Combinatorial Investigation of Magnetostrictive Materials

Combinatorial materials synthesis is a research methodology, which allows one to study a large number of compositionally varying samples simultaneously. We apply this technique in the search for novel multifunctional materials. The work presented here will discuss the combinatorial investigation of novel magnetostrictive materials. In particular, binary Fe-Ga and the ternary Fe-Ga-Al, Fe-Ga-Pd systems are studied. Magnetron co-sputtered composition spread samples of the alloys have been fabricated to study composition dependent trends in magnetostriction. Magnetostriction measurements on all systems studied here have been carried out by optically measuring the deflection of micro-machined cantilever arrays. Measurements of the magnetostriction on binary Fe-Ga thin-films show similar compositional trends as had been reported in bulk systems. The maximum value of magnetostriction observed is 220 ppm, which is comparable to bulk values. A previously unreported minor maximum in magnetostriction as a function of composition has been found for Ga contents of about 4 at%. It is believed that the origin of this minor maximum is related to a peak in the magnetic moment of Fe atoms in Fe-Ga alloys at this composition. We have mapped the Fe-Ga-Pd and Fe-Ga-Al ternary systems. Large regions of the phase diagrams have been mapped out in a single experiment, and the observed magnetostrictive dependence on Ga content matches trends seen in bulk. It was found that the trend of magnetostriction deviated from that of bulk with the inclusion of as little as 1 at% Pd. The addition of up to 10 at % Al to Fe70Ga30 was possible without severe degradation of its magnetostriction

Applications of High Throughput (Combinatorial) Methodologies to Electronic, Magnetic, Optical, and Energy-Related Materials

High throughput (combinatorial) materials science methodology is a relatively new research paradigm that offers the promise of rapid and efficient materials screening, optimization, and discovery. The paradigm started in the pharmaceutical industry but was rapidly adopted to accelerate materials research in a wide variety of areas. High throughput experiments are characterized by synthesis of a “library” sample that contains the materials variation of interest (typically composition), and rapid and localized measurement schemes that result in massive data sets. Because the data are collected at the same time on the same “library” sample, they can be highly uniform with respect to fixed processing parameters. This article critically reviews the literature pertaining to applications of combinatorial materials science for electronic, magnetic, optical, and energy-related materials. It is expected that high throughput methodologies will facilitate commercialization of novel materials for these critically important applications. Despite the overwhelming evidence presented in this paper that high throughput studies can effectively inform commercial practice, in our perception, it remains an underutilized research and development tool. Part of this perception may be due to the inaccessibility of proprietary industrial research and development practices, but clearly the initial cost and availability of high throughput laboratory equipment plays a role. Combinatorialmaterials science has traditionally been focused on materials discovery, screening, and optimization to combat the extremely high cost and long development times for new materialsand their introduction into commerce. Going forward, combinatorial materials science will also be driven by other needs such as materials substitution and experimental verification ofmaterials properties predicted by modeling and simulation, which have recently received much attention with the advent of the Materials Genome Initiative. Thus, the challenge for combinatorial methodology will be the effective coupling of synthesis, characterization and theory, and the ability to rapidly manage large amounts of data in a variety of formats

Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge

With their ability to rapidly elucidate composition-structure-property relationships, high-throughput experimental studies have revolutionized how materials are discovered, optimized, and commercialized. It is now possible to synthesize and characterize high-throughput libraries that systematically address thousands of individual cuts of fabrication parameter space. An unresolved issue remains transforming structural characterization data into phase mappings. This difficulty is related to the complex information present in diffraction and spectroscopic data and its variation with composition and processing. We review the field of automated phase diagram attribution and discuss the impact that emerging computational approaches will have in the generation of phase diagrams and beyond

A High Throughput Aqueous Passivation Testing Methodology for Compositionally Complex Alloys using Scanning Droplet Cell

Compositionally complex alloy systems containing more than five principal elements allow exploring a wide range of compositions, processing, and structural variables with the hope for identifying unique properties. Such opportunities also apply to designing materials for improved corrosion resistance, regulated by a self-healing passive film. Such a rich landscape in reactivity and protectivity demands the search for high-throughput experimental testing workflows to uncover key metrics, indicative of superior properties. In this communication, one such methodology is demonstrated for evaluating passivation performance of a combinatorial library of Al0.7-x-yCoxCryFe0.15Ni0.15 thin film alloys in deaerated 0.1 mol/L H2SO4(aq), using a scanning droplet cell

On the redundancy in large material datasets: efficient and robust learning with less data

Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95 % of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the "bigger is better" mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume.Comment: Main text: 10 pages, 2 tables, 5 figures. Supplemental information: 29 pages, 1 table, 23 figure

One-Step Production of Long-Chain Hydrocarbons from Waste-Biomass-Derived Chemicals using Bi-Functional Heterogeneous Catalysts

n this study, we demonstrate the production of long-chain hydrocarbons (C8+) from 2-methylfuran (2MF) and butanal in a single step reactive process by utilizing a bi-functional catalyst with both acid and metallic sites. Our approach utilizes a solid acid for the hydroalkylation function and as a support as well as a transition metal as hydrodeoxygenation catalyst. A series of solid acids was screened, among which MCM-41 demonstrated the best combination of activity and stability. Platinum nanoparticles were then incorporated into the MCM-41. The Pt/MCM-41 catalyst showed 96% yield for C8+ hydrocarbons and the catalytic performance was stable over four reaction cycles of 20 hour each. The reaction pathways for the production of long-chain hydrocarbons is probed with a combination of infrared spectroscopy and steady-state reaction experiments. It is proposed that 2MF and butanal go through hydroalkylation first on the acid site followed by hydrodeoxygenation to produce the hydrocarbon fuels

Optical Cell for Combinatorial \u3ci\u3eIn Situ\u3c/i\u3e Raman Spectroscopic Measurements of Hydrogen Storage Materials at High Pressures and Temperatures

An optical cell is described for high-throughput backscattering Raman spectroscopic measurements of hydrogen storagematerials at pressures up to 10 MPa and temperatures up to 823 K. High throughput is obtained by employing a 60 mm diameter × 9 mm thick sapphire window, with a corresponding 50 mm diameter unobstructed optical aperture. To reproducibly seal this relatively large window to the cell body at elevated temperatures and pressures, a gold o-ring is employed. The sample holder-to-window distance is adjustable, making this cell design compatible with optical measurement systems incorporating lenses of significantly different focal lengths, e.g., microscope objectives and single element lenses. For combinatorial investigations, up to 19 individual powder samples can be loaded into the optical cell at one time. This cell design is also compatible with thin-film samples. To demonstrate the capabilities of the cell,in situ measurements of the Ca(BH4)2 and nano-LiBH4–LiNH2–MgH2hydrogen storage systems at elevated temperatures and pressures are reported

Self-Healing Catalysts: CO\u3csub\u3e3\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e Nanorods for Fischer-Tropsch Synthesis

We combine kinetic and spectroscopic data to demonstrate the concept of a self-healing catalyst, which effectively eliminates the need for catalyst regeneration. The observed self-healing is triggered by controlling the crystallographic orientation at the catalyst surface