Search for phosphors for use in displays and lighting using heuristics‐based combinatorial materials science

Combinatorial materials research projects in Japan

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.

Conventional ‘one by one’ synthesis approach has been a major rate limiting step in the systematic exploration of increasingly complex materials for the demanding new technologies. New concepts of ‘combinatorial chemistry’ are presented for the substantially efficient and cost-effective parallel synthesis and optimization of variety of multicomponent compounds as well as artificially designed lattices and devices. Effectiveness of variety of application areas developed by us is discussed with typical examples and brief review of the merits, challenges, current status and the future directions.

After forever changing the drug discovery process in the pharmaceutical industry, combinatorial chemistry methodologies are increasingly being applied to the discovery and optimization of more efficient catalysts and materials (see picture). With the advent of new combinatorial synthesis and screening technologies, coupled with integrated data management systems, the application of these technologies to materials science and catalyst research holds tremendous potential and brings high expectations to this new and exciting field.

5,6,7,8-Tetrahydroquinazolin-2-(thio)-ones (THQ) fits the class of N-heterocycles as a structural core in numerous bioactive compounds. They promptly extended previous decades. They were significantly recognized in combinatorial chemistry and materials science to determine the drug discovery, antioxidants, and pharmaceuticals fields. In the present work, one-pot multicomponent sustainable synthesis of THQ with easily accessible starting materials, i.e., cyclohexanone, different aromatic aldehydes and (thio)urea, has been performed to determine the proposed Biginelli mechanism that is supported by DFT. It is found that the THQs are synthesized by a mechano-chemical (grinding) tool to achieve a yield of 85.2% within 3.5 min, i.e., YE (% yield/time) 24.34 differs from the conventional method in which lower % yield (YE = 0.72) of THQ was achieved. This confirmed that in the green chemistry principle, the determination of % yield according to saving reaction time must be considered. Moreover, DFT-based antioxidant properties of the THQ were also studied in which the most potent antioxidant compounds were 7b > 6d > 2f. Softness (σ, eV−1) and hardness (η, eV mol−1) can approve the soft molecule that stays more reactive as a result of decreasing the energy gap along heterocyclic with values 0.1491 > 0.1300 > 0.1168 eV−1 one-to-one with the efficiency of antioxidant.

Using combinatorial and high-throughput materials science methods, we have studied thin-film libraries of and alloy negative electrode materials for Li-ion batteries. Over one hundred compositions have been studied carefully by X-ray diffraction and electrochemical methods. The system is found to be amorphous for . For , the amorphous phase coexists with electrochemically inactive crystalline . Amorphous materials with show a specific capacity of , but differential capacity, , vs potential is not stable vs cycling indicating irreversible atomic-scale changes in the alloy, most likely due to tin aggregation. Adding carbon to this system, for example in the library, has a number of positive effects. First, all alloys with are amorphous, with carbon directly incorporated within the amorphous phase. Second, the addition of carbon increases, not decreases, the specific capacity from about for for . Third, compositions with show differential capacity vs potential curves that do not change during charge-discharge cycling, indicating that such alloys are stable on the atomic scale and hence are extremely good candidates for long cycle life. Stability increases with carbon content up to .

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

High-throughput experimentation (HTE) in catalysis and materials science has become a mature technology since the late 1990s. The reasons for the success of HTE are the potential of increased R&D efficiency, which allow shortening of experimental periods, quick systematic insights, and finally faster time-to-market. Key elements of HTE technology are parallelization, fast sequential execution of experimental operations, and implementation of highly automated hardware and software components into HTE. This article contains sections titled: 1. Introduction 1.1. Origin of High-Throughput Experimentation in Catalysis and Materials Science 1.2. Definition and Scope of HTE 1.3. Workflow Development 1.4. Classification of Screening Approaches into Experimental Stages 1.5. Experimental Planning and Use of Complex Advanced Algorithms in HTE 2. Approaches for High-Throughput Synthesis of Heterogeneous Catalysts and Advanced Materials 3. HTE in Catalysis 3.1. HTE in Heterogeneous Catalysis 3.2. Environmental Catalysis 3.3. Petrochemical Applications 3.4. Homogeneous Catalysis 3.4.1. Homogeneous Catalyst Libraries 4. HTE in Materials Science 4.1. Materials for Optical Applications 4.2. Materials for Electronic Applications 4.3. Materials for Magnetic Applications 5. Conclusions and Perspectives

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.

6 - Synthesis and processing

This chapter contains sections titled: Overview Outlook Challenges Summary References

Combinatorial methods are high-efficiency methods to create large composition “libraries” of materials, for example, continuous composition variations, and test those compositions systematically in parallel for specific properties of interest, in contrast to the time-consuming one-composition-at-a-time approach. These methods have captured the attention of the materials industry with the promise of providing new discoveries “faster, better, and cheaper.” However, in the academic community, combinatorial methods often meet with less enthusiasm, perhaps due to the perception of combinatorial methodology as an Edisonian approach to science. The facts are quite to the contrary. In addition to impressive successes arising from the application of combinatorial methods to materials discovery, results coming out of systematic high-throughput investigations of complex materials phenomena (which would be too time-consuming or expensive to undertake) provide data leading to improvement in theories and models of materials chemistry and physics. Indeed, combinatorial methods provide a new paradigm for advancing a central scientific goal—the fundamental understanding of structure–property relationships of materials behavior.

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.

Bioreceptor-free, sensitive and rapid electrochemical detection of patulin fungal toxin, using a reduced graphene oxide@SnO2 nanocomposite.

Design, testing, and performance of a 64-channel combinatorial electrochemical cell are described. This cell is used for high-throughput screening of materials for use as Li-ion rechargeable battery electrodes. Our sealed cell has 64 separate positive electrodes and Li foil for the reference and counter electrodes. This combinatorial electrochemical cell was designed to complement our existing combinatorial materials science infrastructure. Using the combinatorial electrochemical cell decreases the time and labor associated with test cell assembly, improves the repeatability of our assembly procedure, and increases the number of compositions we can test in a given amount of time. In this paper, we demonstrate that the results from the combinatorial electrochemical cell and from conventional 2325 coin-type test cells, containing electrodes of sputtered films prepared in the same sputtering run, are identical for electrodes of the same composition. © 2003 The Electrochemical Society. All rights reserved.