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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Trivalent Rare Earth Adsorption at Phosphonic Acid Monolayers

The increasing need for rare earth separations requires a detailed understanding of trivalent ion behavior at charged aqueous interfaces. Here, neodymium (Nd) adsorption on Langmuir monolayers of octadecylphosphonic acid (ODPA), a single‐chain phosphonic acid capable of double deprotonation, at the air/water interface, is investigated. Combining sum frequency generation (SFG) spectroscopy with X‐ray fluorescence near total reflection (XFNTR), both the interfacial water ordering and ion density are examined. Under ambient conditions, Nd ions induce enhanced deprotonation of ODPA headgroups, leading to interfacial ion densities as high as 1 Nd per 30 Å 2 . This adsorption behavior arises from a complex interplay between direct electrostatic interactions, ion pairing, and hydration effects, which cannot be fully captured by classical Gouy–Chapman–Stern models. These insights into trivalent ion adsorption mechanisms provide a pathway toward more effective separation processes for rare earth metals.

adsorptions↗
DOE OSTI2024

A general Bayesian algorithm for the autonomous alignment of beamlines

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Space-Charge Aberrations in Single-Shot Time-Resolved Transmission Electron Microscopy

In this paper, we discuss effects of space-charge fields on imaging performance in a single-shot time-resolved transmission electron microscope. Using a Green’s function perturbation method, we derive analytical estimates for the effects of space-charge nonlinearity on the image formation process and the associated aberration coefficients. The dependence of these coefficients on the initial beam phase space distribution is elucidated. The results are validated by particle tracking simulations and provide fundamental scaling laws for the trade-off between temporal and spatial resolutions in single-shot time-resolved transmission electron microscopy.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to enhancements in image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Beyond optimization—supervised learning applications in relativistic laser-plasma experiments

We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change caused by a deformable mirror. Machine learning enables feature analysis beyond merely searching for an optimal beam charge, showing that specific aberrations in the laser wavefront are favored in generating higher beam charges. Supervised learning models allow characterizing the measured data quality as well as recognizing irreproducible data and potential outliers. Furthermore, we also include virtual measurement errors in the experimental data to examine the model robustness under these conditions. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of relativistic laser-plasma interaction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

EXPERIMENTAL STUDIES OF NONLINEAR INTEGRABLE OPTICS

State-of-the-art accelerators at energy and intensity frontiers require increasingly bright and powerful particle beams. In conventional linear lattices, intense beams suffer from collective instabilities, resulting in beam losses and maximum beam intensity limits. This thesis presents experimental studies of a novel lattice design concept, the nonlinear integrable optics (NIO), aimed at enhancing beam stability limits with little to no beam dynamics degradation. Single-particle beam dynamics measurements of two NIO devices, the quasi-integrable octupole system and the fully integrable Danilov-Nagaitsev system, were carried out at the purpose-built Integrable Optics Test Accelerator (IOTA) at Fermilab. Their simulation, hardware design, and commissioning process are presented. Extensive model and analysis algorithm development and benchmarking is described. Electron beam data from two scientific runs is analyzed, yielding frequency and phase space dynamics consisten t with m odels. These results demonstrate viability and advantages of the NIO design, providing the groundwork for proton studies in the strong space-charge regime and future integrable accelerators.

Kuklev, Nikita↗
DOE OSTI2025

Ultrafast dynamics of a fermion chain in a terahertz field-driven optical cavity

In this article, we study the effect of a terahertz field-driven single cavity mode for ultrafast control of a fermion chain with dissipation-induced nonlinearity and quadratic coupling to an infrared-active phonon mode. Unlike the first-order phase transition in the nonequilibrium steady state of the system without a cavity or with a high-quality cavity, we find that a realistic dissipation in a low-quality driven cavity process prevents such a transition. Without realistic photon loss from the cavity, the transition only survives for the lower phonon-polariton branch with strong drive; a weak laser field fails to induce the phase transition and renders the polaritons symmetrical. The ability to control the phase transition is crucial for realizing strongly modulated steady states; we propose experimentally feasible regimes where this occurs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Enhancement of terahertz fields in LiTaO 3 waveguides using a conical pulse front

The development of methods for the generation of strong ultrafast electromagnetic pulses in the terahertz (THz) spectral range has led to a surge of progress in nonlinear THz spectroscopy and THz control of molecular and collective responses. For spectroscopy in the 1-THz range, the submillimeter wavelengths and associated large spot sizes, large optical elements, and short distances between final focusing elements and samples can lead to cumbersome experimental setups that are incompatible with some sample environments. Here, we introduce a novel terahertz ring excitation (TREx) optical pumping geometry to generate superposing, focusing fields in planar THz waveguides made out of the electro-optic material lithium tantalate. High THz fields, >175 kV/cm, are generated and measured optically with no free-space THz propagation. The field level achieved by pumping with a sequence of concentric rings of excitation light exceeds by about 20× the result of a single cylindrically focused line of pump light that has been used routinely in previous work. The technique opens new prospects for compact waveguide-based linear and nonlinear THz spectroscopy and signal processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Time Domain Thermoreflectance and Photodeflection Signatures of He Bubbles in Metals

The mechanical properties of the steel and weldments for different components are affected by the formation of helium bubbles as a result of tritium decay. Therefore, periodic analyses of metals exposed to tritium gas are conducted to determine structural defects and temporal damage from tritium gas decay and He precipitation. Transmission electron microscopy (TEM) and autoradiography are the current analytical methods for the evaluation of metal damage. These methods are functional after several years of damage to the crystal lattice where 3He bubble formation damage can be seen with TEM. New analytical methods that can assess the damage to the crystal lattice early during the exposure to tritium are highly sought to ascertain the effects of material processing and tritium interactions. Further, simple sample preparation, compared to TEM, at a significantly reduced cost is highly desirable. A pump-probe laser technique using a femtosecond laser has been demonstrated to measure the thermal diffusivity of different materials. The time domain thermoreflectance (TDTR) laser setup can provide thermal diffusivity and phonon lifetime, which can be correlated with lattice damage. This advanced nonlinear optical technique will enable measurements of nanostructure damage in the metal before and after tritium exposure within a year. This report discusses current efforts in the design and development of the technique and the application to material characterization.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Density Functional Tight-Binding Enables Tractable Studies of Quantum Plasmonics

Routine investigations of plasmonic phenomena at the quantum level present a formidable computational challenge due to the large system sizes and ultrafast time scales involved. This Feature Article highlights the use of density functional tight-binding (DFTB), particularly its real-time time-dependent formulation (RT-TDDFTB), as a tractable approach to study plasmonic nanostructures from a quantum mechanical purview. We begin by outlining the theoretical framework and limitations of DFTB, emphasizing its efficiency in modeling systems with thousands of atoms over picosecond time scales. Applications of RT-TDDFTB are then explored in the context of optical absorption, nonlinear harmonic generation, and plasmon-mediated photocatalysis. We demonstrate how DFTB can reconcile classical and quantum descriptions of plasmonic behavior, capturing key phenomena such as size-dependent plasmon shifts and plasmon coupling in nanoparticle assemblies. Lastly, we showcase DFTB’s ability to model hot carrier generation and reaction dynamics in plasmon-driven H2 dissociation, underscoring its potential to model photocatalytic processes. Collectively, these studies establish DFTB as a powerful, yet computationally efficient tool to probe the emergent physics of materials at the limits of space and time.

Electrical energy↗
DOE OSTI2023

Spatio-temporally shaped deep UV laser source for ultrabright photocathodes using novel upconversion techniques (Final Report)

The goal of this project was to take a VUV laser system designed by Kapteyn-Murnane Labs (KMLabs)—the Hyperion VUV, and increase its functionality, efficiency, and applicability by further probing the fundamental physics taking place in the system and optimizing the system performance. More specifically, DOE accelerator applications—particularly for XFELs that require low-emittance electron beams, require precisely controlled ultrashort pulses in the deep-UV at 250-260 nm to drive the photocathode electron source. This is conventionally done using an ultrafast infrared laser, with subsequent stages of nonlinear upconversion in solid-state nonlinear-optical crystal media (principally BBO, which is commonly used for UV upconversion). However, maintaining high spatial mode beam quality, as well as precisely controlled temporal profile, has proven a persistent challenge. As a possible alternative to this approach, KMLabs has demonstrated a system that generates vacuum ultraviolet light (VUV) by upconverting pulses from an infrared ultrafast laser in a gas-filled hollow waveguide, driving a process of highly cascaded harmonic generation (HCHG). Because HCHG upconversion is done in a gas, rather than a solid, at high intensity in a guided-wave geometry, it retains an excellent beam spatial profile as well as a large spectral bandwidth that allows for control over the temperal structure of the pulse. A challenge, however, is in energy scaling of this HCHG technique to the ~1-20 μJ pulses required for photocathode applications. To-date, the pulse energies used in published HCHG work are sub-10-nJ. This project proposed to explore ways to generate higher-energy pulses using HCHG, as well as to investigate spectral and temporal shaping of the deep UV pulses by pulse shaping the infrared laser used to seed the process. Although we showed that our approach works for generating light at 259 nm, the total pulse energy achieved seems limited to approximately the same value as past work. However, the techniques we investigated may well have benefits for extending this source to much shorter wavelength output.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Anomalously slow hot carrier cooling and insulator-to-metal transition in a photo-doped Mott insulator

Photo-doped Mott insulators can exhibit novel photocarrier transport and relaxation dynamics and non-equilibrium phases. However, time-resolved real-space imaging of these processes are still lacking. Here, we use scanning ultrafast electron microscopy (SUEM) to directly visualize the spatial-temporal evolution of photoexcited species in a spin-orbit assisted Mott insulator α-RuCl 3 . At low optical fluences, we observe extremely long hot photocarrier transport time over one nanosecond, almost an order of magnitude longer than any known values in conventional semiconductors. At higher optical fluences, we observe nonlinear features suggesting a photo-induced insulator-to-metal transition, which is unusual in a large-gap Mott insulator. Our results demonstrate the rich physics in a photo-doped Mott insulator that can be extracted from spatial-temporal imaging and showcase the capability of SUEM to sensitively probe photoexcitations in strongly correlated electron systems.

42 ENGINEERING↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2026

Polarization Control via Artificial Optical Nonlinearity in Dielectric Metasurfaces

Nonlinear optical phenomena are generally governed by geometry in matter systems, as they depend on the spatial arrangement of atoms within materials or molecules. Metasurfaces, through precisely designed geometries on a subwavelength scale, allow the optical response of a material to be tailored far beyond its natural properties. Therefore, metasurfaces are highly appealing for enabling the engineering of nonlinear optical interactions. Current studies of nonlinear metasurfaces predominantly focus on the phase control of the generated light. Nonetheless, investigating the tensorial nature of the nonlinearity of metasurfaces and its effect on the polarization of the generated light is critical to fully unlocking a range of applications, such as nonlinear vector beam generation and nonlinear polarization imaging. Here, we study the artificial optical nonlinearity of a dielectric metasurface originating from its meta-atom symmetry and describe the third-order nonlinear behavior by considering the polarization degree of freedom. We establish an effective nonlinear medium model that serves as a design toolbox for developing amorphous silicon-based geometric metasurfaces with customizable features for third-harmonic generation. We further extract quantitative values of the artificial nonlinear susceptibility tensor elements related to the investigated nonlinear process and geometry. The implemented functional devices demonstrate the versatility of dielectric metasurfaces in shaping the emitted light in terms of amplitude, phase, and polarization for the precise engineering of advanced nonlinear architectures targeting applications in nonlinear imaging and complex light generation.

Diffraction↗
DOE OSTI2023

Four-dimensional dynamics of multirotational transition stimulated rotational Raman scattering in air

Stimulated rotational Raman scattering in air is a powerful parasitic process that degrades high intensity laser beams and pulses propagated over significant distances. Conversely, it is used beneficially in the context of Raman lasers. Through this inelastic scattering process, laser photons are converted to higher (anti-Stokes) or lower (Stokes) energies, according to rotational mode transitions in nitrogen and oxygen diatomic molecules. The full wave-mixing problem involves numerous frequencies, and it is consistently assumed that only one rotational mode contributes to the conversion process. We instead present a dynamic 4D multirotational model that is implemented in a parallelized manner within the Virtual Beamline++ optical modeling package allowing high-resolution 4D studies. We highlight the effect that spontaneous emission plays in large and small beam-width setups, even in the highly saturating regime. The weaker transition modes play a large role in the persistent dynamics and can lead to complex spatiotemporal coupling through nonlinear competition of the modes. We highlight how and why these weaker modes persist, how the size and shape of speckle patterns depends highly on the initial beam profile, and how weaker modes can transiently become stronger as a result of such competition.

47 OTHER INSTRUMENTATION↗
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 OSTI2023

Multi-Functional Applications of H-Glass Embedded with Stable Plasmonic Gold Nanoislands

Metal nanoparticles (MNPs) are synthesized using various techniques on diverse substrates that significantly impact their properties. However, among the substrate materials investigated, the major challenge is the stability of MNPs due to their poor adhesion to the substrate. Herein, it is demonstrated how a newly developed H-glass can concurrently stabilize plasmonic gold nanoislands (GNIs) and offer multifunctional applications. The GNIs on the H-glass are synthesized using a simple yet, robust thermal dewetting process. The H-glass embedded with GNIs demonstrates versatility in its applications, such as i) acting as a room temperature chemiresistive gas sensor (70% response for NO 2 gas); ii) serving as substrates for surface-enhanced Raman spectroscopy for the identifications of Nile blue (dye) and picric acid (explosive) analytes down to nanomolar concentrations with enhancement factors of 4.8 × 10 6 and 6.1 × 10 5 , respectively; and iii) functioning as a nonlinear optical saturable absorber with a saturation intensity of 18.36 × 10 15 W m -2 at 600 nm, and the performance characteristics are on par with those of materials reported in the existing literature. In conclusion, this work establishes a facile strategy to develop advanced materials by depositing metal nanoislands on glass for various functional applications.

36 MATERIALS SCIENCE↗