Author Correction: A rigorous electrochemical ammonia synthesis protocol with quantitative isotope measurements.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

A rigorous electrochemical ammonia synthesis protocol with quantitative isotope measurements.

The electrochemical synthesis of ammonia from nitrogen under mild conditions using renewable electricity is an attractive alternative1-4 to the energy-intensive Haber-Bosch process, which dominates industrial ammonia production. However, there are considerable scientific and technical challenges5,6 facing the electrochemical alternative, and most experimental studies reported so far have achieved only low selectivities and conversions. The amount of ammonia produced is usually so small that it cannot be firmly attributed to electrochemical nitrogen fixation7-9 rather than contamination from ammonia that is either present in air, human breath or ion-conducting membranes9, or generated from labile nitrogen-containing compounds (for example, nitrates, amines, nitrites and nitrogen oxides) that are typically present in the nitrogen gas stream10, in the atmosphere or even in the catalyst itself. Although these sources of experimental artefacts are beginning to be recognized and managed11,12, concerted efforts to develop effective electrochemical nitrogen reduction processes would benefit from benchmarking protocols for the reaction and from a standardized set of control experiments designed to identify and then eliminate or quantify the sources of contamination. Here we propose a rigorous procedure using 15N2 that enables us to reliably detect and quantify the electrochemical reduction of nitrogen to ammonia. We demonstrate experimentally the importance of various sources of contamination, and show how to remove labile nitrogen-containing compounds from the nitrogen gas as well as how to perform quantitative isotope measurements with cycling of 15N2 gas to reduce both contamination and the cost of isotope measurements. Following this protocol, we find that no ammonia is produced when using the most promising pure-metal catalysts for this reaction in aqueous media, and we successfully confirm and quantify ammonia synthesis using lithium electrodeposition in tetrahydrofuran13. The use of this rigorous protocol should help to prevent false positives from appearing in the literature, thus enabling the field to focus on viable pathways towards the practical electrochemical reduction of nitrogen to ammonia.

Number of sites-based solver for determining coverages from steady-state mean-field micro-kinetic models.

Kinetic models parameterized by ab-initio calculations have led to significant improvements in understanding chemical reactions in heterogeneous catalysis. These studies have been facilitated by implementations which determine steady-state coverages and rates of mean-field micro-kinetic models. As implemented in the open-source kinetic modeling program, CatMAP, the conventional solution strategy is to use a root-finding algorithm to determine the coverage of all intermediates through the steady-state expressions, constraining all coverages to be non-negative and to properly sum to unity. Though intuitive, this root-finding strategy causes issues with convergence to solution due to these imposed constraints. In this work, we avoid explicitly imposing these constraints, solving the mean-field steady-state micro-kinetic model in the space of number of sites instead of solving it in the space of coverages. We transform the constrained root-finding problem to an unconstrained least-squares minimization problem, leading to significantly improved convergence in solving micro-kinetic models and thus enabling the efficient study of more complex catalytic reactions.

The Importance of Reaction Energy in Predicting Chemical Reaction Barriers with Machine Learning Models.

Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling - a one-feature linear regression model - has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e. g., a terrace sites on transition metals. For highly branched and energetically shallow reaction networks, such as electrochemical CO2 reduction or wastewater remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we investigate the feasibility of activation barrier prediction without knowledge of the reaction energy using linear and nonlinear machine learning (ML) models trained on a new database of over 500 dehydrogenation activation barriers. We also find that inclusion of the reaction energy significantly improves both classes of ML models, but complex nonlinear models can achieve performance similar to the simplest BEP scaling when predicting activation barriers on new systems. Additionally, inclusion of the reaction energy significantly improves generalizability to new systems beyond the training set. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies through, e. g., the Open Catalyst Project and others, will be an important route to effective model development for more complex systems.

Micro-kinetic model of electrochemical carbon dioxide reduction over platinum in non-aqueous solvents.

The competition between the hydrogen evolution reaction and the electrochemical reduction of carbon dioxide to multi-carbon products is a well-known challenge. In this study, we present a simple micro-kinetic model of these competing reactions over a platinum catalyst under a strong reducing potential at varying proton concentrations in a non-aqueous solvent. The model provides some insight into the mechanism of reaction and suggests that low proton concentration and a high fraction of stepped sites is likely to improve selectivity to multi-carbon products.

Correction: Films of bacteria at interfaces: three stages of behaviour.

Correction for 'Films of bacteria at interfaces: three stages of behaviour' by Liana Vaccari et al., Soft Matter, 2015, 11, 6062-6074.

A theoretical study of the effect of a non-aqueous proton donor on electrochemical ammonia synthesis.

Ammonia synthesis is one of the most studied reactions in heterogeneous catalysis. To date, however, electrochemical N2 reduction in aqueous systems has proven to be extremely difficult, mainly due to the competing hydrogen evolution reaction (HER). Recently, it has been shown that transition metal complexes based on molybdenum can reduce N2 to ammonia at room temperature and ambient pressure in a non-aqueous system, with a relatively small amount of hydrogen output. We demonstrate that the non-aqueous proton donor they have chosen, 2,6-lutidinium (LutH+), is a viable substitute for hydronium in the electrochemical process at a solid surface, since this donor can suppress the HER rate. We also show that the presence of LutH+ can selectively stabilize the *NNH intermediate relative to *NH or *NH2via the formation of hydrogen bonds, indicating that the use of non-aqueous solvents can break the scaling relationship between limiting potential and binding energies.

Films of bacteria at interfaces: three stages of behaviour.

We report an investigation of the formation of films by bacteria at an oil-water interface using a combination of particle tracking and pendant drop elastometry. The films display a remarkably varied series of dynamical and mechanical properties as they evolve over the course of minutes to hours following the creation of an initially pristine interface. At the earliest stage of formation, which we interrogate using dispersions of colloidal probes, the interface is populated with motile bacteria. Interactions with the bacteria dominate the colloidal motion, and the interface displays canonical features of active matter in a quasi-two-dimensional context. This active stage gives way to a viscoelastic transition, presumably driven by the accumulation at the interface of polysaccharides and surfactants produced by the bacteria, which instill the interface with the hallmarks of soft glassy rheology that we characterize with microrheology. Eventually, the viscoelastic film becomes fully elastic with the capability to support wrinkling upon compression, and we investigate this final stage with the pendant drop measurements. We characterize quantitatively the dynamic and mechanical properties of the films during each of these three stages - active, viscoelastic, and elastic - and comment on their possible significance for the interfacial bacterial colony. This work also brings to the forefront the important role that interfacial mechanics may play in bacterial suspensions with free surfaces.

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium.

In heterogeneous catalysis, free energy profiles of reactions govern the mechanisms, rates, and equilibria. Energetics are conventionally computed using the harmonic approximation (HA), which requires determination of critical states a priori. Here, we use neural networks to efficiently sample and directly calculate the free energy surface (FES) of a prototypical heterogeneous catalysis reaction-the dissociation of molecular nitrogen on ruthenium-at density-functional-theory-level accuracy. We find that the vibrational entropy of surface atoms, often neglected in HA for transition metal catalysts, contributes significantly to the reaction barrier. The minimum free energy path for dissociation reveals an "on-top" adsorbed molecular state prior to the transition state. While a previously reported flat-lying molecular metastable state can be identified in the potential energy surface, it is absent in the FES at relevant reaction temperatures. These findings demonstrate the importance of identifying critical points self-consistently on the FES for reactions that involve considerable entropic effects.

Acetonitrile Transition Metal Interfaces from First Principles.

Acetonitrile is among the most commonly used nonaqueous solvents in catalysis and electrochemistry. We study its interfaces with multiple facets of the metals Ag, Cu, Pt, and Rh using density functional theory calculations; the structures reported shed new light on experimental observations and underscore the importance of solvent-solvent interactions at high coverage. We investigate the relationship of potential of zero charge (PZC) to metal work function, reporting results in agreement with experimental measurements. We develop a model to explain the effects of solvent chemisorption and orientation on the PZC to within a mean absolute deviation of 0.08-0.12 V for all facets studied. Our electrostatic field dependent phase diagram agrees with spectroscopic observations and sheds new light on electrostatic field effects. This work provides new insight into experimental observations on acetonitrile metal interfaces and provides guidance for future studies of acetonitrile and other nonaqueous solvent interfaces with transition metals.

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning.

Surface phase diagrams are necessary for understanding surface chemistry in electrochemical catalysis, where a range of adsorbates and coverages exist at varying applied potentials. These diagrams are typically constructed using intuition, which risks missing complex coverages and configurations at potentials of interest. More accurate cluster expansion methods are often difficult to implement quickly for new surfaces. We adopt a machine learning approach to rectify both issues. Using a Gaussian process regression model, the free energy of all possible adsorbate coverages for surfaces is predicted for a finite number of adsorption sites. Our result demonstrates a rational, simple, and systematic approach for generating accurate free-energy diagrams with reduced computational resources. The Pourbaix diagram for the IrO2(110) surface (with nine coverages from fully hydrogenated to fully oxygenated surfaces) is reconstructed using just 20 electronic structure relaxations, compared to approximately 90 using typical search methods. Similar efficiency is demonstrated for the MoS2 surface.