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Results for “mesostructured materials”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Effect of Anisotropic Confinement on Electronic Structure and Dynamics of Band Edge Excitons in Inorganic Perovskite Nanowires
Inorganic lead halide perovskite nanostructures show promise as the active layers in photovoltaics, light emitting diodes, and other optoelectronic devices. They are robust in the presence of oxygen and water, and the electronic structure and dynamics of these nanostructures can be tuned through quantum confinement. Here we create aligned bundles of CsPbBr3 nanowires with widths resulting in quantum confinement of the electronic wave functions and subject them to ultrafast microscopy. We directly image rapid one-dimensional exciton diffusion along the nanowires, and we measure an exciton trap density of roughly one per nanowire. Using transient absorption microscopy, we observe a polarization-dependent splitting of the band edge exciton line, and from the polarized fluorescence of nanowires in solution, we determine that the exciton transition dipole moments are anisotropic in strength. Furthermore, our observations are consistent with a model in which splitting is driven by shape anisotropy in conjunction with long-range exchange.
Low-loss composite photonic platform based on 2D semiconductor monolayers
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Neuromorphic spintronics
Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here, we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems.
Planar Hall Driven Torque in a Ferromagnet/Nonmagnet/Ferromagnet System
An important goal of spintronics is to covert a charge current into a spin current with a controlled spin polarization that can exert torques on an adjacent magnetic layer. Here we demonstrate such torques in a two ferromagnet system. A CoNi multilayer is used as a spin current source in a sample with structure CoNi/Au/CoFeB. Spin torque ferromagnetic resonance is used to measure the torque on the CoFeB layer. The response as a function of the applied field angle and current is consistent with the symmetry expected for a torque produced by the planar Hall effect originating in CoNi. We find the strength of this effect to be comparable to that of the spin Hall effect in platinum, indicating that the planar Hall effect holds potential as a spin current source with a controllable polarization direction.
Influence of water content on alkali metal chloride transport in cross-linked Poly(ethylene glycol) diacrylate.2. Ion diffusion
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Single and binary ion sorption equilibria of monovalent and divalent ions in commercial ion exchange membranes
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Origin of CO 2 -philic Sorption by Graphene Oxide Layered Nanosheets and Their Derivatives
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Connecting Solute Diffusion to Morphology in Triblock Copolymer Membranes
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Understanding Ion Mobility in P2VP/NMP + I – Polymer Electrolytes: A Combined Simulation and Experimental Study
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The Role of Backbone Polarity on Aggregation and Conduction of Ions in Polymer Electrolytes
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Efficient metal ion sieving in rectifying subnanochannels enabled by metal–organic frameworks
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Inverse methods for design of soft materials
Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.
Consistency and reproducibility in atomic layer deposition
Atomic layer deposition (ALD) is a thin film synthesis technique that can provide exquisite accuracy and precision in film thickness and composition even on complex, large area substrates. Based on self-limiting surface chemistry, ALD can be insensitive to process conditions and reactor designs, allowing an ALD process developed in one lab to be easily reproduced in other labs. In practice, however, ALD is sometimes difficult to reproduce or replicate, and the results can vary substantially between ALD reactors and between labs. This is exemplified by large deviations in reports on the growth of, e.g., Al2O3, FeOx, and TiO2 given the same precursors under similar conditions. Furthermore, the problem of irreproducibility seems to be growing as ALD is adopted by more researchers and integrated into new applications. In this article, the authors highlight some of the major sources of variations and errors and common misconceptions related to ALD. In particular, the authors focus on issues related to precursors, substrates, and deposition tools. The authors illustrate these problems through examples from the literature, and they present results from numerical simulations that describe how nonidealities would manifest in thickness profiles in a typical cross-flow reactor. They also describe how reproducibility in ALD is linked to consistent experimental practice and reporting between labs. The authors’ hope is that by educating newcomers to ALD and advocating for consistent reporting of deposition conditions, they can minimize irreproducibility and enable ALD practitioners to realize the full potential afforded by self-limiting surface chemistry.
Water Treatment: Are Membranes the Panacea?
Alongside the rising global water demand, continued stress on current water supplies has sparked interest in using nontraditional source waters for energy, agriculture, industry, and domestic needs. Membrane technologies have emerged as one of the most promising approaches to achieve water security, but implementation of membrane processes for increasingly complex waters remains a challenge. The technical feasibility of membrane processes replacing conventional treatment of alternative water supplies (e.g., wastewater, seawater, and produced water) is considered in the context of typical and emerging water quality goals. This review considers the effectiveness of current technologies (both conventional and membrane based), as well as the potential for recent advancements in membrane research to achieve these water quality goals. We envision the future of water treatment to integrate advanced membranes (e.g., mixed-matrix membranes, block copolymers) into smart treatment trains that achieve several goals, including fit-for-purpose water generation, resource recovery, and energy conservation.
Water Structure and Properties at Hydrophilic and Hydrophobic Surfaces
The properties of water on both molecular and macroscopic surfaces critically influence a wide range of physical behaviors, with applications spanning from membrane science to catalysis to protein engineering. Yet, our current understanding of water interfacing molecular and material surfaces is incomplete, in part because measurement of water structure and molecular-scale properties challenges even the most advanced experimental characterization techniques and computational approaches. This review highlights progress in the ongoing development of tools working to answer fundamental questions on the principles that govern the interactions between water and surfaces. One outstanding and critical question is what universal molecular signatures capture the hydrophobicity of different surfaces in an operationally meaningful way, since traditional macroscopic hydrophobicity measures like contact angles fail to capture even basic properties of molecular or extended surfaces with any heterogeneity at the nanometer length scale. Resolving this grand challenge will require close interactions between state-of-the-art experiments, simulations, and theory, spanning research groups and using agreed-upon model systems, to synthesize an integrated knowledge of solvation water structure, dynamics, and thermodynamics.
Infrared-active phonon modes in single-crystal thorium dioxide and uranium dioxide
The infrared-active phonon modes, in single-crystal samples of thorium dioxide (ThO 2 ) and uranium dioxide (UO 2 ), were investigated using spectroscopic ellipsometry and compared with density functional theory. Both ThO 2 and UO 2 are found to have one infrared-active phonon mode pair [consisting of one transverse optic (TO) and one associated longitudinal optic (LO) mode], which is responsible for the dominant features in the ellipsometric data. Furthermore, at room temperature, our results for the mode pair’s resonant frequencies and broadening parameters are comparable with previous reflectance spectroscopy characterizations and density functional theory predictions. For ThO 2 , our ellipsometry and density function theory results both show that the LO mode broadening parameter is larger than the TO mode broadening. This signifies mode anharmonicity, which can be attributed to the intrinsic phonon–phonon interaction. In addition to the main mode pair, a broad low-amplitude impurity-like vibrational mode pair is detected within the reststrahlen band for both ThO 2 and UO 2 . Elevated temperature measurements were performed for ThO 2 in order to study the mechanisms by which the phonon parameters evolve with increased heat. The observed change in the TO resonant frequency is in excellent agreement with previous density functional calculations, which only consider volume expansion of the crystal lattice. This suggests that the temperature-dependent change in the TO frequency is primarily due to volume expansion. The change in the main mode pair’s broadening parameters is nearly linear within the temperature range of this study, which indicates the intrinsic anharmonic scattering (via cubic anharmonicities) as the main decay mechanism.
Efficient Langevin dynamics for “noisy” forces
Efficient Boltzmann-sampling using first-principles methods is challenging for extended systems due to the steep scaling of electronic structure methods with the system size. Additionally, stochastic approaches provide a gentler system-size dependency at the cost of introducing “noisy” forces, which could limit the efficiency of the sampling. When the forces are deterministic, the first-order Langevin dynamics (FOLD) offers efficient sampling by combining a well-chosen preconditioning matrix S with a time-step-bias-mitigating propagator [G. Mazzola and S. Sorella, Phys. Rev. Lett. 118, 015703 (2017)]. However, when forces are noisy, S is set equal to the force-covariance matrix, a procedure that severely limits the efficiency and the stability of the sampling. Here, we develop a new, general, optimal, and stable sampling approach for FOLD under noisy forces. We apply it for silicon nanocrystals treated with stochastic density functional theory and show efficiency improvements by an order-of-magnitude.