Andrew A. Peterson

How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels

Density functional theory calculations explain coppers unique ability to convert CO2 into hydrocarbons, which may open up (photo-)electrochemical routes to fuels.

Monodisperse Au Nanoparticles for Selective Electrocatalytic Reduction of CO2 to CO

We report selective electrocatalytic reduction of carbon dioxide to carbon monoxide on gold nanoparticles (NPs) in 0.5 M KHCO3 at 25 °C. Among monodisperse 4, 6, 8, and 10 nm NPs tested, the 8 nm Au NPs show the maximum Faradaic efficiency (FE) (up to 90% at -0.67 V vs reversible hydrogen electrode, RHE). Density functional theory calculations suggest that more edge sites (active for CO evolution) than corner sites (active for the competitive H2 evolution reaction) on the Au NP surface facilitates the stabilization of the reduction intermediates, such as COOH*, and the formation of CO. This mechanism is further supported by the fact that Au NPs embedded in a matrix of butyl-3-methylimidazolium hexafluorophosphate for more efficient COOH* stabilization exhibit even higher reaction activity (3 A/g mass activity) and selectivity (97% FE) at -0.52 V (vs RHE). The work demonstrates the great potentials of using monodisperse Au NPs to optimize the available reaction intermediate binding sites for efficient and selective electrocatalytic reduction of CO2 to CO.

Active and Selective Conversion of CO2 to CO on Ultrathin Au Nanowires

In this communication, we show that ultrathin Au nanowires (NWs) with dominant edge sites on their surface are active and selective for electrochemical reduction of CO2 to CO. We first develop a facile seed-mediated growth method to synthesize these ultrathin (2 nm wide) Au NWs in high yield (95%) by reducing HAuCl4 in the presence of 2 nm Au nanoparticles (NPs). These NWs catalyze CO2 reduction to CO in aqueous 0.5 M KHCO3 at an onset potential of -0.2 V (vs reversible hydrogen electrode). At -0.35 V, the reduction Faradaic efficiency (FE) reaches 94% (mass activity 1.84 A/g Au) and stays at this level for 6 h without any noticeable activity change. Density functional theory (DFT) calculations suggest that the excellent catalytic performance of these Au NWs is attributed both to their high mass density of reactive edge sites (≥16%) and to the weak CO binding on these sites. These ultrathin Au NWs are the most efficient nanocatalyst ever reported for electrochemical reduction of CO2 to CO.

Understanding Trends in the Electrocatalytic Activity of Metals and Enzymes for CO2 Reduction to CO.

We develop a model based on density functional theory calculations to describe trends in catalytic activity for CO2 electroreduction to CO in terms of the adsorption energy of the reaction intermediates, CO and COOH. The model is applied to metal surfaces as well as the active site in the CODH enzymes and shows that the strong scaling between adsorbed CO and adsorbed COOH on metal surfaces is responsible for the persistent overpotential. The active site of the CODH enzyme is not subject to these scaling relations and optimizes the relative binding energies of these adsorbates, allowing for an essentially reversible process with a low overpotential.

Insights into CC Coupling in CO2 Electroreduction on Copper Electrodes

We present a first‐principles theoretical study of carbon–carbon coupling in CO2 electroreduction on the copper 2 1 1 surface. Using DFT, we have determined kinetic barriers to the formation of a CC bond between adsorbates derived from CO. The results of our nudged elastic band calculations demonstrate that kinetic barriers to CC coupling decrease significantly with the degree of hydrogenation of reacting adsorbates. We also show that this trend is not affected by the electrical fields present at the solid‐electrolyte interface during electrocatalysis. Our results explain how copper can catalyze the production of higher hydrocarbons and oxygenates in the electrochemical environment, despite producing only single carbon atom products in gas‐phase catalysis, and how CC bonds can be formed at room temperature in the electrochemical environment, whereas substantially higher temperatures are needed in the Fischer–Tropsch catalysis. The unique feature of the electrochemical environment is that the chemical potential of hydrogen (electrons and protons) can be varied through the applied potential. This allows a variation of the degree of hydrogenation of the reactants and thus the activation barrier for CC coupling.

Production of Hydrogen Using Nanocrystalline Protein-Templated Catalysts on M13 Phage

For decades, ethanol has been in use as a fuel for the storage of solar energy in an energy-dense, liquid form. Over the past decade, the ability to reform ethanol into hydrogen gas suitable for a fuel cell has drawn interest as a way to increase the efficiency of both vehicles and stand-alone power generators. Here we report the use of extremely small nanocrystalline materials to enhance the performance of 1% Rh/10% Ni@CeO(2) catalysts in the oxidative steam reforming of ethanol with a ratio of 1.7:1:10:11 (air/EtOH/water/argon) into hydrogen gas, achieving 100% conversion of ethanol at only 300 degrees C with 60% H(2) in the product stream and less than 0.5% CO. Additionally, nanocrystalline 10% Ni@CeO(2) was shown to achieve 100% conversion of ethanol at 400 degrees C with 73% H(2), 2% CO, and 2% CH(4) in the product stream. Finally, we demonstrate the use of biological templating on M13 to improve the resistance of this catalyst to deactivation over 52 h tests at high flow rates (120 000 h(-1) GHSV) at 450 degrees C. This study suggests that the use of highly nanocrystalline, biotemplated catalysts to improve activity and stability is a promising route to significant gains over traditional catalyst manufacture methods.

Finite-Size Effects in O and CO Adsorption for the Late Transition Metals

Gold is known to become significantly more catalytically active as its particle size is reduced, and other catalysts are also known to exhibit finite-size effects. To understand the trends related to finite-size effects, we have used density functional theory to study adsorption of representative adsorbates, CO and O, on the late transition metals Co, Ni, Cu, Ir, Pd, Ag, Rh, Pt and Au. We studied adsorption energies and geometries on 13-atom clusters and compared them to the fcc(111) and fcc(211) crystal facets. In all cases, adsorbates were found to bind significantly more strongly to the 13-atom clusters than to the extended surfaces. The binding strength of both adsorbates were found to correlate very strongly with the average coordination number of the metal atoms to which the adsorbate binds, indicating that the finite-size effects in bonding are not specific to gold.

Global Optimization of Adsorbate–Surface Structures While Preserving Molecular Identity

As the complexity of atomistic simulations in catalysis and surface science increases, the challenge of manually finding the lowest-energy adsorbate–surface geometries grows significantly. In the current work, a global optimization approach that preserves adsorbate identity is applied to enable the automated search for optimized binding geometries. This technique is based on the minima hopping method developed by Goedecker, but is modified to preserve the molecular identity of adsorbates by the application of a new class of Hookean constraints. These constraints are completely inactive when the adsorbate identity is preserved, but act to restore the adsorbate structure via a Hookean force when the bond length exceeds a threshold distance. Additionally, a related Hookean constraint has been developed to prevent adsorbates (particularly such adsorbates as CO and CH2O that have stable gas-phase forms) from volatilizing during the molecular dynamics portion of the minima hopping technique. This combination, referred to herein as the constrained minima hopping method, was tested for its suitability in finding the minimum-energy binding configuration for a set of 17 CxHyOz adsorbates on a stepped Cu fcc(211) surface and in all cases found the global minima in comparable or fewer steps than the previous brute force methodologies. It is expected that methods such as this will be crucial to finding low-energy states in more complex systems, such as those with high coverages of adsorbed species or in the presence of explicit solvent molecules.

Acceleration of saddle-point searches with machine learning.

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

The Influence of Elastic Strain on Catalytic Activity in the Hydrogen Evolution Reaction

Understanding the role of elastic strain in modifying catalytic reaction rates is crucial for catalyst design, but experimentally, this effect is often coupled with a ligand effect. To isolate the strain effect, we have investigated the influence of externally applied elastic strain on the catalytic activity of metal films in the hydrogen evolution reaction (HER). We show that elastic strain tunes the catalytic activity in a controlled and predictable way. Both theory and experiment show strain controls reactivity in a controlled manner consistent with the qualitative predictions of the HER volcano plot and the d-band theory: Ni and Pts activities were accelerated by compression, while Cus activity was accelerated by tension. By isolating the elastic strain effect from the ligand effect, this study provides a greater insight into the role of elastic strain in controlling electrocatalytic activity.