(Invited) High-Throughput Synthesis, Evaluation, Analysis, and Data Visualization Technology for Exploring Multi-Components Functional Oxides Powder

High-throughput experimentation is a fast and efficient methodology for materials discovery, screening, and optimization. It has, therefore, garnered a lot of attention from battery researchers as a potential way to accelerate the development of next-generation Li-ion batteries and battery systems beyond Li-ion. However, its successful use in battery materials discovery has been extremely limited due to a variety of challenges including: (i) difficulties in synthesizing mg-scale samples that are comparable to bulk samples made by industrial methods, (ii) limited high-throughput characterization acting like a bottleneck even after high throughput synthesis is developed, and (iii) high cost of commercially available high-throughput instrumentation. Here, we develop high throughput techniques for solid-state electrolytes.First, a novel synthesis approach is used to make 64 mg-scale solid electrolytes simultaneously. The powder samples are prepared by a citrate sol-gel method using an automated dispensing system. These powders were formulated into sixty-four pellets using a home-made high-throughput pellet press die. The samples are then sintered at high temperature to yield the solid electrolytes. The proof of concept here is performed on the Li-La-Ti-O pseudo-ternary system that includes the perovskites Li3xLa2/3-xTiO3, considered candidates for solid-state electrolytes for high energy lithium-ion batteries. Given that vacancies and other defects play an essential role in ionic conductivity, the screening of complete phase diagrams has the potential to reveal phases with optimized defect contents and thus yield improved solid electrolytes.We have therefore developed a high-throughput characterization suite for solid electrolytes. First, we use a high throughput X-ray diffractometer capable of producing Rietveld quality patterns for mg-scale samples in 10 min such that 64 patterns can be obtained in under 12 h. This system is readily used to produce structural phase diagrams in ternary or even quaternary systems. Next, a high throughput electrochemical impedance spectrometer is used to map the ionic conductivities onto the phase diagrams. Finally, high throughput electrochemistry is used to determine the stability window of the electrolytes. All aspects of this workflow are demonstrated on the Li-La-Ti-O system. This now permits the synthesis and full characterization of approximately 192 compositions per week with one researcher only. This approach is well suited to determine the structure-property relations in complex composition spaces.The complete ternary Li-La-Ti phase diagram synthesized at 1150 °C with slow cooling was determined and will be presented in detail. The total (ionic + electronic) conductivity of the phase diagram was mapped by the electrochemical impedance spectroscopy. Quenching was performed to verify that transformations during slow cooling are moderate in this system. Consequences for the development of other solid electrolytes will also be discussed.

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTTranslating the Materials Genome Into Safer Consumer ProductsOladele A. Ogunseitan†‡*, Jaime M. Allgood†‡, Stephanie C. Hammel†, and Julie M. Schoenung§View Author Information† Department of Population Health & Disease Prevention, Program in Public University of California, Irvine, California 92697-3957, United States‡ School of Social Ecology, University of California, Irvine, California 92697, United States§ Department of Chemical Engineering & Materials Science, University of California, Davis, California 95616, United States*Phone: 949-824-0611; fax: 949-824-0529; e-mail: [email protected]Cite this: Environ. Sci. Technol. 2013, 47, 22, 12625–12627Publication Date (Web):October 30, 2013Publication History Received13 September 2013Accepted11 October 2013Revised6 October 2013Published online30 October 2013Published inissue 19 November 2013https://pubs.acs.org/doi/10.1021/es4040864https://doi.org/10.1021/es4040864newsACS PublicationsCopyright © 2013 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views825Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Energy,Genomics,Manufacturing,Materials,Testing and assessment Get e-Alerts

ABSTRACTThe High Throughput Experimentation (HTE) project of the Joint Center for Artificial Photosynthesis (JCAP, http://solarfuelshub.org/) performs accelerated discovery of new earth-abundant photoabsorbers and electrocatalysts. Through collaboration within the DOE solar fuels hub and with the broader research community, the new materials will be utilized in devices that efficiently convert solar energy, water and carbon dioxide into transportation fuels. JCAP-HTE builds high-throughput pipelines for the synthesis, screening and characterization of photoelectrochemical materials. In addition to a summary of these pipelines, we will describe several new screening instruments for high throughput (photo-)electrochemical measurements. These instruments are not only optimized for screening against solar fuels requirements, but also provide new tools for the broader combinatorial materials science community. We will also describe the high throughput discovery, follow-on verification, and device implementation of a new quaternary metal oxide catalyst. This rapid technology development from discovery to device implementation is a hallmark of the multi-faceted JCAP research effort.

The core philosophy of Materials Genome Initiative (MGI) is the transition of the way of new materials design from the traditional “trial-and-error” approach to the in-silico materials design approach which employs intensive computing and material informatics. In June 2011, President Barack Obama launched MGI alongside the Advanced Manufacturing Partnership to help businesses discover, develop and deploy new materials twice as fast. In this paper, the concept of rare and precious genome is presented first, followed by the progress of MGI. After that, we focus on the research status of the rare and precious metals’ MGI including the computational tools, the high-throughput experimental methodologies and the rare and precious metals database. We also introduce the application of MGI in the development of rare and precious metal materials, outline the remaining fundamental challenges and present an outlook on the future of the rare and precious metals’ MGI.

The depletion of traditional fossil fuels and growing energy concerns have led to the development of efficient, carbon-neutral energy conversion technologies powered by renewable sources. Among these, green hydrogen production through electrochemical water splitting is widely considered a promising strategy for energy storage. The proton exchange membrane (PEM) system in various water electrolysis technologies is particularly noteworthy for its high current densities, excellent voltage efficiency, and minimal hydrogen cross-diffusion, bringing it closer to commercialization. However, the efficiency of water splitting is constrained by the slow oxygen evolution reactions (OER), the anodic reaction in the process. Artificial intelligence (AI) is revolutionizing numerous fields, including science and technology, by streamlining complex tasks. High-throughput experiments (HTE), aided by AI, provide an accelerated pathway for catalyst discovery, overcoming the challenges posed by traditional, time-consuming experimental methods. Metal oxide catalysts are attractive for OER due to their low-cost transition metals, high structural stability, and element substitutability. However, the reaction mechanisms of oxides, including reaction pathways, kinetic barriers, and surface reconstruction, highlight the need to explore their structure-performance relationships for deeper insights. The complex chemical space offers countless potential materials for synthesis, which cannot be addressed through traditional reliance on heuristics and extensive testing. Researchers are increasingly utilizing AI to predict material properties, enabling the design of new materials by navigating chemical space. Given the complexity of optimizing materials with multidimensional compositions (potentially exceeding > 200,000 candidates based on pyrochlores), novel experimental methods are essential for advancing effective OER catalysts. This work reveals the relationship among catalyst synthesis, reaction mechanisms, and performance (activity/stability) by establishing a combined AI-HTE platform, which includes a self-built database, a state-of-the-art AI model, and high-throughput (HT) experiments/ calculations. Meanwhile, for the first time, this work successfully implement AI to achieve green hydrogen production and drive renewable energy storage and transition.

Opportunities for catalyst discovery and development: Integrating surface science and theory with high throughput methods

High Throughput Experimentation (HTE) has revolutionized the pharmaceutical industry over the last two decades, developing from a technology for screening compounds against biological targets, to now being used for everything from parallel synthesis of diverse libraries of analogues and optimization of catalytic systems and reaction conditions, to screening for optimal crystallization solvents and API polymorphs. Many pharmaceutical companies (and several academic institutions) have invested in HTE capabilities (often including automation) to support their drug discovery pipelines and fundamental research. With HTE workflows now being firmly established and many successful HTE groups to draw inspiration from, new facilities being set up today have options on where and how to build and integrate new high throughput and automation capabilities into their organizations. This chapter discusses and classifies different options for the build and integration of high throughput experimentation resources into existing chemistry departments.

Combinatorial chemistry has been proven effective in the search for novel functional materials, especially in the field of organic chemistry, and is being used to identify functional inorganic compounds. However, there is a growing need for approaches that predict and experimentally realize new materials, beyond composition optimization of known systems. Application of combinatorial chemistry to materials discovery is typically hindered by a limited ability to search a wide chemical composition space, and by our ability to experimentally screen promising compounds. Here, a combinatorial scheme is proposed that combines a materials informatics technique to define a chemical search space with high-throughput synthesis and evaluation. We identify high-performance superionic conductors in the Ca-(Nb,Ta)-Bi-O system, demonstrating the effectiveness of this approach for accelerated materials discovery.

ABSTRACTSince the launch of the Materials Genome Initiative (MGI) the field of materials informatics (MI) emerged to remove the bottlenecks limiting the pathway towards rapid materials discovery. Although the machine learning (ML) and optimization techniques underlying MI were developed well over a decade ago, programs such as the MGI encouraged researchers to make the technical advancements that make these tools suitable for the unique challenges in materials science and engineering. Overall, MI has seen a remarkable rate in adoption over the past decade. However, for the continued growth of MI, the educational challenges associated with applying data science techniques to analyse materials science and engineering problems must be addressed. In this paper, we will discuss the growing use of materials informatics in academia and industry, highlight the need for educational advances in materials informatics, and discuss the implementation of a materials informatics course into the curriculum to jump-start interested students with the skills required to succeed in materials informatics projects.

The materials genome initiative (MGI) aims to accelerate the process of materials discovery and reduce the time to commercialization of advanced materials. Thus far, the MGI has resulted in significant progress in computational simulation, modeling, and predictions of catalytic materials. However, prodigious amounts of experimental data are necessary to inform and validate these computational models. High-throughput (HT) methodologies, in which hundreds of materials are rapidly synthesized, processed, and characterized for their figure of merit, represent the key experimental enabler of the theoretical aspects of the MGI. HT methodologies have been used since the early 1980s for identifying novel catalyst formulations and optimizing existing catalysts. Many sophisticated screening and data mining techniques have been developed and since the mid-1990s, this approach has become a widely accepted industrial practice. This article will provide a short history of major developments in HT and will discuss screening approaches combining rapid, qualitative primary screens via thin-film techniques with a series of quantitative screens using plug flow reactors. An illustrative example will be provided of one such study in which novel fuel-flexible sulfur tolerant cracking catalysts were developed. We will then illustrate a path forward that leverages existing HT expertise to validate, provide empirical data to and help guide future theoretical studies.

The development of a scalable and highly reproducible in vitro tumor microenvironment (TME) platform still sheds light on new insights into cancer metastasis mechanisms and anticancer therapeutic strategies. Here, we present an all-in-one injection molded plastic array three-dimensional culture platform (All-in-One-IMPACT) that integrates vascularized tumor spheroids for highly reproducible, high-throughput experimentation. This device allows the formation of self-assembled cell spheroids on a chip by applying the hanging drop method to the cell culture channel. Then, when the hydrogel containing endothelial cells and fibroblasts is injected, the spheroid inside the droplet can be patterned together in three dimensions along the culture channel. In just two steps above, we can build a vascularized TME within a defined area. This process does not require specialized user skill and minimizes error-inducing steps, enabling both reproducibility and high throughput of the experiment. We have successfully demonstrated the process, from spheroid formation to tumor vascularization, using patient-derived cancer cells (PDCs) as well as various cancer cell lines. Furthermore, we performed combination therapies with Taxol (paclitaxel) and Avastin (bevacizumab), which are used in standard care for metastatic cancer. The All-in-One IMPACT is a powerful tool for establishing various anticancer treatment strategies through the development of a complex TME for use in high-throughput experiments.

Mining materials knowledge with concept lattice algorithm

Data obtained from high throughput experiments often exhibit complex dependencies among features. These dependencies arise from various sources, including genetic correlation, batch effects, technical replicates, and shared biological pathways. Ignoring these dependencies can lead to inflated false discovery rate (FDR), reduced statistical power, and biased biological interpretations. Properly accounting for these dependencies is crucial for accurate detection of biological signals. We propose a new method called Analysis of Correlated Expressions (ACE) to compare the mean expression of features between two groups. ACE is based on a factor analytic model that accounts for dependence among features and also incorporates heterogeneity of variances between groups, a common feature of high throughput data. Furthermore, ACE does not require the data to be normally distributed. It is scalable and free of any unknown tuning parameters. Extensive simulation studies indicate that it is more powerful than many existing methods while controlling the FDR. Application of ACE to a microRNA dataset, a neuroblastoma gene expression dataset, and a Huntington's disease dataset resulted in some novel findings that were missed by existing methods.

To accelerate the discovery of new or improved homogeneous catalysts, research groups in industry and academia have embraced high throughput experimentation (HTE). Such methodologies consist of preparing and testing large numbers of catalysts in parallel. Homogeneous metal catalysts are very well-suited for HTE, since in many cases, they can be prepared by simply mixing a metal precursor and a ligand. However, an HTE program requires a large set of chemically diverse ligands, i.e. a ligand library. In this review, we describe five different approaches for assembling ligand libraries based on an extensive survey of the literature. These approaches are based on commercial ligands, modular ligands, mixtures of ligands, supramolecular ligands or ligands prepared via the tools of biochemistry.