsorbate, the active center, and the immediately surrounding zeolite atoms. Third, dispersive and coulombic interactions between the atoms in the vicinity of the active center and the balance of the zeolite framework must also be accounted for. It is shown that these conditions can be met using hybrid quantum mechanics/molecular mechanics (QM/MM) together with a high-level exchange-correlation functional and a large basis set. The success of our QM/MM approach is illustrated for reactions of light alkanes in H-MFI, as well as other protonated zeolites, and in Ga/H-MFI. We show that for low temperatures (<400 K), the QM/MM approach gives good predictions of molecular adsorption enthalpies and activation enthalpies for elementary reactions. This is also true for higher temperatures (>400 K) if the effects of configuration are considered using a correction obtained from configurationally biased Monte Carlo (CBMC) calculations. Calculations of the molecular adsorption entropy and the activation entropy for elementary reactions are more difficult to predict accurately. Application of the quasi-rigid rotor harmonic approximation overpredicts the loss of entropy of adsorption from the gas phase, particularly for zeolites containing large cavities and channels. CBMC corrections capture this deviation well for molecular adsorption and for early transition states resembling the adsorbed state but are inadequate for late transition states involving two loosely associated fragments.