Block copolymer self-assembly derived mesoporous magnetic materials with three-dimensionally (3D) co-continuous gyroid nanostructure.

Magnetic nanomaterials are gaining interest for their many applications in technological areas from information science and computing to next-generation quantum energy materials. While magnetic materials have historically been nanostructured through techniques such as lithography and molecular beam epitaxy, there has recently been growing interest in using soft matter self-assembly. In this work, a triblock terpolymer, poly(isoprene-block-styrene-block-ethylene oxide) (ISO), is used as a structure directing agent for aluminosilicate sol nanoparticles and magnetic material precursors to generate organic-inorganic bulk hybrid films with co-continuous morphology. After thermal processing into mesoporous materials, results from a combination of small angle X-ray scattering (SAXS) and scanning electron microscopy (SEM) are consistent with the double gyroid morphology. Nitrogen sorption measurements reveal a type IV isotherm with H1 hysteresis, and yield a specific surface area of around 200 m2 g-1 and an average pore size of 23 nm. The magnetization of the mesostructured material as a function of applied field shows magnetic hysteresis and coercivity at 300 K and 10 K. Comparison of magnetic measurements between the mesoporous gyroid and an unstructured bulk magnetic material, derived from the identical inorganic precursors, reveals the structured material exhibits a coercivity of 250 Oe, opposed to 148 Oe for the unstructured at 10 K, and presence of remnant magnetic moment not conventionally found in bulk hematite; both of these properties are attributed to the mesostructure. This scalable route to mesoporous magnetic materials with co-continuous morphologies from block copolymer self-assembly may provide a pathway to advanced magnetic nanomaterials with a range of potential applications.

Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams.

Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA's performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies.

Superconducting Quantum Metamaterials from Convergence of Soft and Hard Condensed Matter Science.

Superconducting quantum metamaterials are expected to exhibit a variety of novel properties, but have been a major challenge to prepare as a result of the lack of appropriate synthetic routes to high-quality materials. Here, the discovery of synthesis routes to block copolymer (BCP) self-assembly-directed niobium nitrides and carbonitrides is described. The resulting materials exhibit unusual structure retention even at temperatures as high as 1000 °C and resulting critical temperature, Tc , values comparable to their bulk analogues. Applying the concepts of soft matter self-assembly, it is demonstrated that a series of four different BCP-directed mesostructured superconductors are accessible from a single triblock terpolymer. Resulting materials display a mesostructure-dependent Tc without substantial variation of the XRD-measured lattice parameters. Finally, field-dependent magnetization measurements of a sample with double-gyroid morphology show abrupt jumps comparable in overall behavior to flux avalanches. Results suggest a fruitful convergence of soft and hard condensed matter science.

Electrochemical Screening of Metallic Oxygen Reduction Reaction Catalyst Thin Films Using Getter Cosputtering.

Current commercial fuel cells operate in acidic media where Pt-containing compositions have been shown to be the best oxygen reduction reaction (ORR) electrocatalysts, due to their facile reaction kinetics and long-term stability under operating conditions. However, with the development of alkaline membranes, alkaline fuel cells have become a potentially viable alternative that offers the possibility of using Pt-free (precious metal-free) electrocatalysts. However, the search for better electrocatalysts can be very effort-consuming, if we intend to test every potential bi- or trimetallic combination. In this work, we have explored the application of physical vapor deposition using a custom-built getter cosputtering chamber to prepare catalyst thin films on glassy carbon electrodes, enabling catalyst compositions to be screened in a combinatorial fashion. The activity of combinations containing Au, Cu, Ag, Rh, and Pd as binary metal catalysts, in alkaline media, was studied using rotating disk electrode (RDE) voltammetry with an exchangeable disk electrode holder. Subsequently, we investigated a composition gradient of Pd-Cu, the best performing bimetallic catalyst thin film identified in the initial screening tests. Our results show the viability of using metal getter cosputtering as a rapid and effective tool for preliminary testing of ORR fuel cell electrocatalysts.

Combinatorial Studies of Palladium-Based Oxygen Reduction Electrocatalysts for Alkaline Fuel Cells.

Hydrogen fuel cells have emerged as promising, potentially renewable energy-based, energy conversion technologies for powering electric vehicles. However, the sluggish oxygen reduction reaction (ORR) at the cathode has remained a longstanding challenge and requires the design of nonplatinum electrocatalysts with high activity and, ideally, low cost. Here, we present a combinatorial study of Pd-Cu thin-film electrodes with well-defined composition and structures, prepared by magnetron sputtering, as a fast method for assessing the ORR activity of binary alloys. This represents a facile catalyst screening method, using replaceable glassy carbon disk electrodes, which enables the rapid and reliable evaluation of ORR activity using standard rotating disk electrode (RDE) measurements. Among nine Pd-Cu alloys, Pd50Cu50 was identified as the most promising composition for the ORR and employed as a target for nanoparticle synthesis. The PdCu nanoparticles, supported on carbon, achieved a mass-specific and surface-specific activity, 3 and 2.5 times, respectively, as high as Pd/C in 1 M KOH. PdCu/C further exhibited an impressive durability with only 3 and 13 mV negative shifts in the half-wave potential after 20000 and 100000 potential cycles, respectively. The combinatorial approach guiding the nanoparticle synthesis, described herein, provides an optimized high-throughput screening method for other binary or ternary alloys as fuel cell electrocatalysts.

Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System.

Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial X-ray diffraction data sets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of X-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their concentration and lattice parameter as a function of composition. By incorporating Gibbs' phase rule into the algorithm, physically meaningful phase maps are obtained with unsupervised operation, and more refined solutions are attained by injecting expert knowledge of the system. The algorithm is demonstrated through investigation of the V-Mn-Nb oxide system where decomposition of eight oxide phases, including two with substantial alloying, provides the first phase map for this pseudoternary system. This phase map enables interpretation of high-throughput band gap data, leading to the discovery of new solar light absorbers and the alloying-based tuning of the direct-allowed band gap energy of MnV2O6. The open-source family of AgileFD algorithms can be implemented into a broad range of high throughput workflows to accelerate materials discovery.

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

High energy x-ray diffraction/x-ray fluorescence spectroscopy for high-throughput analysis of composition spread thin films.

High-throughput crystallography is an important tool in materials research, particularly for the rapid assessment of structure-property relationships. We present a technique for simultaneous acquisition of diffraction images and fluorescence spectra on a continuous composition spread thin film using a 60 keV x-ray source. Subsequent noninteractive data processing provides maps of the diffraction profiles, thin film fiber texture, and composition. Even for highly textured films, our diffraction technique provides detection of diffraction from each family of Bragg reflections, which affords direct comparison of the measured profiles with powder patterns of known phases. These techniques are important for high throughput combinatorial studies as they provide structure and composition maps which may be correlated with performance trends within an inorganic library.

High-throughput combinatorial study of local stress in thin film composition spreads.

We investigate the stresses in thin films with sub-millimeter lateral spatial resolution using a dense array of prefabricated cantilever beams prepared by microelectromechanical-system techniques. Stress induced deflection of the cantilever is interrogated by an optical (laser/position sensitive detector) measurement system. Composition spread films are deposited on the cantilever array using a three gun on-axis magnetron cosputtering system. The position dependent composition is inferred using rate calibrations and verified by electron microprobe/energy dispersive spectroscopy. We demonstrate the function of this system using an Fe-Ni-Al composition spread with approximately 1 at. % resolution. This approach allows for measurement of the composition dependence of other electromechanical properties such as the martensitic phase transition temperature of traditional and ferromagnetic shape-memory alloys, as well as the properties of hydrogen storage materials and the magnetic response of magnetostrictive materials.