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Goddard, III, William A.

Publications and source records attributed to Goddard, III, William A..

26 records · Page 2
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DOE OSTI2021

Noncovalent Immobilization of Pentamethylcyclopentadienyl Iridium Complexes on Ordered Mesoporous Carbon for Electrocatalytic Water Oxidation

The attachment of molecular catalysts to conductive supports for the preparation of solid-state anodes is important for the development of devices for electrocatalytic water oxidation. The preparation and characterization of three molecular cyclopentadienyl iridium(III) complexes, Cp*Ir(1-pyrenyl(2-pyridyl)ethanolate-κO,κN)Cl (1) (Cp* = pentamethylcyclopentadienyl), Cp*Ir(diphenyl(2-pyridyl)methanolate-κO,κN)Cl (2), and [Cp*Ir(4-(1-pyrenyl)-2,2'-bipyridine)Cl]Cl (3), as precursors for electrochemical water oxidation catalysts, are reported. These complexes contain aromatic groups that can be attached via noncovalent π-stacking to ordered mesoporous carbon (OMC). The resulting iridium-based OMC materials (Ir-1, Ir-2, and Ir-3) were tested for electrocatalytic water oxidation leading to turnover frequencies (TOFs) of 0.9–1.6 s -1 at an overpotential of 300 mV under acidic conditions. The stability of the materials is demonstrated by electrochemical cycling and X-ray absorption spectroscopy analysis before and after catalysis. Theoretical studies on the interactions between the molecular complexes and the OMC support provide insight onto the noncovalent binding and are in agreement with the experimental loadings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Understanding Reaction Networks through Controlled Approach to Equilibrium Experiments Using Transient Methods

We report a combined experimental/theoretical approach to studying heterogeneous gas/solid catalytic processes using low-pressure pulse response experiments achieving a controlled approach to equilibrium that combined with quantum mechanics (QM)-based computational analysis provides information needed to reconstruct the role of the different surface reaction steps. We demonstrate this approach using model catalysts for ammonia synthesis/decomposition. Polycrystalline iron and cobalt are studied via low-pressure TAP (temporal analysis of products) pulse response, with the results interpreted through reaction free energies calculated using QM on Fe-BCC(110), Fe-BCC(111), and Co-FCC(111) facets. In TAP experiments, simultaneous pulsing of ammonia and deuterium creates a condition where the participation of reactants and products can be distinguished in both forward and reverse reaction steps. This establishes a balance between competitive reactions for D* surface species that is used to observe the influence of steps leading to nitrogen formation as the nitrogen product remains far from equilibrium. Here, the approach to equilibrium is further controlled by introducing delay timing between NH 3 and D 2 which allows time for surface reactions to evolve before being driven in the reverse direction from the gas phase. The resulting isotopic product distributions for NH 2 D, NHD 2 , and HD at different temperatures and delay times and NH 3 /D 2 pulsing order reveal the role of the N 2 formation barrier in controlling the surface concentration of NH x * species, as well as providing information on the surface lifetimes of key reaction intermediates. Conclusions derived for monometallic materials are used to interpret experimental results on a more complex and active CoFe bimetallic catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Sulfur-doped graphene anchoring of ultrafine Au 25 nanoclusters for electrocatalysis

The biggest challenge of exploring the catalytic properties of under-coordinated nanoclusters is the issue of stability. We demonstrate herein that chemical dopants on sulfur-doped graphene (S-G) can be utilized to stabilize ultrafine (sub-2 nm) Au 25 (PET)18 clusters to enable stable nitrogen reduction reaction (NRR) without significant structural degradation. The Au 25 @S-G exhibits an ammonia yield rate of 27.5μgNH 3 ∙ mg Au -1 ∙ h -1 at -0.5 V with faradic efficiency of 2.3%. More importantly, the anchored clusters preserve ~ 80% NRR activity after four days of continuous operation, a significant improvement over the 15% remaining ammonia production rate for clusters loaded on undoped graphene tested under the same conditions. Isotope labeling experiments confirmed the ammonia was a direct reaction product of N 2 feeding gas instead of other chemical contaminations. Ex-situ X-ray photoelectron spectroscopy and X-ray absorption near-edge spectroscopy of post-reaction catalysts reveal that the sulfur dopant plays a critical role in stabilizing the chemical state and coordination environment of Au atoms in clusters. Further ReaxFF molecular dynamics (RMD) simulation confirmed the strong interaction between Au nanoclusters (NCs) and S-G. Furthermore, this substrate-anchoring process could serve as an effective strategy to study ultrafine nanoclusters’ electrocatalytic behavior while minimizing the destruction of the under-coordinated surface motif under harsh electrochemical reaction conditions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

CO 2 reduction on pure Cu produces only H 2 after subsurface O is depleted: Theory and experiment

Significance Electrochemical reduction of CO 2 to fuels and valuable chemicals is a global imperative to address energy and climate challenges. Despite enormous efforts, there is not yet a commercial process. The best catalyst remains Cu metal, but a great deal of controversy revolves around the role of the presence of oxygen in the material. We combine theory and experiments to show that pure Cu without subsurface oxygen leads to no organic products. Thus, maintaining substantial oxygen at Cu surfaces is essential for long-term reduction of CO 2 to organics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

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