34 research outputs found
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Designing Transition Metal Surfaces for Their Adsorption Properties and Chemical Reactivity
Many technological processes, such as catalysis, electrochemistry, corrosion, and some materials synthesis techniques, involve molecules bonding to and/or reacting on surfaces. For many of these applications, transition metals have proven to have excellent chemical reactivity, and this reactivity is strongly tied to the surface\u27s adsorption properties. This thesis focuses on predicting adsorption properties for use in the design of transition metal surfaces for various applications.
First, it is shown that adsorption through a particular atom (e.g, C or O) can be treated in a unified way. This allows predictions of all C-bound adsorbates from a single, simple adsorbate, such as CH3. In particular, consideration of the adsorption site can improve the applicability of previous approaches, and gas-phase bond energies correlate with adsorption energies for similarly bound adsorbates.
Next, a general framework is presented for understanding and predicting adsorption through any atom. The energy of the adsorbate\u27s highest occupied molecular orbital (HOMO) determines the strength of the repulsion between the adsorbate and the surface. Because adsorbates with similar HOMO energies behave similarly, their adsorption energies correlate. This can improve the efficiency of predictions, but more importantly it constrains catalyst design and suggests strategies for circumventing these constraints. Further, the behavior of adsorbates with dissimilar HOMO energies varies in a systematic way, allowing predictions of adsorption energy differences between any two adsorbates. These differences are also useful in surface design.
In both of these cases, the dependence of adsorption energies on surface electronic properties is explored. This dependence is used to justify the unified treatments mentioned above, and is used to gain further insight into adsorption. The properties of the surface\u27s d band and p band control variations in adsorption energy, as does the strength of the adsorbate-surface coupling. A single equation, with only a single adsorbate-dependent fitting parameter as well as a few universal fitting parameters, is developed that can predict the adsorption energy of any radical on any close-packed transition metal surface. The surface electronic properties that are input into this equation can be estimated based on the alloy structure of the surface, improving prospects for high-throughput screening and rational catalyst design.
The methods discussed in this thesis are used to design a novel catalyst for ethylene epoxidation, which is experimentally synthesized and tested. Initial tests indicate that this catalyst may have improved selectivity over pure A
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Direct visualization of quasi-ordered oxygen chain structures on Au(110)-(1×2)
The Au(110) surface offers unique advantages for atomically-resolved model studies of catalytic oxidation processes on gold. We investigate the adsorption of oxygen on Au(110) using a combination of scanning tunneling microscopy (STM) and density functional theory (DFT) methods. We identify the typical (empty-states) STM contrast resulting from adsorbed oxygen as atomic-sized dark features of electronic origin. DFT-based image simulations confirm that chemisorbed oxygen is generally detected indirectly, from the binding-induced electronic structure modification of gold. STM images show that adsorption occurs without affecting the general structure of the pristine Au(110) missing-row reconstruction. The tendency to form one-dimensional structures is observed already at low coverage (< 0.05 ML), with oxygen adsorbing on alternate sides of the reconstruction ridges. Consistently, calculations yield preferred adsorption on the (111) facets of the reconstruction, on a 3-fold coordination site, with increased stability when adsorbed in chains. Gold atoms with two oxygen neighbors exhibit enhanced electronic hybridization with the O states. Finally, the species observed are reactive to CO oxidation at 200 K and desorption of CO2 leaves a clean and ordered gold surface.Engineering and Applied Science
Density Functional Theory Investigation of Oxidation Intermediates on Gold and Gold-Silver Surfaces
Montemore, Matthew/0000-0002-4157-1745WOS: 000529225800041Gold and gold-silver alloys can be active and selective oxidation catalysts. Previous work has suggested that O-2 dissociation occurs at bimetallic step sites on gold-silver alloys, but the site responsible for the rest of the reaction steps has not been studied. As a first step in gaining insight into this issue, we investigated the adsorption of oxygen and other oxidation intermediates on the (111) and (211) facets of gold-silver alloys using density functional theory. Oxygen and silver coverage effects were analyzed, and different model structures were compared. We also examined the energy barriers for the diffusion of atomic oxygen to gain insight into O migration and spillover. on (111) surfaces, O adsorption is much stronger at low O coverage (less than 0.22 ML), while on (211) surfaces O is strongly bound at both high and low O coverage. O diffusion across the step is faster than diffusion along the step. Ag stabilizes O, both when directly bound to it and when in an adjacent site. Ag also reduces repulsive O-O interactions at low O coverage. Our calculated reaction barriers for O-assisted CH3O dehydrogenation suggest that reaction is faster on steps than on terraces. Overall, our findings suggest that spillover of O from Ag-rich steps to Au-rich terraces does not occur and that oxidation reactions on gold-silver alloys occur on step sites. More specifically, oxidation likely occurs either on Ag-rich step sites or on Au-rich step sites that are adjacent to Ag-rich step sites.U.S. Department of Energy, Office of Science, Basic Energy SciencesUnited States Department of Energy (DOE) [DESC0012573]; DOE Office of Science User Facility (Office of Science of the U.S. Department of Energy)United States Department of Energy (DOE) [DE-AC02-05CH11231]This work was performed as part of Integrated Mesoscale Architectures for Sustainable Catalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award DESC0012573. Computational resources on the Odyssey cluster (FAS Division of Science, Research Computing Group at Harvard University), at the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility (Office of Science of the U.S. Department of Energy, Contract DE-AC02-05CH11231), were used in this work. We gratefully acknowledge Prof. Efthimios Kaxiras for providing us computational resources and helpful discussions
Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability
Factors Controlling Oxophilicity and Carbophilicity of Transition Metals and Main Group Metals
The strength of interaction between a metal and oxygen and/or carbon is a crucial factor for catalytic performance, materials stability, and other important applications. While these are fundamental properties in materials science, there is no general understanding of what makes a metal oxophilic or carbophilic, especially for main group metals. In this work, we elucidate the factors that control how oxophilic or carbophilic a metal is by creating a predictive model and applying it to a variety of data sets for transition metals and main group metals, including DFT-calculated adsorption energies and experimental formation energies. Our model is easily interpretable and accurately describes oxophilic and carbophilic trends across different regions of the periodic table. This model captures the ionic contribution to bonding, the adsorbate-sp contribution to bonding, and the adsorbate-d contribution to bonding by using the reduction potential, matrix coupling elements, band centers, and band filling. For transition metals, the adsorbate-surface d coupling is the major factor that determines oxophilicity relative to carbophilicity. For metals that do not contain d electrons either in their core or valence shell (Li, Be, Na, Mg, Al, K, and Ca), the reduction potential and the adsorbate-surface s coupling are the major factors. As a simple application, we demonstrate the utility of oxophilicity and carbophilicity in rapidly screening metal dopants for improved selectivity for ethylene epoxidation on silver-based catalysts. Using our model, we established a direct relationship between the electronic properties of the metal dopants and their selectivity for ethylene epoxidation. The results suggest that transition metals with high adsorbate-surface d coupling and main group metals with low adsorbate-surface s coupling are good silver-dopant candidates for this reaction. Overall, the improved linkage between a metal’s electronic structure and its interaction with carbon or oxygen will be broadly useful in design of functional materials for a variety of applications
Machine learning approach for screening alloy surfaces for stability in catalytic reaction conditions
A catalytic surface should be stable under reaction conditions to be effective. However, it takes significant effort to screen many surfaces for their stability, as this requires intensive quantum chemical calculations. To more efficiently estimate stability, we provide a general and data-efficient machine learning (ML) approach to accurately and efficiently predict the surface energies of metal alloy surfaces. Our ML approach introduces an element-centered fingerprint (ECFP) which was used as a vector representation for fitting models for predicting surface formation energies. The ECFP is significantly more accurate than several existing feature sets when applied to dilute alloy surfaces and is competitive with existing feature sets when applied to bulk alloy surfaces or gas-phase molecules. Models using the ECFP as input can be quite general, as we created models with good accuracy over a broad set of bimetallic surfaces including most d-block metals, even with relatively small datasets. For example, using the ECFP, we developed a kernel ridge regression ML model which is able to predict the surface energies of alloys of diverse metal combinations with a mean absolute error of 0.017 eV atom ^−1 . Combining this model with an existing model for predicting adsorption energies, we estimated segregation trends of 596 single-atom alloys (SAAs)with and without CO adsorbed on these surfaces. As a simple test of the approach, we identify specific cases where CO does not induce segregation in these SAAs
Hydrocarbon adsorption in an aqueous environment: A computational study of alkyls on Cu(111)
Hydrocarbon chains are important intermediates in various aqueous-phase surface processes, such as CO2 electroreduction, aqueous Fischer-Tropsch synthesis, and aqueous phase reforming of biomass-derived molecules. Further, the interaction between water and adsorbed hydrocarbons represents a difficult case for modern computational methods. Here, we explore various methods for calculating the energetics of this interaction within the framework of density functional theory and explore trade-offs between the use of low water coverages, molecular dynamics approaches, and minima hopping for identification of low energy structures. An effective methodology for simulating low temperature processes is provided by using a unit cell in which the vacuum space is filled with water, employing the minima hopping algorithm to search for low-lying minima, and including dispersion (van der Waals) interactions. Using this methodology, we show that a high coverage of adsorbed alkyls is destabilized by the presence of water, while a low coverage of alkyls is stabilized. Solvation has a small effect on the energetics of hydrocarbon chain growth, generally decreasing its favorability at low temperatures. We studied higher temperatures by running molecular dynamics simulations starting at the minima found by the minima hopping algorithm and found that increased temperatures facilitate chain growth. The self-consistent continuum solvation method effectively describes the alkyl-water interaction and is in general agreement with the explicit solvation results in most cases, but care should be taken at high alkyl coverage. Published by AIP Publishing
Replication Data for: Nonadiabatic Hydrogen Dissociation on Copper Nanoclusters
The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573
A Simple, Accurate Model for Alkyl Adsorption on Late Transition Metals
A simple model that predicts the adsorption energy of
an arbitrary
alkyl in the high-symmetry sites of late transition metal fcc(111)
and related surfaces is presented. The model makes predictions based
on a few simple attributes of the adsorbate and surface, including
the d-shell filling and the matrix coupling element, as well as the
adsorption energy of methyl in the top sites. We use the model to
screen surfaces for alkyl chain-growth properties and to explain trends
in alkyl adsorption strength, site preference, and vibrational softening