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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Atomic Layer Deposition of Pt on the Surface Deactivated by Fluorocarbon Implantation: Investigation of the Growth Mechanism

Selective atomic layer deposition (ALD) using surface-controlled reactivity is attracting a great deal of attention as a simple bottom-up patterning process that can provide both continued dimensional scaling and accurate pattern placement for next-generation nanoelectronics. Previously, we reported topographically selective deposition through Pt ALD using a MeCpPtMe 3 precursor and an O 2 counter reactant on fluorocarbon (CF x )-modified surfaces; however, gradual loss of selectivity in the CF x -modified regions was observed during the Pt ALD process. This work develops a fundamental understanding of the microscopic growth mechanisms of Pt ALD on the CF x -modified surface using a combination of experimental analyses and theoretical methods. The Pt growth characteristics on the CF x surface are investigated within a temperature window from 225 to 350 °C, and the results show a sharp sensitivity to growth temperature, with significant Pt growth occurring at temperatures above 300 °C. Through density functional theory (DFT) calculations, the reaction energies for adsorption of oxygen and the MeCpPtMe 3 precursor as well as formation of reaction products of CF x degradation are determined. Based on experimental results in conjunction with the DFT calculations, we show that while lower temperature Pt ALD (<300 °C) on CF x -modified surfaces can be significantly retarded because of a lack of MeCpPtMe 3 chemisorption, the surface reaction pathways for Pt ALD on CF x -modified surfaces at temperatures above 300 °C can proceed through oxygen adsorption and CF 4 desorption followed by Pt nucleation in CF x -degraded regions.

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

Stochastically Realized Observables for Excitonic Molecular Aggregates

In this work, we show that a stochastic approach enables calculations of the optical properties of large 2-dimensional and nanotubular excitonic molecular aggregates. Previous studies of such systems relied on numerically diagonalizing the dense and disordered Frenkel Hamiltonian, which scales approximately as $\mathcal{O}$($N$ 3 ) for $N$ dye molecules. Our approach scales much more efficiently as $\mathcal{O}$($N$ log($N$)), enabling quick study of systems with a million of coupled molecules on the micrometer size scale. We calculate several important experimental observables, including the optical absorption spectrum and density of states, and develop a stochastic formalism for the participation ratio. Quantitative agreement with traditional matrix diagonalization methods is demonstrated for both small- and intermediate-size systems. The stochastic methodology enables the study of the effects of spatial-correlation in site energies on the optical signatures of large 2D aggregates. Our results demonstrate that stochastic methods present a path forward for screening structural parameters and validating experiments and theoretical predictions in large excitonic aggregates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Ab Initio Thermodynamic Model for Magnesium Carbonates and Hydrates

An ab initio thermodynamic framework for predicting properties of hydrated magnesium carbonate minerals has been developed using density-functional theory linked to macroscopic thermodynamics through the experimental chemical potentials for MgO, water, and CO2. Including semiempirical dispersion via the Grimme method and small corrections to the generalized gradient approximation of Perdew, Burke, and Ernzerhof for the heat of formation yields a model with quantitative agreement for the benchmark minerals brucite, magnesite, nesquehonite, and hydromagnesite. The model shows how small differences in experimental conditions determine whether nesquehonite, hydromagnesite, or magnesite is the result of laboratory synthesis from carbonation of brucite, and what transformations are expected to occur on geological time scales. Because of the reliance on parameter-free first principles methods, the model is reliably extensible to experimental conditions not readily accessible to experiment and to any mineral composition for which the structure is known or can be hypothesized, including structures containing defects, substitutions, or transitional structures during solid state transformations induced by temperature changes or processes such as water, CO2, or O2 diffusion. Demonstrated applications of the ab initio thermodynamic framework include an independent means to evaluate differences in thermodynamic data for lansfordite, predicting the properties of Mg analogs of Ca-based hydrated carbonates monohydrocalcite and ikaite which have not been observed in nature, and an estimation of the thermodynamics of barringtonite from the stoichiometry and a single experimental observation.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

36 MATERIALS SCIENCE↗
DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗
DOE OSTI2019

A Study of the Effect of Radiation on Recombination Loss in Heterojunction Solar Cells Based on Single-Crystal Silicon

A method has been developed for numerically estimating the recombination loss in silicon heterojunction solar cells under irradiation. The calculations are based on an analysis of the experimental short-circuit currents. The suggested model makes it possible to evaluate the degree of degradation of semiconductor structures by calculating the decrease in the bulk lifetime and in the diffusion length of carriers. The results obtained are of practical importance for examining the possibility of using this type of solar cells in space conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Bimolecular Chemistry in the Ultracold Regime

Advances in atomic, molecular, and optical physics techniques allowed the cooling of simple molecules down to the ultracold regime ([Formula: see text]1 mK) and opened opportunities to study chemical reactions with unprecedented levels of control. This review covers recent developments in studying bimolecular chemistry at ultralow temperatures. We begin with a brief overview of methods for producing, manipulating, and detecting ultracold molecules. We then survey experimental works that exploit the controllability of ultracold molecules to probe and modify their long-range interactions. Further combining the use of physical chemistry techniques such as mass spectrometry and ion imaging significantly improved the detection of ultracold reactions and enabled explorations of their dynamics in the short range. We discuss a series of studies on the reaction KRb + KRb → K 2 + Rb 2 initiated below 1 μK, including the direct observation of a long-lived complex, the demonstration of product rotational state control via conserved nuclear spins, and a test of the statistical model using the complete quantum state distribution of the products.

Chemistry↗
DOE OSTI2017

Numerical and Experimental Investigation of Turbulent Transport Control via Shaping of Radial Plasma Flow Profiles

Turbulence, and turbulence-driven transport are ubiquitous in magnetically confined plasmas, where there is an intimate relationship between turbulence, transport, instability driving mechanisms (such as gradients), plasma flows, and flow shear. Though many of the detailed physics of the interrelationship between turbulence, transport, drive mechanisms, and flow remain unclear, there have been many demonstrations that transport and/or turbulence can be suppressed or reduced via manipulations of plasma flow profiles. This is well known in magnetic fusion plasmas [e.g., high confinement mode (H-mode) and internal transport barriers (ITB’s)], and has also been demonstrated in laboratory plasmas. However, it may be that the levels of particle transport obtained in such cases [e.g. H-mode, ITB’s] are actually lower than is desirable for a practical fusion device. Ideally, one would be able to actively feedback control the turbulent transport, via manipulation of the flow profiles. The purpose of this research was to investigate the feasibility of using both advanced model-based control algorithms, as well as non-model-based algorithms, to control cross-field turbulence-driven particle transport through appropriate manipulation of radial plasma flow profiles. The University of New Mexico was responsible for the experimental portion of the project, while our collaborators at the University of Montana provided plasma transport modeling, and collaborators at Lehigh University developed and explored control methods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

A Study on the Effects of a Viscous Fluid Surrounding a Small Electrical Assembly

Currently there are limitations in computing chatter behavior of small electrical contacts embedded in components using finite element models. Reduced order models (ROM) have been developed of such electrical contact sub-assemblies to assess the chatter behavior of the contacts during vibration and shock environments. The current ROM method requires experimental validation. This ROM also neglects the frequency and damping effects of the viscous fluid that typically surrounds such sub-assemblies. Dynamic ring-down testing of the electrical contacts in air will be performed and will provide a validation data set for the current ROM. Additionally, dynamic ring-down testing of the electrical contact will be performed in fluids of varying viscosities that will help characterize the effect of the fluid on the contact for an improved ROM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Utilizing Highly Scattered Light for Intelligence through Aerosols

This communication is the final report for the project Utilizing Highly Scattered Light for Intelligence through Aerosols funded by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories and lasting six months in 2019. Aerosols like fog reduce visibility and cause down-time that for critical systems or operations are unacceptable. Information is lost due to the random scattering and absorption of light by tiny particles. Computational diffuse optical imaging methods show promise for interpreting the light transmitted through fog, enabling sensing and imaging to improve situational awareness at depths 10 times greater than current methods. Developing this capability first requires verification and validation of diffusion models of light propagation in fog. For this reason, analytical models were developed and compared to experimental data captured at the Sandia National Laboratory Fog Chamber facility. A methodology was developed to incorporate the propagation of scattered light through the imaging optics to a pixel array. The diffusion approximation to the radiative transfer equation was found to predict light propagation in fog under the appropriate conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

LDRD 226360 Final Project Report: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis

This report is the final documentation for the one-year LDRD project 226360: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. As Sandia has successfully developed in-house X-ray diffraction tools for study of atomic structure in experiments, it has become increasingly important to develop computational analysis methods to support these experiments. When dynamically compressed lattices and orientations are not known a priori, the identification requires a cumbersome and sometimes intractable search of possible final states. These final states can include phase transition, deformation and mixed/evolving states. Our work consists of three parts: (1) development of an XRD simulation tool and use of traditional data science methods to match XRD patterns to experiments; (2) development of ML-based models capable of decomposing and identifying the lattice and orientation components of multicomponent experimental diffraction patterns; and (3) conducting experiments which showcase these new analysis tools in the study of phase transition mechanisms. Our target material has been cadmium sulfide, which exhibits complex orientation-dependent phase transformation mechanisms. In our current one-year LDRD, we have begun the analysis of high-quality c-axis CdS diffraction data from DCS and Thor experiments, which had until recently eluded orientation identification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Large-x partons from Jefferson Lab to the LHC

This project addressed the longitudinal quark and gluon structure of hadrons, a key topic in the 2015 and 2023 NSAC long range plans for nuclear science. The research encompassed and combined theoretical studies with advanced Quantum Chromo Dynamics (QCD) analysis of experimental data, from Jefferson Lab to the Large Hadron Collider. The results have pushed the boundaries in both aspects, and capitalized on the results and methods developed in previous grant renewals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA): Molecular and Computational Technologies for Environmental Microbiology (Final Scientific/Technical Report)

The ENIGMA science focus area (SFA) is a multi-disciplinary, multi-institutional research effort focused on addressing foundational knowledge gaps in environmental microbial communities by studying groundwater and sediment microbiomes in the shallow subsurface at the contaminated Oak Ridge Reservation (ORR). We seek to discover and characterize the reciprocal interactions between the microbial communities and the geochemical and geophysical parameters of the shallow subsurface within the contamination plume. The primary goal of this subcontract was to develop experimental and computational tools to advance our understanding of microbial adaptation and community assembly in contaminated environments, with specific efforts in high-throughput genomic methods, microbial ecology tools, and studies of heavy metal contamination impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Electric-Discharge Milling of Small Airplane-Engine Components

The production of small complex components by electric-discharge milling is studied by experimental methods. Means of optimizing numerical-control software so as to obtain the required shaping precision are proposed. The proposed systems and equipment permit the development and implementation of designs unattainable by other means.

42 ENGINEERING↗
DOE OSTI2021

Modeling of Thermal Decomposition of TATB-Based Explosive for Safety Analysis

We investigate and model the cook-off behavior of LX-17 to understand the response of explosive systems in abnormal thermal environments. Decomposition has been explored via conventional ODTX (One-Dimensional Time-to-eXplosion), PODTX (ODTX with pressure-measurement), TGA (Thermo-Gravimetric Analysis), and DSC (Differential Scanning Calorimetry) experiments under isothermal and ramped temperature profiles. The data were used to fit reaction rate parameters for proposed schemes in an ALE3D computational model. This model includes chemical reactions, thermo- and hydro-dynamics, and material properties, including thermal expansion, compressibility, and strength. These parameterizations were carried out utilizing a Python evolutionary optimization method on LLNL’s high-performance computing clusters. Additional experiments are being developed to further characterize and monitor decomposition intermediates to improve the model. Once experimentally validated, this model will be scalable to several applications involving LX-17. Furthermore, the optimization methodology developed herein should be applicable to other high explosive materials.

Chemistry - Chemical explosives↗
DOE OSTI2018

Electron microscopy techniques employed to explore mitochondrial defects in the developing rat brain following ketamine treatment

Ketamine, an FDA-approved N-methyl-D-aspartate (NMDA) receptor antagonist, is commonly used for general pediatric anesthesia. Accumulating evidence has indicated that prolonged exposure to ketamine induces widespread apoptotic cell death in the developing brains of experimental animals. Although mitochondria are known to play a pivotal role in cell death, little is known about the alterations in mitochondrial ultrastructure that occur during ketamine-induced neurotoxicity. The objective of this pilot study was to utilize classic and contemporary methods in electron microscopy to study the impact of ketamine on the structure of mitochondria in the developing rat brain. While transmission electron microscopy (TEM) was employed to comprehensively study mitochondrial inner membrane topology, serial block-face scanning electron microscopy (SBF-SEM) was used as a complementary technique to compare the overall mitochondrial morphology from a representative treated and untreated neuron. In this study, postnatal day 7 (PND-7) Sprague-Dawley rats were treated with ketamine or saline (6 subcutaneous injections × 20 mg/kg or 10 ml/kg, respectively, at 2-h intervals with a 6-h withdrawal period after the last injection, n=6 each group). Samples from the frontal cortex were harvested and analyzed using TEM or SBF-SEM. While classic TEM revealed that repeated ketamine exposure induces significant mitochondrial swelling in neurons, the newer technique of SBF-SEM confirmed the mitochondrial swelling in three dimensions (3D) and showed that ketamine exposure may also induce mitochondrial fission, which was not observable in the two dimensions (2D) of TEM. Furthermore, 3D statistical analysis of these reconstructed mitochondria appeared to show that ketamine-treated mitochondria had significantly larger volumes per unit surface area than mitochondria from the untreated neuron. The ultrastructural mitochondrial alterations demonstrated here by TEM and SBF-SEM support ketamine’s proposed mechanism of neurotoxicity in the developing rat brain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Electromagnetic properties of indium isotopes illuminate the doubly magic character of 100 Sn

Understanding the nuclear properties in the vicinity of 100 Sn, which has been suggested to be the heaviest doubly magic nucleus with proton number Z equal to neutron number N, has been a long-standing challenge for experimental and theoretical nuclear physics. In particular, contradictory experimental evidence exists regarding the role of nuclear collectivity in this region of the nuclear chart. Here, we provide further evidence for the doubly magic character of 100 Sn by measuring the ground-state electromagnetic moments and nuclear charge radii of indium (Z = 49) isotopes as N approaches 50 from above using precision laser spectroscopy. Our results span almost the complete range between the two major closed neutron shells at N = 50 and N = 82 and reveal parabolic trends as a function of the neutron number, with a clear reduction towards these two closed neutron shells. Furthermore, a detailed comparison between our experimental results and numerical results from two complementary nuclear many-body frameworks (density functional theory and ab initio methods) exposes deficiencies in nuclear models and establishes a benchmark for future theoretical developments.

100Sn↗
DOE OSTI2025

Toward shell model interactions with credible uncertainties

Background: The nuclear shell model is a powerful framework for predicting nuclear structure observables, but relies on interaction matrix elements fit to experimental data as its inputs. Extending the shell model's applicability, particularly toward dripline nuclei, requires efficient fitting methods and credible uncertainty quantification. Traditional approaches face computational challenges and may underestimate uncertainties. Purpose: We develop and test a framework combining eigenvector continuation and Markov chain Monte Carlo to efficiently fit shell model interaction matrix elements and quantify their uncertainties. Methods: Eigenvector continuation is used to emulate shell model calculations, reducing computational costs. The emulator enables Markov chain Monte Carlo sampling to optimize interaction matrix elements and rigorously assess parametric uncertainties. Here, the framework is benchmarked using the USDB interaction in the 𝑠⁢𝑑 shell. Results: The emulator reproduces the USDB interaction with negligible error, validating its use in shell model fitting applications. However, we find that to obtain credible predictive intervals, the model defect of the shell model itself, rather than experimental or emulator error, must be taken into account in order to obtain credible uncertainties. Conclusions: The proposed framework provides an efficient and rigorous approach for fitting shell model interactions and quantifying uncertainties. Further, the normality assumption used in the past appears sufficient to describe the distribution of interaction matrix elements. However, it is crucial to account for model correlations to avoid underestimating uncertainties.

Nuclear forces↗