Changwon Suh

A metal-free organic–inorganic aqueous flow battery

As the fraction of electricity generation from intermittent renewable sources—such as solar or wind—grows, the ability to store large amounts of electrical energy is of increasing importance. Solid-electrode batteries maintain discharge at peak power for far too short a time to fully regulate wind or solar power output. In contrast, flow batteries can independently scale the power (electrode area) and energy (arbitrarily large storage volume) components of the system by maintaining all of the electro-active species in fluid form. Wide-scale utilization of flow batteries is, however, limited by the abundance and cost of these materials, particularly those using redox-active metals and precious-metal electrocatalysts. Here we describe a class of energy storage materials that exploits the favourable chemical and electrochemical properties of a family of molecules known as quinones. The example we demonstrate is a metal-free flow battery based on the redox chemistry of 9,10-anthraquinone-2,7-disulphonic acid (AQDS). AQDS undergoes extremely rapid and reversible two-electron two-proton reduction on a glassy carbon electrode in sulphuric acid. An aqueous flow battery with inexpensive carbon electrodes, combining the quinone/hydroquinone couple with the Br2/Br− redox couple, yields a peak galvanic power density exceeding 0.6 W cm−2 at 1.3 A cm−2. Cycling of this quinone–bromide flow battery showed >99 per cent storage capacity retention per cycle. The organic anthraquinone species can be synthesized from inexpensive commodity chemicals. This organic approach permits tuning of important properties such as the reduction potential and solubility by adding functional groups: for example, we demonstrate that the addition of two hydroxy groups to AQDS increases the open circuit potential of the cell by 11% and we describe a pathway for further increases in cell voltage. The use of π-aromatic redox-active organic molecules instead of redox-active metals represents a new and promising direction for realizing massive electrical energy storage at greatly reduced cost.

Combinatorial design of semiconductor chemistry for bandgap engineering: “virtual” combinatorial experimentation

Abstract The objective of this paper is to show how one may design combinatorial libraries a priori by integrating data mining techniques with physically robust multivariate data. It is shown that large datasets can be developed from relatively small amounts of experimental and theoretically based information. This involves a process of strategically selecting appropriate physical based parameters that can be analyzed in a multivariate manner. In this paper we identify for the first time the bandgap and lattice parameters of nearly 200 stoichiometries of new and yet to be synthesized compound chalcopyrite semiconductors. The robustness of this “virtual” combinatorial experimentation approach is demonstrated by comparison to band gap predictions from theoretical studies on a range of compositions for a selected quaternary compound semiconductor.

Analyzing Sparse Data for Nitride Spinels Using Data Mining, Neural Networks, and Multiobjective Genetic Algorithms

Nitride spinels are typically characterized by their unique AB2N4 structure containing a divalent cation A, a trivalent cation B, and an anion N. Numerous such species may exist as metals, semiconductors, or semimetals leading to their extensive usage in diverse scientific and engineering fields. Experimental and theoretical data on the physical or material properties of nitride spinels are, however, severely limited for coming up with a data driven, generic description for their material properties. In this study we have attempted to establish a methodology for handling such sparse data where the various features of some of the state of the art soft computing tools like Genetic Algorithms, Data Mining, and Neural Networks are used in tandem to construct some generic predictive models, in principle applicable to the nitride spinel structures at large, irrespective of their electronic characteristics. The paucity of the available data was circumvented in this work with a data mining strategy, important inputs were identified through an evolving neural net, and finally, the best possible tradeoffs between the bulk moduli and the relative stabilization energies of the nitride spinels were identified by constructing the Pareto-frontier for them through a Genetic Algorithms-based multiobjective optimization strategy.

“Secondary” descriptor development for zeolite framework design: an informatics approach

An analytical foundation for statistically identifying key crystallographic descriptors for zeolite frameworks is presented in this paper. A new set of descriptor formulations containing various aspects of information about zeolite crystal structure is introduced. Selected crystallographic structure data for all known zeolite structure types were analyzed using principal components (PC) and partial least squares in order to understand the statistical distribution and hence help design new zeolites. Critical crystallographic ratios and transformed variables with good correlations to average ring size were calculated. The principal components of the framework descriptors were used to identify the important linear combinations of critical descriptors and to identify statistical outliers. Partial least squares was used to predict the average ring size based on these descriptors. These new secondary descriptors were tested against experimental findings of zeolite analogue structures demonstrating the value of these secondary descriptors as a guide for future combinatorial experiments.

Visualization of High-Dimensional Combinatorial Catalysis Data

The role of various techniques for visualization of high-dimensional data is demonstrated in the context of combinatorial high-throughput experimentation (HTE). Applying visualization tools, we identify which constituents of catalysts are associated with final products in a huge combinatorially generated data set of heterogeneous catalysts, and catalytic activity regions are identified with respect to pentanary composition spreads of catalysts. A radial visualization scheme directly visualizes pentanary composition spreads in two-dimensional (2D) space and catalytic activity of a final product by combining high-throughput results from five slate libraries. A glyph plot provides many possibilities for visualizing high-dimensional data with interactive tools. For catalyst discovery and lead optimization, this work demonstrates how large multidimensional catalysis data sets are visualized in terms of quantitative composition activity relationships (QCAR) to effectively identify the relevant key role of compositions (i.e., lead compositions) of catalysts.

Quantitative analysis and feature recognition in 3-D microstructural data sets

A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

Data Mining and Multivariate Analysis in Materials Science

Databases in materials science applications tend to be phenomenological in nature. In other words, they are built around a taxonomy of specific classes of properties and materials characteristics. In order for databases to serve as more than only a “search and retrieve” infrastructure, and more for a tool for “knowledge discovery”, data bases need to have functional capabilities. The recent advances in genomics and proteomics for instance provide a good example of the development of such “functional” databases. A first step to achieve this is to develop descriptors of materials properties that can be sorted and classified using appropriate data mining algorithms. In this paper we provide some examples of the use of some well established statistical tools to “prepare” such data especially when there is a multi-dimensional component associated with structure- chemistry-property relationships.

Quantitative Structure-Activity Relationships (QSARs) for Materials Science

The field of combinatorial synthesis and “artificial intelligence” in materials science is still in its infancy. In order to develop and accelerated strategy in the discovery of new materials and processes, requires the need to integrate both the experimental aspects of combinatorial synthesis with the computational aspects of information based design of materials. In biology and organic chemistry, this has been accomplished by developing descriptors which help to specify “quantitative structure- activity relationships” at the molecular level. If materials science is to adopt these strategies as well, a similar framework of “QSARs” is required. In this paper, we outline some approaches that can lay the foundations for QSARs in materials science.

Exploring high-dimensional data space: Identifying optimal process conditions in photovoltaics

We demonstrate how advanced exploratory data analysis coupled to data-mining techniques can be used to scrutinize the high-dimensional data space of photovoltaics in the context of thin films of Al-doped ZnO (AZO), which are essential materials as a transparent conducting oxide (TCO) layer in CuInxGa1−xSe2 (CIGS) solar cells. AZO data space, wherein each sample is synthesized from a different process history and assessed with various characterizations, is transformed, reorganized, and visualized in order to extract optimal process conditions. The data-analysis methods used include parallel coordinates, diffusion maps, and hierarchical agglomerative clustering algorithms combined with diffusion map embedding.

Multivariate Analysis for Chemistry-Property Relationships in Molten Salts

Abstract We systematically analyze the molten salt database of Janz to gain a better understanding of the relationship between molten salts and their properties. Due to the multivariate nature of the database, the intercorrelations amongst the molten salts and their properties are often hidden and defining them is challenging. Using principal component analysis (PCA), a data dimensionality reduction technique, we have effectively identified chemistry-property relationships. From the various patterns in the PCA maps, it has been demonstrated that information extracted with PCA not only contains chemistryproperty relationships of molten salts, but also allows us to understand bonding characteristics and mechanisms of transport and melting, which are difficult to otherwise detect.