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.

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.

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.

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.

The Materials Provenance Store.

We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

Scaling Relationships and Volcano Plots in Homogeneous Catalysis.

Scaling relations and volcano plots are widely used in heterogeneous catalysis. In this Perspective, we discuss the prospects and challenges associated with the application of similar concepts in homogeneous catalysis using examples from the literature that have appeared recently.

Benchmarking the acceleration of materials discovery by sequential learning.

Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any "good" material, discovery of all "good" materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery.

Automated Image Processing for Spatially Resolved Analysis of Lipid Droplets in Cultured 3T3-L1 Adipocytes.

Cellular hypertrophy of adipose tissue underlies many of the proposed proinflammatory mechanisms for obesity-related diseases. Adipose hypertrophy results from an accumulation of esterified lipids (triglycerides) into membrane-enclosed intracellular lipid droplets (LDs). The coupling between adipocyte metabolism and LD morphology could be exploited to investigate biochemical regulation of lipid pathways by monitoring the dynamics of LDs. This article describes an image processing method to identify LDs based on several distinctive optical and morphological characteristics of these cellular bodies as they appear under bright-field. The algorithm was developed against images of 3T3-L1 preadipocyte cultures induced to differentiate into adipocytes. We show that the calculated lipid volumes are in excellent agreement with enzymatic assay data on total intracellular triglyceride content. We also demonstrate that the image processing method can efficiently characterize the highly heterogeneous spatial distribution of LDs in a culture by showing that differentiation occurs in distinct clusters separated by regions of nearly undifferentiated cells. Prospectively, the LD detection method described in this work could be applied to time-lapse data collected with simple visible light microscopy equipment to quantitatively investigate LD dynamics.