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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Geometry Optimization of Cable-Based Actuation for Small-Scale Model Testing of a Floating Marine Turbine: Preprint

This research aims to apply combined wave and tidal current loads to a small-scale floating marine current turbine in a wave tank, where an actuation system applies hydrodynamic and mooring forces on the hardware based on results from a simulation. We use a real-time hybrid test setup with physical wave forcing from the wave tank and simulated current and mooring forces implemented through a tensioned cable array. For this paper, we developed an optimization algorithm that adjusts the hardware geometry of the cable array to achieve more efficient tension allocation across the cables for the loads that need to be actuated. By adjusting the points where the cables are attached to the floating platform and the angles between the platform and the winches, an optimal cable geometry can be found to minimize tension variations in the lines, maintain the desired pretension, and prevent excessive tensions. We present the optimization problem formulation, the actuation system evaluation approach, and optimization results that show effective cable actuation setups that are being considered for implementation in the wave tank tests.

cable-based actuation↗
DOE OSTI2020

Unraveling the Nature of Excellent Potassium Storage in Small-Molecule Se@Peapod-Like N-Doped Carbon Nanofibers

The potassium-selenium (K-Se) battery is considered as an alternative solution for stationary energy storage because of abundant resource of K. However, the detailed mechanism of the energy storage process is yet to be unraveled. In this work, the findings in probing the working mechanism of the K-ion storage in Se cathode are reported using both experimental and computational approaches. A flexible K-Se battery is prepared by employing the small-molecule Se embedded in freestanding N -doped porous carbon nanofibers thin film (Se@NPCFs) as cathode. The reaction mechanisms are elucidated by identifying the existence of short-chain molecular Se encapsulated inside the microporous host, which transforms to K 2 Se by a two-step conversion reaction via an "all-solid-state" electrochemical process in the carbonate electrolyte system. Through the whole reaction, the generation of polyselenides (K 2 Se n , 3 ≤ n ≤ 8) is effectively suppressed by electrochemical reaction dominated by Se-2 molecules, thus significantly enhancing the utilization of Se and effecting the voltage platform of the K-Se battery. This work offers a practical pathway to optimize the K-Se battery performance through structure engineering and manipulation of selenium chemistry for the formation of selective species and reveal its internal reaction mechanism in the carbonate electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2020

Material erosion measurements and expected operational lifetime of a deployable photon sieve payload

Spacecraft operating in low-Earth orbit are subjected to a number of hazardous environmental constituents that can lead to decreased system performance and reduced operational lifetimes. Due to their thermal, optical, and mechanical properties, polymers are used extensively in space systems; however they are particularly susceptible to material erosion and degradation as a result of exposure to the LEO environment. The focus of this research is to examine the material erosion and mass loss experienced by a custom Kaptonlike polyimide due to exposure in a simulated low-earth orbit environment. The deployable membrane telescope design discussed in this research is named Peregrine and is the scientific payload for FalconSat-7, a 3U cubesat designed and developed at the United States Air Force Academy (USAFA) for the purpose of demonstrating the capability of deployable membrane telescope technology on a nanosatellite platform. In addition to the polymer samples, chrome, silver and gold specimens will be examined to measure the oxidation rate and act as a control specimen, respectively. A magnetically filtered atomic oxygen plasma source has previously been developed and characterized for the purpose of simulating the low-Earth orbit environment. The plasma source can be operated at a variety of discharge currents and gas flow rates, of which the plasma parameters downstream of the source are dependent. The characteristics of the generated plasma were examined as a function of these operating parameters to optimize the production of O + ions with energy relevant to LEO applications. The erosion yield of the Kapton-like polyimide was experimentally determined to be 2.84 x 10 -24 cm 3 per atom which is in close agreement when compared to the on-orbit measurement for reaction efficiency of Kapton HN. We report the experimentally determined reaction rate for the Kapton-like polyimide was used to estimate the operational lifetime of the photon sieve during the solar conditions expected beginning in May 2019. The effective lifetime is estimated between 126 and 153 days.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Toward Rational Design of Supported Vanadia Catalysts of Lignin Conversion to Phenol

In sustainable chemical engineering, catalytic upgrading of lignocellulosic biomass has recently gained attention for producing renewable platform chemicals. To achieve maximal biomass utilization, upgrading the underutilized lignin components is essential. Among various catalysts for lignin upgrading, supported vanadia (V2O5) catalysts are promising because of their cost-effectiveness and tunability of either dopant metals or catalyst supports. Here, computational studies are conducted to derive rational design guidelines of supported V2O5 catalysts for accomplishing the high catalytic activity of lignin upgrading to phenol, a key compound for producing bioplastics and biofuel blendstocks. Guaiacol was used as the model compound since it comprises the highest portion of depolymerized lignin. Computational mechanistic studies for the catalytic guaiacol conversion to phenol were performed for the V2O5 catalysts on Titania (TiO2) and silica (SiO2) to explain higher experimental phenol yields on V2O5/SiO2 than V2O5/TiO2. The hydrogen migration from the methoxy group to the aryl ring was identified as a rate-determining step, and the overall activation energies on the two catalysts were compared. A structural analysis was carried out for the catalysts and rate-determining transition states to gain further insights from mechanistic studies. It was concluded that the tilt angle of the aryl group in the hydrogen migration transition state is a key descriptor determining the catalytic activity of phenol formation. These features correlate well with activation energies and experimental phenol yields, indicating that they provide design guidelines for supported metal catalysts for lignin upgrading before experiments.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2022

Room-temperature skyrmion lattice in a layered magnet (Fe 0.5 Co 0.5 ) 5 GeTe 2

Novel magnetic ground states have been stabilized in two-dimensional (2D) magnets such as skyrmions, with the potential next-generation information technology. Here, we report the experimental observation of a Néel-type skyrmion lattice at room temperature in a single-phase, layered 2D magnet, specifically a 50% Co-doped Fe 5 GeTe 2 (FCGT) system. The thickness-dependent magnetic domain size follows Kittel's law. The static spin textures and spin dynamics in FCGT nanoflakes were studied by Lorentz electron microscopy, variable-temperature magnetic force microscopy, micromagnetic simulations, and magnetotransport measurements. Current-induced skyrmion lattice motion was observed at room temperature, with a threshold current density, j th = 1 × 10 6 A/cm 2 . This discovery of a skyrmion lattice at room temperature in a noncentrosymmetric material opens the way for layered device applications and provides an ideal platform for studies of topological and quantum effects in 2D.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Towards cost-competitive middle distillate fuels from ethanol within a market-flexible C2 platform-based biorefinery concept

Ethanol to middle distillates (ETMD) is a promising pathway to produce sustainable liquid fuels to decarbonize the hard-to-electrify transportation sectors due to (1) the abundant sugar/starch and lignocellulosic biomass, (2) the existing deployment scale of fuel ethanol production (similar to 29 billion gallons per year globally), and (3) emerging opportunities in C2+ alcohol synthesis from CO2. Here we report a conceptual market-responsive biorefinery centered around a new ETMD pathway based on one-step ethanol to butene-rich olefins (ETO) over a Cu-Zn-Y/Beta catalyst. Specifically, this ethanol conversion pathway comprises one-step ETO, oligomerization, and hydrotreating. This ETO is distinct from that in the conventional ethanol-to-jet process which is based on two-step ethanol to ethylene and ethylene oligomerization to butenes. Butene-rich olefins can be shifted to butadiene-rich products by simply changing the reaction atmosphere from hydrogen to inert gas over the same ETO catalyst. Leveraging the experimental results, baseline techno-economic analysis (TEA) and sensitivity analysis indicate that the ethanol conversion cost is $0.60 per gallon gasoline equivalent (GGE), with opportunities for further cost reduction via improving the liquid hydrocarbon yield and space velocities, and process optimization on balancing dewatering of ethanol feed prior to the ETO step. The minimum fuel selling price (MFSP) of liquid hydrocarbons derived from corn starch ethanol with butadiene as coproduct is $1.64 per GGE, in the range that is cost competitive with petroleum kerosene-type jet fuel. Projected MFSP for cellulosic ethanol (corn stover) derived hydrocarbons is below $3.00 per GGE and co-production of butadiene further reduces the MFSP to $1.70 per GGE. The Well-to-Wake life-cycle analysis indicates that 85% greenhouse gas emission reduction can be achieved when using corn stover compared to petroleum reference and the associated carbon credits will provide significant economic incentives to favor the cellulosic ethanol-derived hydrocarbon fuels. This study demonstrates a low-cost pathway to middle distillate fuels leveraging existing ethanol infrastructure, where catalysis innovation drives the reduction of process complexity and flexible coproduction of a value-added chemical product.

Zhang, Junyan↗
DOE OSTI2022

Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments

Transition metal carbides (TMCs) have attracted significant attention because of their applications toward a wide range of catalytic transformations. However, the practicality of their synthesis is still limited because of the harsh conditions in which most TMCs are prepared. Recently, a solution-phase synthesis of phase-pure a-MoC1-x nanoparticles was presented. While this synthetic route yielded nanoparticles with exceptional catalytic performance, the reaction parameter space was not explored, and catalyst throughput was not optimized for scale-up. Continuous flow platforms coupled with statistical design of experiments (DoE) can provide a powerful method for understanding the reaction parameter space for optimizations. Here, we demonstrate the use of statistical DoE in tandem with response surface methodology for a parametric screening analysis to optimize the throughput of a MoC1-x nanoparticle synthesis utilizing a millifluidic flow reactor. A full factorial design was implemented to evaluate four input variables (reaction temperature, flow rate, solvent fraction of oleylamine, and precursor concentration) that carry statistically significant effects on three responses (throughput, residence time, and isolated yield). A Doehlert matrix was implemented to investigate each significant variable at a higher number of levels to optimize throughput. Our results give a nonintuitive set of experimental conditions that resulted in an optimized throughput of 2.2 g h-1. This translates to a 50-fold increase in throughput compared to the previously reported batch method. The catalytic performance of the MoC1-x nanoparticles produced under optimized throughput was demonstrated in the CO2 hydrogenation reaction. This DoE screening analysis and throughput optimization of MoC1-x synthesis open the door to an increased feasibility for scale-up.

design of experiments↗
DOE OSTI2023

Measurements of DC SQUID Damping Effects on Superconducting Resonant Circuits

Here, we present experimental measurements of SQUID-induced damping effects on strongly coupled, lumped-element resonators in the 500kHz–1 MHz frequency range. While dc SQUIDs are commonly used for sensitive readout of electromagnetic signals such as in lumped-element axion dark matter searches, coupling a dc SQUID to a resonant circuit modifies the circuit's resonance frequency, quality factor, noise, and impedance. These parameters have a direct impact on the science reach of axion dark matter detectors, making it important to understand SQUID damping contributions. We describe a helium dip probe and two-stage SQUID readout scheme used to probe SQUID damping effects. Measurements from this setup inform the design and operation of both future test platforms as well as next-generation dark-matter experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Emerging Materials for Plasmonics, Metamaterials and Metasurfaces

This symposium addressed emerging topics of hybrid nanophotonics including plasmonics, metamaterials, metasurfaces, and two-dimensional materials to overcome existing limitations that prevent the development of practical photonic devices. Symposium topics are related to fundamental, basic, and applied research, and the symposium addressed the need for basic and applied research in developing plasmonic materials and metamaterials aiming at fast optical switching including utilizing materials with large broadband optical nonlinearities, the ability to effectively reduce transmission, and ultra-fast response time. The advanced material designs are put forth by numerical and experimental methods with an emphasis on developing hierarchical designs and two/three-dimensional self-assembly of multifunctional materials and advance material concepts including transformational optics. Novel plasmonic materials for optoelectronics with the aim to increase in the data rate are of great interest. Besides that, the efforts are in understanding electromagnetic phenomena in solid-state materials and structure involving the latest developments in semiconductor materials and device physics. In the fundamental research and engineering science, the symposium topics in electronics and material science focus on investigations of novel metamaterial, metasurface and optical component designs as well as diving into developments of alternative materials. The symposium provided a general overview of recent advances in new design concepts and material platforms, including fabrication techniques and promising applications enabled by the new developments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Robust implementation of generative modeling with parametrized quantum circuits

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze, a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver’s stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires one to perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. In this work, we leverage on Rigetti’s Quantum Cloud Services (QCS™) to overcome this implementation barrier, and we study the on-hardware performance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansätze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes dataset. In this experimental benchmarking, the gradient-free optimization algorithms show an outstanding performance compared to the gradient-based solver. In particular, one of them had better performance when handling the unavoidable noisy objective function to be minimized under experimental conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CRISPRi-ART enables functional genomics of diverse bacteriophages using RNA-binding dCas13d

Bacteriophages constitute one of the largest reservoirs of genes of unknown function in the biosphere. Even in well-characterized phages, the functions of most genes remain unknown. Experimental approaches to study phage gene fitness and function at genome scale are lacking, partly because phages subvert many modern functional genomics tools. Here we leverage RNA-targeting dCas13d to selectively interfere with protein translation and to measure phage gene fitness at a transcriptome-wide scale. We find CRISPR Interference through Antisense RNA-Targeting (CRISPRi-ART) to be effective across phage phylogeny, from model ssRNA, ssDNA and dsDNA phages to nucleus-forming jumbo phages. Using CRISPRi-ART, we determine a conserved role of diverse rII homologues in subverting phage Lambda RexAB-mediated immunity to superinfection and identify genes critical for phage fitness. CRISPRi-ART establishes a broad-spectrum phage functional genomics platform, revealing more than 90 previously unknown genes important for phage fitness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparative analysis of the sensitivity of nanometallic thin film thermometers

Thin film platinum resistive thermometers are conventionally applied for resistance thermometry techniques due to their stability and proven measurement accuracy. Depending upon the required thermometer thickness and temperature measurement, however, performance benefits can be realized through the application of alternative nanometallic thin films. Herein, a comparative experimental analysis is provided on the performance of nanometallic thin film thermometers most relevant to microelectronics and thermal sensing applications: Al, Au, Cu, and Pt. Sensitivity is assessed through the temperature coefficient of resistance, measured over a range of 10–300 K for thicknesses nominally spanning 25–200 nm. In this work, the interplay of electron scattering sources, which give rise to the temperature-dependent TCR properties for each metal, are analyzed in the framework of a Mayadas–Shatzkes based model. Despite the prevalence of evaporated Pt thin film thermometers, Au and Cu films fabricated in a similar manner may provide enhanced sensitivity depending upon thickness. These results may serve as a guide as the movement toward smaller measurement platforms necessitates the use of smaller, thinner metallic resistance thermometers.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scalable manufacturing of quantum light emitters in silicon under rapid thermal annealing

Quantum light sources play a fundamental role in quantum technologies ranging from quantum networking to quantum sensing and computation. The development of these technologies requires scalable platforms, and the recent discovery of quantum light sources in silicon represents an exciting and promising prospect for scalability. The usual process for creating color centers in silicon involves carbon implantation into silicon, followed by rapid thermal annealing. However, the dependence of critical optical properties, such as the inhomogeneous broadening, the density, and the signal-to-background ratio, on centers implantation steps is poorly understood. We investigate the role of rapid thermal annealing on the dynamic of the formation of single color centers in silicon. We find that the density and the inhomogeneous broadening greatly depend on the annealing time. We attribute the observations to nanoscale thermal processes occurring around single centers and leading to local strain fluctuations. Our experimental observation is supported by theoretical modeling based on first principles calculations. The results indicate that annealing is currently the main step limiting the scalable manufacturing of color centers in silicon.

42 ENGINEERING↗
DOE OSTI2020

Quantum Memristors in Frequency-Entangled Optical Fields

A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Manufacturing of Fabric Electrodes using a High-Throughput Screening Platform for Redox Flow Batteries

The objective of this project is to establish a new manufacturing methodology with machine learning- based high-throughput screening for the design and development of hierarchical structured, high-performance fabric electrodes for redox flow batteries (RFBs). The end goal of the project is to design and manufacture fabric electrodes for RFB applications that can provide 250 mA/cm2 current density operation for 100-cycles with 80% average energy efficiency. This was accomplished by first examining the structure-performance-property linkages of the electrodes provided by our partner, AvCarb. The electrodes’ microstructure was characterized by determining their pore size distribution, tortuosity, specific surface area, and porosity. The ohmic, charge transfer and mass transfer resistances were then calculated using electrochemical impedance spectroscopy. Carbon cloth electrodes showed the greatest resistance, which was dominated by charge transfer resistance, which we believe is related to the surface functionalization. Full cell cycling was used in order to determine the area specific resistance and energy efficiency of the cells. All of this experimental data and the results of the mathematical model (to increase the amount of inputs with parametric sweeping) were used to develop a machine learning-based model for the design of high-performance fabric electrodes. Using the results from the machine learning tool, optimized electrodes were fabricated by AvCarb. The ohmic, charge transfer and mass transfer resistances for these new electrodes were measured, and both performed better than any of the initial samples which had been provided by AvCarb.

25 ENERGY STORAGE↗
DOE OSTI2021

Temperature-Dependent Properties of Molten Li 2 BeF 4 Salt Using Ab Initio Molecular Dynamics

Molten lithium tetrafluoroberyllate (Li 2 BeF 4 ) salt, also known as FLiBe, with a 2:1 mixture of LiF and BeF 2 is being proposed as a coolant and solvent in advanced nuclear reactor designs, such as the molten salt reactor or the fluoride salt cooled high-temperature reactor. We present the results on the structure and properties of FLiBe over a wide range of temperatures, 0–2000 K, from high-throughput ab initio molecular dynamics simulation using a supercell model of 504 atoms. The variations in the local structures of solid and liquid FLiBe with temperature are discussed in terms of a pair distribution function, coordination number, and bond angle distribution. The temperature-dependent electronic structure and optical and mechanical properties of FLiBe are calculated. The optical and mechanical property results are reported for the first time. The results above and below the melting temperature (~732 K) are compared with the experimental data and with data for crystalline FLiBe. The electronic structure and interatomic bonding results are discussed in correlation with the mechanical strength. A novel concept of total bond order density (TBOD), an important quantum mechanical parameter, is used to characterize the internal cohesion and strength in the simulated models. The results show a variation in the rate of change in properties in solid and liquid phases with anomalous behavior across the melting region. The observed trend is the decrease in mechanical strength, band gap, and TBOD in a nonlinear fashion as a function of temperature. The refractive index shows a surprising minimum at 850 K, among the tested temperatures, which lies above the melting point. These findings provide a new platform to understand the interplay between the temperature-dependent structures and properties of FLiBe salt.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Few-femtosecond resolved imaging of laser-driven nanoplasma expansion

Abstract The free expansion of a planar plasma surface is a fundamental non-equilibrium process relevant for various fields but as-yet experimentally still difficult to capture. The significance of the associated spatiotemporal plasma motion ranges from astrophysics and controlled fusion to laser machining, surface high-harmonic generation, plasma mirrors, and laser-driven particle acceleration. Here, we show that x-ray coherent diffractive imaging can surpass existing approaches and enables the quantitative real-time analysis of the sudden free expansion of laser-heated nanoplasmas. For laser-ionized SiO 2 nanospheres, we resolve the formation of the emerging nearly self-similar plasma profile evolution and expose the so far inaccessible shell-wise expansion dynamics including the associated startup delay and rarefaction front velocity. Our results establish time-resolved diffractive imaging as an accurate quantitative diagnostic platform for tracing and characterizing plasma expansion and indicate the possibility to resolve various laser-driven processes including shock formation and wave-breaking phenomena with unprecedented resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗