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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Design and Fabrication of Metamaterial Anti-reflection Coatings for the Simons Observatory

The Simons Observatory (SO) will be a cosmic microwave background (CMB) survey experiment with three small-aperture telescopes and one large-aperture telescope, which will observe from the Atacama Desert in Chile. In total, SO will field over 60,000 transition-edge sensor (TES) bolometers in six spectral bands centered between 27 and 280 GHz in order to achieve the sensitivity necessary to measure or constrain numerous cosmological quantities, as outlined in The Simons Observatory Collaboration et al. (2019). These telescopes require 33 highly transparent, large aperture, refracting optics. To this end, we developed mechanically robust, highly efficient, metamaterial anti-reflection (AR) coatings with octave bandwidth coverage for silicon optics up to 46 cm in diameter for the 22-55, 75-165, and 190-310 GHz bands. We detail the design, the manufacturing approach to fabricate the SO lenses, their performance, and possible extensions of metamaterial AR coatings to optical elements made of harder materials such as alumina.

Joseph E. Golec↗
NASA NTRSDate not supplied

Rapid Lightweight Firmware Architecture of the Mobile Metamaterial Internal Co-Integrator Robot

The Mobile Metamaterial Internal Co-Integrator (MMIC-I) is a structure assembly and servicing robot for in-space servicing, assembly, and manufacturing of primary structures and infrastructure. MMIC-I is a battery-powered crawling robot that can travel through periodic structures such as trusses and open framework mechanical metamaterials. It does this through sequences of component extension, contraction, and gripping. This paper provides a detailed discussion of MMIC-I’s lightweight and rapidly developed firmware architecture, to enable demonstration of robot locomotion, secondary operations, and communications with a central command source. The rationale for the lightweight rapid development approach is to allow for assessment of long term system requirements in parallel with the mechatronics development, including optimization of system and subsystem power densities, to inform a future choice of flight ready software frameworks. MMIC-I system computing and I/O requirements are much lower than what is provided by proven baseline computing hardware for existing flight ready software frameworks such as the core Flight System, F prime, and the Robot Operating System. Development of earth gravity ground demonstration of the robotic systems is greatly benefited by limited power and mass factors for computing hardware. Here, we implement inter-process communication, commanding, and telemetry with the Espressif ESP32 module running the Arduino OS.

Damiana Catanoso↗
DOE OSTI2021

Imaging devices including dielectric metamaterial absorbers and related methods

Imaging devices including dielectric metamaterial absorbers and related methods are disclosed. According to an aspect, an imaging device includes a support. The imaging device also includes multiple dielectric metamaterial absorbers attached to the support. Each absorber includes one or more dielectric resonators configured to generate and emit thermal heat upon receipt of electromagnetic energy.

Padilla, Willie John↗
DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗
DOE OSTI2024

Terahertz Dirac Hyperbolic Metamaterial

Hyperbolic metamaterials (HMMs) are engineered materials with a hyperbolic isofrequency surface, enabling a range of interesting phenomena and applications including negative refraction, enhanced sensing, and subdiffraction imaging, focusing, and waveguiding. Existing HMMs primarily work in the visible and infrared spectral range due to the inherent properties of their constituent materials. Here, we demonstrate a THz-range Dirac HMM using topological insulators as the building blocks. Here, we find that the structure houses up to three high-wavevector volume plasmon polariton (VPP) modes, consistent with transfer matrix modeling and effective medium theory calculations. The VPPs have mode indices greater than 100, significantly larger than observed for VPP modes in HMMs made from metals or doped semiconductors while maintaining comparable quality factors. We attribute these properties to the two-dimensional Dirac nature of the electrons occupying the topological insulator surface states. Because these are van der Waals materials, these structures can be grown at a wafer-scale on a variety of substrates, allowing them to be integrated with existing THz structures and enabling next-generation THz optical devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Here, artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are research fields where DNN results valorize the data driven approach; especially in cases where conventional methods have failed. In view of the great potential of deep learning for the future of artificial electromagnetic materials research, the status of the field with a focus on recent advances, key limitations, and future directions is reviewed. Strategies, guidance, evaluation, and limits of using deep networks for both forward and inverse AEM problems are presented.

14 SOLAR ENERGY↗
DOE OSTI2024

Structural metamaterials with innate capacitive and resistive sensing

Interpenetrating lattices consist of two or more interwoven but physically separate sub-lattices with unique behaviors derived from their multi-body construction. If the sublattices are constructed or coated with an electrically conducting material, the close proximity and high surface area of the electrically isolated conductors allow the two lattices to interact electromagnetically either across the initial dielectric filled gap or through physical contact. Furthermore, changes in the size of the dielectric gap between the sub-lattices induced by deformation can be measured via capacitance or resistance, allowing a structurally competent lattice to operate as a force or deformation sensor. In addition to resistive and capacitive deformation sensing, this work explores capacitance as a fundamental metamaterial property and the environmental sensing behaviors of interpenetrating lattices.

Computational Solid Mechanics↗
DOE OSTI2020

Strain-controlled magnetic ordering in 2D carbon metamaterials

Here, we use ab initio spin-polarized density functional theory to study the magnetic order in a Kagomé-like 2D metamaterial consisting of pristine or substitutionally doped phenalenyl radicals polymerized into a nanoporous, graphene-like structure. In this and in a larger class of related structures, the constituent polyaromatic hydrocarbon molecules can be considered as quantum dots that may carry a net magnetic moment. The structure of this porous system and the coupling between the quantum dots may be changed significantly by applying moderate strain, thus allowing to control the magnetic order and the underlying electronic structure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bio-inspired non self-similar hierarchical elastic metamaterials

Hierarchy provides unique opportunities for the design of advanced materials with superior properties that arise from architecture, rather than from constitutive material response. Contrary to the quasi-static regime, where the potential of hierarchy has been largely explored, its role in vibration mitigation and wave manipulation remains elusive. So far, the majority of the studies concerning hierarchical elastic metamaterials have proposed a self-similar repetition of a specific unit cell at multiple scale levels, leading to the activation of the same bandgap mechanism at different frequencies. On the contrary, here, we show that by designing non self-similar hierarchical geometries allows us to create periodic structures supporting multiple, highly attenuative and broadband bandgaps involving (independently or simultaneously) different scattering mechanisms, namely, Bragg scattering, local resonance and/or inertial amplification, at different frequencies. The type of band gap mechanism is identified and discussed by examining the vibrational mode shapes and the imaginary component of the wavenumber in the dispersion diagram of the unit cell. Finally, we also experimentally confirm this by performing measurements in the lowest frequency regime on a 3D printed structure. Hierarchical design strategies may find application in vibration mitigation for civil, aerospace and mechanical engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strong Coupling in Semiconductor Hyperbolic Metamaterials

Semiconductor-based layered hyperbolic metamaterials (HMMs) house high-wavevector volume plasmon polariton (VPP) modes in the infrared spectral range. VPP modes have successfully been exploited in the weak-coupling regime through the enhanced Purcell effect. In this paper, we experimentally demonstrate strong coupling between the VPP modes in a semiconductor HMM and the intersubband transition of epitaxially embedded quantum wells. We observe clear anticrossings in the dispersion curves for the zeroth-, first-, second-, and third-order VPP modes, resulting in upper and lower polariton branches for each mode. This demonstration sets the stage for the creation of novel infrared optoelectronic structures combining HMMs with embedded epitaxial emitter or detector structures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Laser heating of ferroelectric cubes for tunable electromagnetic properties in metamaterials

In this experimental study, we demonstrate the feasibility of a thermally tunable, resonant dielectric microwave metasurface based on sub-wavelength Mie resonances of ferroelectric dielectric cubes (Calcium titanate, CaTiO 3 ) in which the temperature of the meta-atoms can be locally and dynamically controlled by heating with an optical laser beam over a large range of temperatures and, consequently, values of the dielectric constant of the cube and corresponding resonance frequency of the meta-atoms. Further, this avoids any interference of otherwise necessary heating elements or power supply wires with the propagation of the microwave signal and can be made spatially addressable. The concept scales at least to the THz domain. This contactless approach opens up an alternative avenue to tunability and active control of metamaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inverse design of plasma metamaterial devices with realistic elements

In an expansion of a previous study, we apply inverse design methods to produce two-dimensional plasma metamaterial devices with realistic plasma elements which incorporate quartz envelopes, collisionality (loss), non-uniform density profiles, and resistance to experimental error/perturbation. Finite difference frequency domain simulations are used along with forward-mode differentiation to design waveguides and demultiplexers operating under the transverse magnetic polarization. Additionally, optimal devices with realistic elements are compared to previous devices with idealized elements, and several parameter initialization schemes for the optimization algorithm are explored, yielding a robust procedure for producing such devices. Demultiplexing and waveguiding are demonstrated for microwave-regime devices composed of plasma elements with reasonable space-averaged plasma frequencies ~10GHz and a collision frequency ~1 GHz, allowing for future in-situ training and experimental realization of these designs.

42 ENGINEERING↗
DOE OSTI2021

Inverse Design of Plasma Metamaterial Devices for Optical Computing

We apply inverse-design methods to produce two-dimensional plasma metamaterial (PMM) devices. Backpropagated finite-difference frequency-domain (FDFD) simulations are used to design waveguides and demultiplexers operating under both TE and TM modes. Demultiplexing and waveguiding are demonstrated for devices composed of plasma elements with reasonable plasma densities of approximately 7 GHz, allowing for future in situ training and experimental realization of these designs. We also explore the possible applicability of PMMs to nonlinear boolean operations for use in optical computing. Functionally complete logical connectives (or and and) are achieved in the TM mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Directional infrasound sensing using acoustic metamaterials

Natural and anthropogenic infrasound may travel vast distances, making it an invaluable resource for monitoring phenomena such as nuclear explosions, volcanic eruptions, severe storms, and many others. Typically, these waves are captured using pressure sensors, which cannot encode the direction of arrival—critical information when the source location is not known beforehand. Obtaining this information therefore requires arrays of sensors with apertures ranging from tens of meters to kilometers depending on the wavelengths of interest. This is often impractical in locations that lack the necessary real estate (urban areas, rugged regions, or remote islands); in any case, it requires multiple power, digitizer, and telemetry deployments. In this work, the theoretical basis behind a compact infrasound direction of arrival sensor based on the acoustic metamaterials is presented. This sensor occupies a footprint that is orders of magnitude smaller than the span of a typical infrasound array. The diminutive size of the unit greatly expands the locations where it can be deployed. The sensor design is described, its ability to determine the direction of arrival is evaluated, and further avenues of study are suggested.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

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, T c , 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 T c without substantial variation of the XRDmeasured lattice parameters. Lastly, 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.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tunable, Homoepitaxial Hyperbolic Metamaterials Enabled by High Mobility CdO

We report propagating light exhibits hyperbolicity in strongly anisotropic materials where the principal components of the dielectric tensor are opposite in sign. While hyperbolicity occurs naturally in anisotropic polar dielectrics, wherein optical phonons along orthogonal crystal axes are nondegenerate, such optical anisotropy can also be engineered in hyperbolic metamaterials (HMMs): thin film superlattices of alternating dielectric and metallic layers. Contrasted with the severely limited tunability of natural hyperbolic materials, the hyperbolic behavior of HMMs can be tailored significantly both through superlattice design and material selection. However, so far HMMs have suffered from high optical losses, hindering their performance. In this report, broadly tunable (λ = 2–5 µm) Type I and II hyperbolic modes with low losses (quality (Q)-factors up to 19.7) are observed through attenuated total reflectance measurements of monolithic, homoepitaxial superlattices of high- and low-doped cadmium oxide (CdO). Further, the low losses offered by CdO enable the first demonstration of real-space imaging of hyperbolic plasmon polaritons in nanoresonators by scattering-type scanning near-field optical microscopy—previously only possible for hyperbolic phonon polariton materials. Tunable, low-loss CdO HMMs promise designability for applications such as on-chip photonics, super-resolution imaging (hyperlensing), enhanced emission, novel emitter designs, and possibly quantum nanophotonic and time variant metasurfaces.

36 MATERIALS SCIENCE↗