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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nanoscale insights into the structure of solution-processed graphene by x-ray scattering

Abstract Chemical exfoliation is an attractive approach for the synthesis of graphene due to its low cost and simplicity. However, challenges still remain in the characterization of solution-processed graphene, in particular with atomic resolution. Through this work we demonstrate the x-ray pair distribution function as a novel approach to study solution-processed graphene or other 2D materials with atomic resolution, directly in solution, produced by liquid-phase and electrochemical exfoliations. The results show the disappearance of long-range atomic correlations, in both cases, confirming the production of single and few-layer graphene. In addition, a considerable ring distortion has been observed as compared to graphite, irrespective of the solvent used: the normal surface angle to the sheet of the powder sample should be less than 6°, compatible with ripples formation observed in suspended graphene. We attribute this effect to the interaction of solvent molecules with the graphene nanosheets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Review of the 1st EUV Light Sources Code Comparison Workshop

We review the results of the 1 st Extreme Ultraviolet (EUV) Light Sources Code Comparison Workshop. The goal of this workshop was to provide a platform for specialists in EUV light source plasma modeling to benchmark and validate their numerical codes using well-defined case studies. Detailed consideration of a plethora of atomic collisional and radiative processes is required for modeling EUV light source plasmas. Eight institutions spanning four countries contributed data to the workshop. Two topics were addressed, namely (i) the atomic kinetics and radiative properties of tin plasmas under EUV-generating conditions and (ii) laser absorption in a fully ionized, one-dimensional hydrogen plasma. In this paper, we summarize the key findings of the workshop and outline plans for future iterations of the code comparison activity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Uncovering extreme nonlinear dynamics in solids through time-domain field analysis

Time-domain analysis of harmonic fields with sub-cycle resolution is now experimentally viable due to the emergence of sensitive, on-chip techniques for petahertz-scale optical-field sampling. We demonstrate how such a time-domain, field-resolved analysis uncovers the extreme nonlinear electron dynamics responsible for high-harmonic generation within solids. Time-dependent density functional theory was used to simulate harmonic generation from a solid-state band-gap system driven by near- to mid-infrared waveforms. Particular attention was paid to regimes where both intraband and interband emission mechanisms play a critical role in shaping the nonlinear response. We show that a time-domain analysis of the harmonic radiation fields identifies the interplay between intra- and interband dynamical processes underlying the nonlinear light generation. With further analysis, we show that changes to the dominant emission regime can occur after only slight changes to the peak driving intensity and central driving wavelength. Furthermore, time-domain analysis of harmonic fields also reveals, for the first time, the possibility of rapid changes in the dominant emission mechanism within the temporal window of the driving pulse envelope. Finally, we examine the experimental viability of performing time-domain analysis of harmonic fields with sub-cycle resolution using realistic parameters.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Machine learning and atomic layer deposition: Predicting saturation times from reactor growth profiles using artificial neural networks

In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to saturation everywhere in the reactor based on thickness values measured at different points of an ALD reactor after a single trial growth. In order to tackle this problem, we introduce a dataset designed to train neural networks to predict saturation times based on these inputs for a cross-flow ALD reactor. Here, we then explore the predictive ability of artificial neural networks of different depths and sizes using a separate testing dataset to evaluate their accuracies. The results obtained show that networks trained using stochastic gradient descent methods can accurately predict saturation times without requiring any additional information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor, and it highlights the way machine learning can be leveraged for thin film growth and manufacturing. While the datasets and training procedure depend on the reactor geometry, the trained neural networks provide a general surrogate model connecting thickness values and trial dose times with optimal saturation times that can be reused for different ALD processes within the same reactor.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Coherent Reaction between Molecular and Atomic Bose-Einstein Condensates: Integrable Model

Here, we solve a model that describes a stimulated conversion between ultracold bosonic atoms and molecules. The reaction is triggered by a linearly time-dependent transition throughout the Feshbach resonance. Our solution predicts a dependence, with a dynamic phase transition, of the reaction efficiency on the transition rate for both atoms-to-molecule pairing and molecular dissociation processes. We find that for the latter process with a linear energy dispersion of atomic modes, the emerging phase can have a thermalized energy distribution of noninteracting bosons with the temperature defined by the rate of the transition. This provides a simple interpretation of the phase transition in terms of the creation of equilibrium Bose-Einstein condensate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Fundamental limitations on photoisomerization from thermodynamic resource theories

Small, out-of-equilibrium, and quantum systems defy simple thermodynamic expressions. Such systems are exemplified by molecular switches, which exchange heat with a bath. These molecules can photoisomerize, or change conformation, or switch, on absorbing light. The photoisomerization probability depends on kinetic details that couple the molecule’s energetics to its dissipation. Therefore, a simple, general, thermodynamic-style bound on the photoisomerization probability seems out of reach. We derive such a bound using a resource theory. The resource-theory framework is a set of mathematical tools, developed in quantum information theory, used to generalize thermodynamics to small and quantum settings. From this toolkit has been derived a generalization of the second law, the thermomajorization preorder. We use thermomajorization to upper-bound the photoisomerization probability. Then, we compare the bound with an equilibrium prediction and with a Lindbladian model. We identify a realistic parameter regime in which the Lindbladian evolution saturates the thermomajorization bound. We also quantify the energy coherence in the electronic degree of freedom, and we argue that this coherence cannot promote photoisomerization. Overall, this work illustrates how quantum-information-theoretic thermodynamics can elucidate complex quantum processes in nature, experiments, and synthetics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Partial-wave decomposition of the Keldysh ionization amplitude

We present an alternative way of calculating the Keldysh amplitude, i.e., the length-gauge form of the ionization amplitude in the strong-field approximation. The amplitude is evaluated exactly by expanding it in Fourier components and partial waves. Comparisons of the semianalytic model predictions with results of ab initio numerical simulations of the time-dependent Schrödinger equation for the interaction of electrons in short-range potentials with intense laser light yield excellent agreement, for wavelengths from the single photon to the multiphoton to the tunneling regime. Specifically, for ionization from initial states with higher angular momentum quantum number, e.g., p states, a significant improvement over predictions based on the popular saddle-point approximation is found. Furthermore, the current model rate allows for interpretation of the strong-field ionization process in terms of multiphoton absorption pathways and angular momentum selection rules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Atomic-scale oxygen-vacancy engineering in Sub-2 nm thin Al 2 O 3 /MgO memristors

Ultrathin (sub-2 nm) Al 2 O 3 /MgO memristors were recently developed using an in vacuo atomic layer deposition (ALD) process that minimizes unintended defects and prevents undesirable leakage current. These memristors provide a unique platform that allows oxygen vacancies (V O ) to be inserted into the memristor with atomic precision and study how this affects the formation and rupture of conductive filaments (CFs) during memristive switching. Herein, we present a systematic study on three sets of ultrathin Al 2 O 3 /MgO memristors with V O -doping via modular MgO atomic layer insertion into an otherwise pristine insulating Al 2 O 3 atomic layer stack (ALS) using an in vacuo ALD. At a fixed memristor thickness of 17 Al 2 O 3 /MgO atomic layers (~1.9 nm), the properties of the memristors were found to be affected by the number and stacking pattern of the MgO atomic layers in the Al 2 O 3 /MgO ALS. Importantly, the trend of reduced low-state resistance and the increasing appearance of multi-step switches with an increasing number of MgO atomic layers suggests a direct correlation between the dimension and dynamic evolution of the conducting filaments and the V O concentration and distribution. Understanding such a correlation is critical to an atomic-scale control of the switching behavior of ultrathin memristors.

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

Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future

Abstract As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) “Resolution Revolution” made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.

Burley, Stephen K. (ORCID:0000000224879713)↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Interplay between disorder and collective coherent response: Superradiance and spectral motional narrowing in the time domain

The interplay between static and dynamic disorder and collective optical response in molecular ensembles is an important characteristic of nanoplasmonic and nanophotonic molecular systems. Here we investigate the cooperative superradiant response of a molecular ensemble of quantum emitters under the influence of environmental disorder, including inhomogeneous broadening (as induced by a static random distribution of the molecular transition frequencies) and motional narrowing (as induced by stochastic modulation of these excitation energies). The effect of inhomogeneous broadening is to destroy the coherence of the collective molecular excitation and suppress superradiant emission. However, fast stochastic modulation of the molecular excitation energy can effectively restore the coherence of the quantum emitters and lead to a recovery of superradiant emission, which is an unexpected manifestation of motional narrowing. For a light-scattering process as induced by an off-resonant incident pulse, stochastic modulation leads to inelastic fluorescence emission at the average excitation energy at long times and suggests that dynamic disorder effects can actually lead to collective excitation of the molecular ensemble.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Surrogate models to optimize plasma-assisted atomic layer deposition in high aspect ratio features

In this work, we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching (ALE), surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma–surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to ALE and more complex structures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Insights from high-fidelity modeling of industrial rotary bell atomization

The global automotive industry sprayed over 2.6 billion liters of paint in 2018, much of which through electrostatic rotary bell atomization, a highly complex process involving the fluid mechanics of rapidly rotating thin films tearing apart into micrometer-thin filaments and droplets. Coating operations account for 65% of the energy usage in a typical automotive assembly plant, representing 10,000s of gigawatt-hours each year in the United States alone. Optimization of these processes would allow for improved robustness, reduced material waste, increased throughput, and significantly reduced energy usage. Here, we introduce a high-fidelity mathematical and algorithmic framework to analyze rotary bell atomization dynamics at industrially relevant conditions. Our approach couples laboratory experiment with the development of robust non-Newtonian fluid models; devises high-order accurate numerical methods to compute the coupled bell, paint, and gas dynamics; and efficiently exploits high-performance supercomputing architectures. These advances have yielded insight into key dynamics, including i) parametric trends in film, sheeting, and filament characteristics as a function of fluid rheology, delivery rates, and bell speed; ii) the impact of nonuniform film thicknesses on atomization performance; and iii) an understanding of spray composition via primary and secondary atomization. These findings result in coating design principles that are poised to improve energy- and cost-efficiency in a wide array of industrial and manufacturing settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Primary Radiation Damage Formation in Solids

The processes that give rise to changes in the microstructure and the physical and mechanical properties of solid metals and ceramics exposed to energetic particles are initiated by essentially elastic collisions between atoms in what has been called an atomic displacement cascade. These processes are similar in metals and ceramics, with some differences related to the ionic and covalent bonding in ceramics compared to metallic bonding. The formation and evolution of primary radiation damage mechanisms are described to provide an overview of how stable defects are formed by displacement cascades, as well as the nature and morphology of the defects themselves. The impact of the primary variables cascade energy and irradiation temperature are discussed, along with a range of secondary factors that can influence damage formation.

Zarkadoula, Eva↗
DOE OSTI2020

Two-Photon Spontaneous Emission in Atomically Thin Plasmonic Nanostructures

The ability to harness light-matter interactions at the few-photon level plays a pivotal role in quantum technologies. Single photons—the most elementary states of light—can be generated on demand in atomic and solid state emitters. Two-photon states are also key quantum assets, but achieving them in individual emitters is challenging because their generation rate is much slower than competing one-photon processes. We demonstrate that atomically thin plasmonic nanostructures can harness two-photon spontaneous emission, resulting in giant far field two-photon production, a wealth of resonant modes enabling tailored photonic and plasmonic entangled states, and plasmon-assisted single-photon creation orders of magnitude more efficient than standard one-photon emission. We unravel the two-photon spontaneous emission channels and show that their spectral line shapes emerge from an intricate interplay between Fano and Lorentzian resonances. Enhanced two-photon spontaneous emission in two-dimensional nanostructures paves the way to an alternative efficient source of light-matter entanglement for on-chip quantum information processing and free-space quantum communications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated phase segmentation and quantification of high-resolution TEM image for alloy design

In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Molecular Layer Etching of Metalcone Films Using Lithium Organic Salts and Trimethylaluminum

Advances in semiconductor device manufacturing are limited by our ability to precisely add and remove thin layers of material in multistep fabrication processes. Recent reports on atomic layer etching (ALE) have provided the means for the precise removal of inorganic thin films deposited by atomic layer deposition (ALD), opening new avenues for nanoscale device design. Here, we report on a new technique for the precise removal of metal organic thin films deposited by molecular layer deposition (MLD), which we term molecular layer etching. This etching process employs sequential exposures of lithium organic salt (LOS) and trimethylaluminum (TMA) precursors to produce self-limiting etching behavior. We employ quartz crystal microbalance experiments to demonstrate (i) etching of alucone films preloaded with LOS upon TMA exposures and (ii) layer-by-layer etching of alucone films using alternating exposures of LOS and TMA. We also identify the selectivity of these etching mechanisms. We probe the mechanism for the layer-by-layer etching of alucone using a quartz crystal microbalance and Fourier transform infrared spectroscopy and identify that the etching proceeds via heterolytic cleaving of Al-O bonds in alucone upon LOS exposure followed by methylation to produce volatile species upon TMA exposure. The etching process results in the removal of 0.4 nm/cycle of alucone at 160 °C and up to 3.6 nm/cycle of alucone at 266 °C in ex situ etching experiments on silicon wafers. This halogen-free etching process enables etching of MLD films and provides new fabrication pathways for the control of material geometries at the nanoscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Photothermal alternative to device fabrication using atomic precision advanced manufacturing techniques

The attachment of dopant precursor molecules to depassivated areas of hydrogen-terminated silicon templated with a scanning tunneling microscope (STM) has been used to create electronic devices with sub-nanometer precision, typically for quantum physics demonstrations, and to dope silicon past the solid-solubility limit, with potential applications in microelectronics and plasmonics. However, this process, which we call atomic precision advanced manufacturing (APAM), currently lacks the throughput required to develop sophisticated applications because there is no proven scalable hydrogen lithography pathway. Here, we demonstrate and characterize an APAM device workflow where STM lithography has been replaced with photolithography. An ultraviolet laser is shown to locally heat silicon controllably above the temperature required for hydrogen depassivation. STM images indicate a narrow range of laser energy density where hydrogen has been depassivated, and the surface remains well-ordered. A model for photothermal heating of silicon predicts a local temperature which is consistent with atomic-scale STM images of the photo-patterned regions. Finally, a simple device made by exposing photo-depassivated silicon to phosphine is found to have a carrier density and mobility similar to that produced by similar devices patterned by STM.

42 ENGINEERING↗