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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structural, Electrical, and Optical Properties of Single-Walled Carbon Nanotubes Synthesized through Floating Catalyst Chemical Vapor Deposition

Single-walled carbon nanotube (SWCNT) thin films were synthesized by using a floating catalyst chemical vapor deposition (FCCVD) method with a low flow rate (200 sccm) of mixed gases (Ar and H 2 ). SWCNT thin films with different thicknesses can be prepared by controlling the collection time of the SWCNTs on membrane filters. Transmission electron microscopy (TEM) showed that the SWCNTs formed bundles and that they had an average diameter of 1.46 nm. The Raman spectra of the SWCNT films suggested that the synthesized SWCNTs were very well crystallized. Although the electrical properties of SWCNTs have been widely studied so far, the Hall effect of SWCNTs has not been fully studied to explore the electrical characteristics of SWCNT thin films. In this research, Hall effect measurements have been performed to investigate the important electrical characteristics of SWCNTs, such as their carrier mobility, carrier density, Hall coefficient, conductivity, and sheet resistance. The samples with transmittance between 95 and 43% showed a high carrier density of 10 21– 10 23 cm –3 . The SWCNTs were also treated using Brønsted acids (HCl, HNO 3 , H 2 SO 4 ) to enhance their electrical properties. After the acid treatments, the samples maintained their p-type nature. The carrier mobility and conductivity increased, and the sheet resistance decreased for all treated samples. The highest mobility of 1.5 cm 2 /Vs was obtained with the sulfuric acid treatment at 80 °C, while the highest conductivity (30,720 S/m) and lowest sheet resistance (43 ohm/square) were achieved with the nitric acid treatment at room temperature. Different functional groups were identified in our synthesized SWCNTs before and after the acid treatments using Fourier-Transform Infrared Spectroscopy (FTIR).

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Modified Model Dielectric Function for Analyzing Optical Spectra of InGaN Nanofilms on Sapphire Substrates

Due to a lower InN bandgap energy Eg~0.7 eV, InxGa1−xN/Sapphire epifilms are considered valuable in the development of low-dimensional heterostructure-based photonic devices. Adjusting the composition x and thickness d in epitaxially grown films has offered many possibilities of light emission across a wide spectral range, from ultraviolet through visible into near-infrared regions. Optical properties have played important roles in making semiconductor materials useful in electro-optic applications. Despite the efforts to grow InxGa1−xN/Sapphire samples, no x- and d-dependent optical studies exist for ultrathin films. Many researchers have used computationally intensive methods to study the electronic band structures Ejk→, and subsequently derive optical properties. By including inter-band transitions at critical points from Ejk→, we have developed a semiempirical approach to comprehend the optical characteristics of InN, GaN and InxGa1−xN. Refractive indices of InxGa1−xN and sapphire substrate are meticulously integrated into a transfer matrix method to simulate d- and x-dependent reflectivity RE and transmission TE spectra of nanostructured InxGa1−xN/Sapphire epifilms. Analyses of RE and TE have offered accurate x-dependent shifts of energy gaps for InxGa1−xN (x = 0.5, 0.7) in excellent agreement with the experimental data.

Talwar, Devki N. (ORCID:0000000202723195)↗
DOE OSTI2020

Assessment of Water Quality in Indo-Gangetic Plain of South-Eastern Asia under Organic vs. Conventional Rice Farming

Water contamination is often reported in agriculturally intensive areas such as the Indo-Gangetic Plain (IGP) in south-eastern Asia. We evaluated the impact of the organic and conventional farming of basmati rice on water quality during the rainy season (July to October) of 2011 and 2016 at Kaithal, Haryana, India. The study area comprised seven organic and seven conventional fields where organic farming has been practiced for more than two decades. Water quality parameters used for drinking (nitrate, NO 3 ; total dissolved solids (TDS); electrical conductivity (EC) pH) and irrigation (sodium adsorption ratio (SAR) and residual sodium carbonate (RSC)) purposes were below permissible limits for all samples collected from organic fields and those from conventional fields over the long-term (~15 and ~20 years). Importantly, the magnitude of water NO 3 contamination in conventional fields was approximately double that of organic fields, which is quite alarming and needs attention in future for farming practices in the IGP in south-eastern Asia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

NENCI-2021. I. A large benchmark database of non-equilibrium non-covalent interactions emphasizing close intermolecular contacts

In this work, we present NENCI-2021, a benchmark database of ~8000 Non-Equilibirum Non-Covalent Interaction energies for a large and diverse selection of intermolecular complexes of biological and chemical relevance. To meet the growing demand for large and high-quality quantum mechanical data in the chemical sciences, NENCI-2021 starts with the 101 molecular dimers in the widely used S66 and S101 databases and extends the scope of these works by (i) including 40 cation–π and anion–π complexes, a fundamentally important class of non-covalent interactions that are found throughout nature and pose a substantial challenge to theory, and (ii) systematically sampling all 141 intermolecular potential energy surfaces (PESs) by simultaneously varying the intermolecular distance and intermolecular angle in each dimer. Designed with an emphasis on close contacts, the complexes in NENCI-2021 were generated by sampling seven intermolecular distances along each PES (ranging from 0.7× to 1.1× the equilibrium separation) and nine intermolecular angles per distance (five for each ion–π complex), yielding an extensive database of 7763 benchmark intermolecular interaction energies (E int ) obtained at the coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory. The E int values in NENCI-2021 span a total of 225.3 kcal/mol, ranging from -38.5 to +186.8 kcal/mol, with a mean (median) E int value of -1.06 kcal/mol (-2.39 kcal/mol). In addition, a wide range of intermolecular atom-pair distances are also present in NENCI-2021, where close intermolecular contacts involving atoms that are located within the so-called van der Waals envelope are prevalent—these interactions, in particular, pose an enormous challenge for molecular modeling and are observed in many important chemical and biological systems. A detailed symmetry-adapted perturbation theory (SAPT)- based energy decomposition analysis also confirms the diverse and comprehensive nature of the intermolecular binding motifs present in NENCI-2021, which now includes a significant number of primarily induction-bound dimers (e.g., cation–π complexes). NENCI-2021 thus spans all regions of the SAPT ternary diagram, thereby warranting a new four-category classification scheme that includes complexes primarily bound by electrostatics (3499), induction (700), dispersion (1372), or mixtures thereof (2192). A critical error analysis performed on a representative set of intermolecular complexes in NENCI-2021 demonstrates that the E int values provided herein have an average error of ±0.1 kcal/mol, even for complexes with strongly repulsive E int values, and maximum errors of ±0.2–0.3 kcal/mol (i.e., ~±1.0 kJ/mol) for the most challenging cases. For these reasons, we expect that NENCI-2021 will play an important role in the testing, training, and development of next-generation classical and polarizable force fields, density functional theory approximations, wavefunction theory methods, and machine learning based intra- and inter-molecular potentials.

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

Elucidating correlated defects in metal organic frameworks using theory-guided inelastic neutron scattering spectroscopy

Metal organic frameworks (MOFs) that incorporate metal oxide cluster nodes, exemplified by UiO-66, have been widely studied, especially in terms of their deviations from the ideal, defect-free crystalline structures. Although defects such as missing linkers, missing nodes, and the presence of adventitious synthesis-derived node ligands (such as acetates and formates) have been proposed, their exact structures remain unknown. Previously, it was demonstrated that defects are correlated and span multiple unit cells. The highly specialized techniques used in these studies are not easily applicable to other MOFs. Thus, there is a need to develop new experimental and computational approaches to understand the structure and properties of defects in a wider variety of MOFs. Here, we show how low-frequency phonon modes measured by inelastic neutron scattering (INS) spectroscopy can be combined with density functional theory (DFT) simulations to provide unprecedented insights into the defect structure of UiO-66. We are able to identify and assign peaks in the fingerprint region (<100 cm –1 ) which correspond to phonon modes only present in certain defective topologies. Specifically, this analysis suggests that our sample of UiO-66 consists of predominantly defect-free fcu regions with smaller domains corresponding to a defective bcu topology with 4 and 2 acetate ligands bound to the Zr 6 O 8 nodes. Importantly, the INS/DFT approach provides detailed structural insights (e.g., relative positions and numbers of acetate ligands) that are not accessible with microscopy-based techniques. Furthermore, the quantitative agreement between DFT simulations and the experimental INS spectrum combined with the relative simplicity of sample preparation, suggests that this methodology may become part of the standard and preferred protocol for the characterization of MOFs, and, in particular, for elucidating the structure defects in these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multimodal Tip-Enhanced Nonlinear Optical Nanoimaging of Plasmonic Silver Nanocubes

Optical field localization at plasmonic tip-sample nanojunctions has enabled high spatial resolution chemical analysis through tip-enhanced linear optical spectroscopies, including Raman scattering and photoluminescence. In this work, we illustrate that nonlinear optical processes, including parametric four-wave mixing (4WM), second harmonic/sum-frequency generation (SHG and SFG), and two-photon photoluminescence (TPPL), can be enhanced at plasmonic junctions and spatio-spectrally resolved simultaneously with few-nm spatial resolution under ambient conditions. More importantly, through a detailed analysis of our spectral nano-images, we find that the efficiencies of the local nonlinear signals are determined by sharp tip-sample junction resonances that vary over the few-nanometer length scale because of the corrugated nature of the probe. Namely, plasmon resonances centered at or around the different nonlinear signals are tracked through TPPL, and they are found to selectively enhance nonlinear signals with closely matched optical resonances.

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

On the importance of color in mass spectrometry imaging

Mass spectrometry imaging (MSI) data visualization relies on heatmaps to show the spatial distribution and measured abundances of molecules within a sample. Non-uniform color gradients such as jet are still commonly used to visualize MSI data, increasing the probability of data misinterpretation and false conclusions. Also, the use of non-uniform color gradients and the combination of hues used in common colormaps makes it challenging for people with color vision deficiencies (CVDs) to visualize and accurately interpret data. Here we present best practices for choosing a colormap to accurately display MSI data, improve readability, and accommodate all CVDs. We also provide other resources on the misuse of color in the scientific field and resources on scientifically derived colormaps presented herein.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

ATOMIC AND MICROSTRUCTURAL ORIGINS OF STORED ENERGY RELEASE IN NEUTRON-IRRADIATED SILICON CARBIDE

Here, we employ a combination of advanced synchrotron-based scattering characterization techniques to understand and unravel the atomic origins of the colossal stored energy release in neutron irradiated silicon carbide. The quantification of the neutron irradiation-induced defects, and their impact on the structureproperty relationship is important for the design and safe operation of advanced fission and fusion reactors. Our experimental results show that the atomic structure in the as irradiated samples is significantly perturbed due to a large fraction of vacancy and interstitial type defects that lead to complex microstructures and additional components in the x-ray diffraction and pair distribution function results. We directly correlate the stored energy release to the recovery of the sub-lattices, with pair distribution function analysis highlighting that the carbon interstitial and vacancy-type defects contribute to stored energy more than those of silicon. We find these results to be striking and believe our discoveries to be timely and noteworthy given the technological importance of silicon carbide to the nuclear fission and fusion communities.

Sprouster, D.J.↗
DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Expansion of smallmouth bass distribution and habitat overlap with juvenile Chinook salmon in the Willamette River, Oregon

Abstract Smallmouth bass populations have expanded far beyond their native range and these predatory fish present a pervasive threat to native aquatic species throughout North America. In the western United States, smallmouth bass are now present in river and reservoir habitats where Pacific salmon are found and are considered a potential threat to salmon recovery in many locations. We conducted a study to determine if smallmouth bass are expanding their range in the mainstem Willamette River, Oregon, and developed a model to assess habitat overlap between smallmouth bass and juvenile Chinook salmon. Sampling during 2011–2022 revealed that the distribution of smallmouth bass had expanded throughout that timeframe to encompass the entire mainstem Willamette River, including important rearing habitats for juvenile Chinook salmon. The model predicted that smallmouth bass and juvenile Chinook salmon habitat overlap was substantial, highlighting the need for additional research to evaluate for potential negative impacts to salmon recovery in the basin. The model was also used to evaluate the efficacy of using flow management to reduce interactions between these two species, but the scenarios we examined suggested that this was not a viable option. These results highlight the need for continued research to assess interactions between smallmouth bass and juvenile salmon, and other native species of concern, in the Willamette River Basin. The development of the model is useful for resource managers to understand interactions between these species to prioritize locations for sampling in the future.

White, James S.↗
DOE OSTI2022

Role of p-GaN layer thickness in the degradation of InGaN-GaN MQW solar cells under 405 nm laser excitation

GaN-based solar cells with InGaN multiple quantum wells (MQWs) are promising devices for application in space environment, concentrator solar systems, wireless power transmission and multi-junction solar cells. It is therefore important to understand their degradation kinetics when submitted to high-temperature and high-intensity stress. We submitted three samples of GaN-InGaN MQW solar cells with p-AlGaN electron-blocking-layer with different thickness of the p-GaN layer to constant power stress at 310 W/cm 2 , 175 °C for several hundred hours. The main degradation modes are a reduction of open-circuit voltage, short-circuit current, external quantum efficiency, power conversion efficiency and electroluminescence. In particular, we observed that a thinner p-GaN layer results in a stronger degradation observed on the cell operating parameters. The analysis of the dark I-V characteristics showed an increase in low-forward bias current and the analysis of electroluminescence showed a decrease in the electroluminescene emitted by the (forward biased) cell, as a consequence of stress. This work highlights that the cause of degradation is possibly related to a diffusion mechanism, which results in an increase of defect density in the active region. Finally, the impurities involved in the diffusion processes possibly originate from the p-side of the devices, therefore a thicker p-GaN layer reduces the amount of defects reaching the active region.

14 SOLAR ENERGY↗
DOE OSTI2026

Quantitative 14 N NMR with Monte Carlo Uncertainty Analysis of Nitrate/Nitrite in Alkaline Nuclear Waste

While monitoring of nitrate and nitrite concentrations is important for managing corrosion in nuclear waste systems, existing analytical methods are hindered by turbidity, spectral interference, and delays from sample handling. Here, we demonstrate quantitative 14 N nuclear magnetic resonance (qNMR) spectroscopy as a direct, matrix-tolerant approach for nitrate and nitrite detection at natural abundance. Monte Carlo resampling was integrated into the workflow to quantify random error, establish precision–time tradeoffs, and separate noise-limited uncertainty from systematic bias arising from shimming, transmitter offset, or excitation pulse conditions. Quantification of nitrate and nitrite were validated in controlled alkaline matrix challenges and in 18-component Hanford-type simulants. These results establish 14 N qNMR as a practical, uncertainty-bounded tool for monitoring redox-active nitrogen species in chemically complex environments and provide a generalizable framework for quantitative analysis of quadrupolar nuclei.

Graham, Trent R. [Pacific Northwest National Labor↗
DOE OSTI2021

A Preprocessing Tool for Enhanced Ion Mobility–Mass Spectrometry-Based Omics Workflows

The ability to improve the data quality of ion mobility–mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Further, other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Demonstration of an Automated System for Vertical Profiles of Volatile Organic Compounds

Volatile organic compounds (VOCs) play important roles throughout the atmosphere, many of which are altitude dependent. This highlights the need for easily deployable devices to sample VOCs across different atmospheric layers. To address this, we present the design and initial application of a Time Resolved Automated Volatile organIc compounds Sampling system (TRAVIS). VOCs are collected on sorbent tubes, which are subsequently analyzed by a thermal desorption gas chromatography mass spectrometry pipeline. TRAVIS leverages a piezoelectric pump with an integrated pressure sensor for precise (0.1% flow rate relative standard deviation) and accurate (−3 ± 2% error in VOC quantitation) measurements. Via deployment on a tethered balloon system over an agricultural area, TRAVIS is used to show consistent vertically resolved VOC concentrations in a well-mixed (i.e., turbulent) atmosphere (e.g., 5% relative standard deviation for isoprene) and vertically dependent concentrations for a stratified atmosphere (e.g., prior to boundary layer development). Furthermore, we also show VOC information from an intermittent plume via both targeted and untargeted analysis, highlighting future applications for spurious events in agriculture, air quality monitoring, and environmental impact. Overall, the development of TRAVIS represents a lightweight, accurate, sensitive, and precise VOC sampling module for the scientific community.

Aerosols↗
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
DOE OSTI2024

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unravelling crystal growth of nanoparticles

Crystal growth from nanoscale constituents is a ubiquitous phenomenon in biology, geology and materials science. Numerous studies have focused on understanding the onset of nucleation and on producing high-quality crystals by empirically sampling constituents with different attributes and varying the growth conditions. However, the kinetics of post-nucleation growth processes, an important determinant of crystal morphology and properties, have remained underexplored due to experimental challenges associated with real-space imaging at the nanoscale. Here we report the imaging of the crystal growth of nanoparticles of different shapes using liquid-phase transmission electron microscopy, resolving both lateral and perpendicular growth of crystal layers by tracking individual nanoparticles. We observe that these nanoscale systems exhibit layer-by-layer growth, typical of atomic crystallization, as well as rough growth prevalent in colloidal systems. Surprisingly, the lateral and perpendicular growth modes can be independently controlled, resulting in two mixed crystallization modes that, until now, have received only scant attention. Combining analytical considerations with molecular dynamics and kinetic Monte Carlo simulations, we develop a comprehensive framework for our observations, which are fundamentally determined by the size and shape of the building blocks. Furthermore, these insights unify the understanding of crystal growth across four orders of magnitude in particle size and suggest novel pathways to crystal engineering.

Imaging techniques↗
DOE OSTI2020

A Look Inside the Black Box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters

A Continuous Filter Convolutional Neural Network (CF-CNN) was trained to predict the potential energy of water cluster networks \ce{(H2O)_{\textit{N}}}, \textit{N}=10--30, corresponding to local minima lying within 5 kcal/mol from the putative minima taken from a newly published database containing over 5 million unique networks. The chemical sampling space of the database was characterized using chemical descriptors derived from graph theory, which led to the identification of important trends in the topology, connectivity, polygon structures associated with the various networks as a function of cluster size. The resulting graphs are available alongside the original database at \url{https://sites.uw.edu/wdbase/}. The CF-CNN trained on a subset of 500,000 networks for (\textit{N}=10, 30) yielded a mean absolute error of 0.002$\pm$0.002 kcal/mol per water molecule, giving the trained CF-CNN the highest accuracy of any neural network-based surrogate model to date. In addition, clusters of sizes not included in the training set exhibited errors of the same magnitude, indicating that the CF-CNN ptotocol is general enough to accurately predict energies of networks for both smaller and larger sizes than those used during training. The graph-theoretical descriptors were developed in order to analyze the properties of the full database and interpret the predictive power of the CF-CNN. Using topology measures, such as the Wiener index and the average shortest path length along with two similarity measures, we showed that all networks from the test set were within the range of the ones from the training set, suggesting that the training set covered the chemical space of interest quite well. Our graph analysis suggests that the mean degree and number of polygons for networks with larger errors tend to lie further from the mean than those with lower errors. The generality of the used CF-CNN was thus demonstrated, while the use of the graph-theoretical descriptors assisted in interpreting the predicted results.

Bilbrey, Jenna A.↗