This project aimed to develop fundamental understanding of the chemistry of NO adsorption and reaction in Pd/zeolites so as to facilitate the rational design of passive NOx adsorber catalysts. The approach adopted combined both experimental and computational methods, which together allow a deeper understanding of the governing chemistry than the use of either method alone. The workflow began with Pd/H-CHA and Pd/H-BEA catalyst synthesis and characterization, in which the Si/Al ratio and Al siting were systematically varied. This was followed by catalyst evaluation using temperature-programed adsorption/desorption methods, as well as in situ spectroscopic measurements to probe the chemistry of NO adsorption. In parallel, the adsorption of NO and other relevant species (H<sub>2</sub>O, CO, HCs) was studied by means of quantum chemical calculations in order to rationalize the experimental data and provide additional insights. Catalyst aging studies were also performed with the aim of elucidating the mechanism of catalyst degradation. Finally, the insights gained in this project were applied to the preparation of an optimized HC/NOx adsorber catalyst, the performance of which was studied using exhaust gas from an engine dynamometer.

Membrane-electrode assemblies utilize ionomer-coated electrocatalysts to achieve facile ion transport. Consequently, isolation of intrinsic catalyst kinetics from measured polarization curves is challenging, as the properties of the catalyst and ionomer both affect the measurements. Here, we employ a Pt microelectrode coated by a thin perfluorosulfonic acid (PFSA) layer to measure polarization curves for the hydrogen oxidation reaction/hydrogen evolution reaction (HER/HOR). Intrinsic electrode kinetics are isolated by theoretical analysis of the local catalyst microenvironment, accounting for mass transport and thermodynamics. The observed enhancements in HER and HOR rates with increasing relative humidity (RH) at the working electrode are attributable to two competing factors: the decrease in activity of H+ in the ionomer and the dominant decrease in the water reorganization energy in the Marcus–Hush–Chidsey representation of HER/HOR kinetics. The increase in intrinsic rate with increasing RH is attributed to increased H+ transfer dynamics resulting from reduced confinement of water within the subnanometer water layer between the catalyst and ionomer as RH increases.

A decade ago, the U.S. chemical industry was in decline. Of the more than 40 chemical manufacturing plants being built worldwide in the mid-2000s with more than $1 billion in capitalization, none were under construction in the United States. Today, as a result of abundant domestic supplies of affordable natural gas and natural gas liquids resulting from the dramatic rise in shale gas production, the U.S. chemical industry has gone from the world’s highest-cost producer in 2005 to among the lowest-cost producers today. The low cost and increased supply of natural gas and natural gas liquids provides an opportunity to discover and develop new catalysts and processes to enable the direct conversion of natural gas and natural gas liquids into value-added chemicals with a lower carbon footprint. The economic implications of developing advanced technologies to utilize and process natural gas and natural gas liquids for chemical production could be significant, as commodity, intermediate, and fine chemicals represent a higher-economic-value use of shale gas compared with its use as a fuel. To better understand the opportunities for catalysis research in an era of shifting feedstocks for chemical production and to identify the gaps in the current research portfolio, the National Academies of Sciences, Engineering, and Medicine conducted an interactive, multidisciplinary workshop in March 2016. The goal of this workshop was to identify advances in catalysis that can enable the United States to fully realize the potential of the shale gas revolution for the U.S. chemical industry and, as a result, to help target the efforts of U.S. researchers and funding agencies on those areas of science and technology development that are most critical to achieving these advances. This publication summarizes the presentations and discussions from the workshop.

We describe catalytic sequences for converting biomass-derived carboxylic acids, to fuels and lubricants that are compatible with the existing energy infrastructure.

Abstract Synthesis of a pentasil‐type zeolite with ultra‐small few‐unit‐cell crystalline domains, which we call FDP (few‐unit‐cell crystalline domain pentasil), is reported. FDP is made using bis‐1,5(tributyl ammonium) pentamethylene cations as structure directing agent (SDA). This di‐quaternary ammonium SDA combines butyl ammonium, in place of the one commonly used for MFI synthesis, propyl ammonium, and a five‐carbon nitrogen‐connecting chain, in place of the six‐carbon connecting chain SDAs that are known to fit well within the MFI pores. X‐ray diffraction analysis and electron microscopy imaging of FDP indicate ca. 10 nm crystalline domains organized in hierarchical micro‐/meso‐porous aggregates exhibiting mesoscopic order with an aggregate particle size up to ca. 5 μm. Al and Sn can be incorporated into the FDP zeolite framework to produce active and selective methanol‐to‐hydrocarbon and glucose isomerization catalysts, respectively.

solid solutions of structure type have been synthesized from homogeneous hydroxide precursors. X‐ray powder diffraction and scanning transmission electron microscopy show that single‐phase solid solutions are formed up to y ≈ 0.5 at 800°C in air. Electrochemical tests using nonaqueous liquid electrolyte cells shows that the open‐circuit voltage and working voltage increase with Al content as predicted by ab initio calculations [Ceder et al., Nature, 392, 694 (1998)], while capacity fade was significant during cycling at room temperature. However, both the absolute capacity and cycleability were improved at 55°C. © 1999 The Electrochemical Society. All rights reserved.

Abstract Sn‐BEA zeolite is known to catalyze the aldose‐to‐ketose isomerization of xylose and glucose; however, the selectivity to pentose and hexose isomers is not stoichiometric, suggesting the formation of other products. In the present study, we have observed near‐complete conversion of all pentose and hexose isomers when xylose and glucose were reacted in the presence of Sn‐BEA at 140 °C and 200 °C, respectively. The previously unidentified products were identified by nuclear magnetic resonance and mass spectrometry to be hydroxyalkanoic acids and their derivatives. The hydroxyl‐rich acids comprise a significant fraction of the converted sugars and are potential monomers for the synthesis of hyper‐crosslinked, biodegradable polymers.

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.