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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Predictive Deep-Reinforcement-Learning-Based Connected Automated Vehicle Anticipatory Longitudinal Control in a Mixed Traffic Lane Change Condition

Maintaining safety and efficiency for mixed traffic consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is an arduous task due to the inherent HDVs’ stochasticity. Especially for longitudinal control, which is the basic function of vehicle automation, prevailing research primarily considers CAV’s car-following control merely the acceleration and deceleration of leading vehicles. However, this approach overlooks the potential disruptions caused by surrounding vehicles executing lane changes, which can significantly impact the control vehicle’s stability and overall safety. Hence, our study introduces a predictive deep reinforcement learning (DRL) longitudinal CAV controller. This innovative approach leverages prediction from a physics-informed neural network as well as the control capability of DRL to better anticipate and mitigate issues arising from lane-changing, enhancing the safety and efficiency of CAVs in such scenarios. Finally, validated by the numerical simulations embedded with the real-world data, the results indicate that the proposed controller significantly enhances the safety and efficiency of CAVs in situations involving lane changes by other vehicles, showcasing its potential as a valuable tool in advancing CAV technology in mixed traffic.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2002

Dip Spectroscopy of the Low Mass X-Ray Binary XB 1254-690

We observed the low mass X-ray binary XB 1254-690 with the Rossi X-ray Timing Explorer in 2001 May and December. During the first observation strong dipping on the 3.9-hr orbital period and a high degree of variability were observed, along with "shoulders" approx. 15% deep during extended intervals on each side of the main dips. The first observation also included pronounced flaring activity. The non-dip spectrum obtained using the PCA instrument was well-described by a two-component model consisting of a blackbody with kT = 1.30 +/- 0.10 keV plus a cut-off power law representation of Comptonized emission with power law photon index 1.10 +/- 0.46 and a cut-off energy of 5.9(sup +3.0, sub -1.4) keV. The intensity decrease in the shoulders of dipping is energy-independent, consistent with electron scattering in the outer ionized regions of the absorber. In deep dipping the depth of dipping reached 100%, in the energy band below 5 keV, indicating that all emitting regions were covered by absorber. Intensity-selected dip spectra were well-fit by a model in which the point-like blackbody is rapidly covered, while the extended Comptonized emission is progressively overlapped by the absorber, with the, covering fraction rising to 95% in the deepest portion of the dip. The intensity of this component in the dip spectra could be modeled by a combination of electron scattering and photoelectric absorption. Dipping did not occur during the 2001 December observation, but remarkably, both bursting and flaring were observed contemporaneously.

Smale, Alan P.↗
DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗
NASA NTRS2019

Deep Learning Method for Detecting Precursors to Adverse Events

With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.

Matthews, Bryan L.↗
NASA NTRS2019

NASA’s Space Launch System: Enabling a New Generation of Lunar Exploration

Following two decades of operational experience in low-Earth orbit (LEO), NASA has turned its focus once again to deep space exploration. The Agency is building the Space Launch System (SLS) to take astronauts and cargo to the Moon and send robotic spacecraft deep into the solar system. Offering unmatched performance, departure energy and payload capacity, SLS is designed to evolve into progressively more powerful configurations, enabling a new generation of human exploration of the Moon in preparation for future missions to Mars. The first build of the Block 1 vehicle is nearly complete for Exploration Mission-1 (EM-1), the first integrated flight of SLS and the Orion crew vehicle. EM-1 will send an uncrewed Orion to a distant retrograde lunar orbit in order to test and verify new systems, and along the way will deploy 13 6U-class CubeSats in deep space along the upper stage disposal trajectory after separation from Orion. The Agency’s current plans call for the first three missions on the SLS manifest to utilize the Block 1 vehicle in crew and cargo configurations. A more powerful evolved vehicle, Block 1B, will provide additional mass and volume performance using a new Exploration Upper Stage (EUS). Block 1B will lift 34 to 40 metric tons (t) to trans-lunar injection (TLI), depending on crew or cargo configuration. The Block 1B crew configuration will offer as much payload volume as industry-standard 5 m-diameter fairings to co-manifested payloads in a Universal Stage Adapter (USA). The Block 1B cargo variant will accommodate 8.4 meter-diameter fairings in 62.7-foot (19.1 meter) or 90-foot (27.4 meter) lengths. Adding smallsat secondary payloads to ride along with primary and co-manifested payloads on future flights may be possible, depending on mass margins. Leveraging a flight-proven, well-understood propulsion system, SLS’s flexible architecture, unmatched performance and expansive payload accommodations will open exciting new mission possibilities in deep space. Launches of habitat modules for NASA’s new Gateway lunar outpost, the next generation of robotic spacecraft to the far reaches of the solar system, large-aperture deep space telescopes, probes to interstellar space and the return of astronauts to the Moon are all possible with SLS.

Creech, Stephen D.↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Deep learning for in situ data compression of large turbulent flow simulations

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

58 GEOSCIENCES↗
NASA NTRS2018

NASA's Space Launch System: Unprecedented Payload Capabilities

As NASA turns 60 and plans to transition the International Space Station (ISS) and other low-Earth orbit (LEO) activities to commercial enterprises, the Agency's human exploration program turns its focus to deep space. With missions planned to send astronauts back to the Moon and to construct a lunar orbiting Gateway for surface access as well as science experiments and technology demonstrations, NASA requires a vehicle with capabilities for launching more mass and volume than is currently commercially available. To that end, NASA and its private sector partners are building the Space Launch System (SLS) super heavy-lift launch vehicle, which will send the new Orion crew capsule, eventually with a complement of four astronauts, to cislunar space for the first time since the Apollo Program in the 1960s and 1970s. NASA Kennedy Space Center's (KSC's) Exploration Ground Systems (EGS) Program has upgraded and refurbished ground and launch facilities to process, assemble and launch NASA's new deep space exploration system, which is managed by the Exploration Systems Development (ESD) organization in the Human Exploration and Operations Mission Directorate (HEOMD). Offering an unmatched combination of power, payload capacity and departure energy, the evolvable SLS features the world's most proven propulsion system: solid rocket boosters and RS-25 main engines with a modified Delta Cryogenic Second Stage (DCSS) cryogenic upper stage. The initial SLS configuration, Block 1, will deliver at least 26 metric tons (t) to trans-lunar injection (TLI). The second variant, Block 1B, will deliver at least 34 t to TLI in its crew configuration and at least 40 t to TLI in its cargo configuration. The Block 1 cargo vehicle will fly with an industry-standard 5 m fairing while the Block 1B cargo configuration will accommodate 8 m-diameter fairings in varying lengths. The Block 2 vehicle will incorporate upgraded boosters and possibly larger fairings for launching Mars-class payloads to deep space. Although designed to enable human exploration of deep space, the vehicle also provides game-changing benefits for large science payloads and even harnesses excess capacity to provide small satellites with access to deep space. Three flights of the Block 1 vehicle are now planned; the first vehicle, being built for a test flight known as Exploration Mission-1 (EM-1), is nearing completion at NASA and contractor sites across the United States. In fact, hardware for the second mission has also been built. This paper will provide an overview of the SLS vehicle, with a focus on its payload accommodations and the missions enabled by the unprecedented payload volume and departure energy of SLS. This paper also describes the status of the manufacturing and integration for first flight and beyond.

Exploration Mission-1↗
NASA NTRS2021

Low and High Temperature Non-Thermonuclear Fusion Approaches to Energy Production

We are responding to the RFI points A1, A2 and A3. We appreciate the need for a high density, high power, environmentally-benign and carbon-neutral power system. Although fusion has been considered for over 70 years, the nearly intractable means of maintaining a hydrogen plasma at temperatures exceeding the center of the Sun increasingly delays not only commercialization but even the scientific breakeven point. As a recent JASON report notes, the world-wide energy grid is moving towards decentralization which is counter to the large tokamak fusion de-vices necessary given their underdense plasmas. NASA has spent several years understanding and developing Lattice Confinement Fusion as an alternative to thermonuclear fusion by exploring many means of loading and triggering fusion reactions in deuterated metal lattices. Triggering has run the gamut from bremsstrahlung-initiated photo-neutrons to the role of electron screening in assisting these reactions. Electron screening may be responsible for both nuclear effects in deuterium pumped metals as well as fast neutrons observed from electrolytically driven cathodes. Electron screened fusion rate enhancement has been observed by others, including the Lawrence Berkeley National Laboratory. NASA’s need for deep space power drove this research with a goal of a ten-year lifetime for a multi-kWe power system. Previous NASA studies have explored the use of fusion systems for nuclear thermal propulsion for manned spaceflight to the outer planets. Despite these designated uses, small-scale terrestrial power applications as well as medical isotope production are possible once scaling is achieved. ARPA-E can contribute to the Nation’s Green Energy mandates by funding research in this field that builds on condensed matter nuclear science findings by the US Navy and NASA.

L. P. Forsley↗
DOE OSTI2024

Deep Dive on Energy Finance Options for Local Governments [Slides]

This NREL training provides a basic introduction to the options for financing a clean energy project as well as for recovering the investment and generating revenue from the project. Two common clean energy project types will be used as case studies to walk through the process and details of obtaining project funding and recovering the investment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2002

Toward An Understanding of Cluster Evolution: A Deep X-Ray Selected Cluster Catalog from ROSAT

In the past year, we have focussed on studying individual clusters found in this sample with Chandra, as well as using Chandra to measure the luminosity-temperature relation for a sample of distant clusters identified through the ROSAT study, and finally we are continuing our study of fossil groups. For the luminosity-temperature study, we compared a sample of nearby clusters with a sample of distant clusters and, for the first time, measured a significant change in the relation as a function of redshift (Vikhlinin et al. in final preparation for submission to Cape). We also used our ROSAT analysis to select and propose for Chandra observations of individual clusters. We are now analyzing the Chandra observations of the distant cluster A520, which appears to have undergone a recent merger. Finally, we have completed the analysis of the fossil groups identified in ROM observations. In the past few months, we have derived X-ray fluxes and luminosities as well as X-ray extents for an initial sample of 89 objects. Based on the X-ray extents and the lack of bright galaxies, we have identified 16 fossil groups. We are comparing their X-ray and optical properties with those of optically rich groups. A paper is being readied for submission (Jones, Forman, and Vikhlinin in preparation).

Jones, Christine↗
NASA NTRS2020

Implementation Concept of Operation for a Multi-Purpose Cassegrain Solar Concentrator, Micro-Spectrometers, and Electrostatic Neutralizers to Enable In Situ Construction Activities plus Lunar, Planetary, and Deep Space Science Exploration on the Moon

The ability to utilize regolith would support human missions to the Moon and Mars by both stabilizing the surface as well as the use of indigenous resources. Precision landing requirements include surface stabilization to prevent damage or contamination due to regolith projectiles as a result of plume interaction with regolith. The use of indigenous resources rather than hauling materials from Earth appears to be economically a palatable option by converting indigenous resources to usable products. However, such activities have new technical challenges to overcome the issues related to lunar environmental conditions, a wide range of temperature fluctuation, extremely high vacuum, and electrostatically charged fine regolith dusts. For both the regolith sintering and extraction of resources onsite, a Cassegrain solar concentrator was studied for not only sintering lunar regolith into a hardened stabilized surface, but also other multiple applications. This report illustrates a Cassegrain solar concentrator that has multi-functional capabilities for space missions. Proper design and implementation of high-performance lightweight composite materials for the primary mirror of the Cassegrain concentrator can offer multiple capabilities to be performed on the Moon. The multiple applications studied with Cassegrain concentrators are (1) Solar sintering for landing pad and habitats, (2) Harvest of volatiles: H2O, O2, H2, and He-3, (3) Space antenna for telemetry and telecommunication, and (4) Space telescope with 20-meter aperture that exceeds the space telescopes to date in terms of the State-of-Art (SOA) in resolution and aperture diameter. In this study, a key emphasis was placed on the NASA Langley-developed boron nitride nanotube (BNNT) nanocomposite technology which is ideal for the segmented primary mirror structure of the Cassegrain system because it promises a very low coefficient of thermal expansion (CTE) and negligible Poisson ratio. Also, BNNT nanocomposites offer several noticeable benefits, such as light weight, radiation shielding capability, and mechanical strength for structural applications. Additionally, the NASA Langley-developed bullet-like micro-spectrometer and electrostatic power generator were reviewed for mineral mapping applications and electrostatic power generation and dust mitigation from electrostatically charged regolith.

landing pad↗
DOE OSTI2021

Helium tribology of Inconel 617 subjected to laser peening for high temperature nuclear reactor applications

Inconel 617 is among the best candidates for utilization in high temperature gas cooled reactor tribo-components. However, the combined effects of sliding contact, along with intermittent idle times and very high temperature material degradation, deteriorates the alloy tribological performance, especially under a helium atmosphere. Laser peening is a surface treatment technique which can enhance the properties at the surface and subsurface by generating deep residual stresses and enhanced microstructure. In this work, we report the tribological behavior of regular laser peened as well as thermally-engineered laser peened Inconel 617 under helium and air atmospheres at 800 °C. In addition to friction and wear studies, the specimens are characterized by different analytical techniques to further understand the mechanisms involved in the peening process and sliding contact. Regardless of the peening process and post-process treatment types, it is observed that laser peening improves the tribological characteristics of Inconel 617. Interestingly, laser peening followed by helium thermal aging shows highly enhanced tribological behavior. This is attributed to the strengthening effect of the laser peening on the surface oxides providing an excellent and lasting protective and lubricating film under helium exposure.

42 ENGINEERING↗
DOE OSTI2024

Simultaneous reweighting of Transverse Momentum Dependent distributions

The Bayesian reweighting procedure is extended to the case of multiple independent extractions of transverse momentum dependent parton distributions (TMDs). By exploiting the data on transverse single spin asymmetries, A N , for inclusive pion production in polarized proton-proton collisions measured at RHIC, we perform a simultaneous reweighting of the quark Sivers, transversity and Collins TMD functions extracted from semi-inclusive deep inelastic scattering (SIDIS) and annihilation into hadron pairs. The impact of the implementation of the Soffer bound, as well as the differences between older and newer e + e - data, are investigated. The agreement with A N data at large-x F values, a kinematical region complementary to those explored in SIDIS measurements, is enhanced, improving the knowledge of the polarized quark TMDs in the large-x region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Nonenergy Biomass Carbon Removal and Storage (BiCRS): Assessing Durability of Nongaseous Carbon Products Across Terrestrial Storage Fates

Biomass Carbon Removal and Storage, or BiCRS, pathways use plants or algae that remove carbon dioxide from the atmosphere through photosynthesis and store it underground or in long-lived products. While some BiCRS approaches generate an energy product, all BiCRS approaches generate a carbon product. A new subset of BiCRS approaches focus on the storage of these raw or converted carbon products for generation of carbon credits. However, the durability of these approaches is highly variable as carbon products vary widely in their “form” and the conditions of their “fate.” We organize our thinking about carbon products and their durability around these two primary axes. The durability of carbon product “forms” is mediated by chemical recalcitrance and ranges substantially across agricultural residues, municipal solid waste, woody biomass, and nongaseous products of thermochemical conversion (e.g., biochars and bio-oils). Meanwhile, terrestrial storage “fates” vary in the mechanism employed to stall decay, including surface storage, dry storage, shallow anoxic storage, and deep or geologic anoxic storage (or injection). Each mechanism has different implications for suitability with different feedstock forms as well as long-term risks. We present a framework for assessing durability of solid or liquid raw and conversion carbon products under terrestrial storage fates, highlighting knowns, unknowns, and research priorities moving forward.

09 BIOMASS FUELS↗
DOE OSTI2022

Strong Structural and Electronic Coupling in Metavalent PbS Moiré Superlattices

Moiré superlattices are twisted bilayer materials in which the tunable interlayer quantum confinement offers access to new physics and novel device functionalities. Previously, moiré superlattices were built exclusively using materials with weak van der Waals interactions, and synthesizing moiré superlattices with strong interlayer chemical bonding was considered to be impractical. Here, in this study, using lead sulfide (PbS) as an example, we report a strategy for synthesizing moiré superlattices coupled by strong chemical bonding. We use water-soluble ligands as a removable template to obtain free-standing ultrathin PbS nanosheets and assemble them into direct-contact bilayers with various twist angles. Atomic-resolution imaging shows the moiré periodic structural reconstruction at the superlattice interface due to the strong metavalent coupling. Electron energy loss spectroscopy and theoretical calculations collectively reveal the twist-angle-dependent electronic structure, especially the emergent separation of flat bands at small twist angles. The localized states of flat bands are similar to well-arranged quantum dots, promising an application in devices. This study opens a new door to the exploration of deep energy modulations within moiré superlattices alternative to van der Waals twistronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Highly confined epsilon-near-zero and surface phonon polaritons in SrTiO 3 membranes

Recent theoretical studies have suggested that transition metal perovskite oxide membranes can enable surface phonon polaritons in the infrared range with low loss and much stronger subwavelength confinement than bulk crystals. Such modes, however, have not been experimentally observed so far. Here, using a combination of far-field Fourier-transform infrared (FTIR) spectroscopy and near-field synchrotron infrared nanospectroscopy (SINS) imaging, we study the phonon polaritons in a 100 nm thick freestanding crystalline membrane of SrTiO 3 transferred on metallic and dielectric substrates. We observe a symmetric-antisymmetric mode splitting giving rise to epsilon-near-zero and Berreman modes as well as highly confined (by a factor of 10) propagating phonon polaritons, both of which result from the deep-subwavelength thickness of the membranes. Theoretical modeling based on the analytical finite-dipole model and numerical finite-difference methods fully corroborate the experimental results. Our work reveals the potential of oxide membranes as a promising platform for infrared photonics and polaritonics.

36 MATERIALS SCIENCE↗