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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Identification of Common Types of Plastics by Vibrational Spectroscopic Techniques

Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS) account for most plastic use worldwide, with production nearing 380 million tons annually. A considerable portion enters municipal solid waste and landfills, creating long-term environmental concerns. Scaling recycling operations requires automated sorting technologies, with spectroscopy and machine learning offering promising solutions. In this study, a six-class convolutional neural network (CNN) was developed for plastic identification using vibrational spectroscopies. Raman Scattering (RS) spectra collected from recycling samples enabled accurate chemical differentiation while assessing the influence of visible features such as color. A CNN trained on RS data achieved 100% classification accuracy. To strengthen field applicability, Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy was incorporated, achieving 95% accuracy with a similar CNN model. These findings demonstrate the potential of integrating spectroscopy with deep learning for reliable plastic classification, advancing development of scalable, field-ready recycling technologies.

Garcia Tovar, Maria P.↗
DOE OSTI2025

Dual‐Transformer Deep Learning Framework for Seasonal Forecasting of Great Lakes Water Levels

Abstract The Great Lakes of North America form one of the largest freshwater systems on Earth, and their lake‐wide average water levels (lake levels) can fluctuate by more than 0.5 m on a seasonal scale. These fluctuations pose substantial challenges for coastal resilience, flood risk management, and navigation planning. Accurate seasonal forecasting of lake levels using traditional mechanistic models is challenging due to the complex physical mechanisms and coupled hydroclimatic processes involved. Recently, deep learning has gained prominence in geoscience applications for its ability to recognize intricate patterns within multiphysical data sets. Here, we introduce a novel Dual‐Transformer deep learning framework, tested on the Great Lakes. This architecture integrates two modified Transformer models: the Prophet, which predicts underlying trends, and the Critic, which refines the Prophet's predictions. The final lake level prediction is derived by weighting the outputs of both models through a multi‐layer perceptron, jointly trained with the Prophet and Critic to enhance overall accuracy. Our results demonstrate that the innovative learning framework achieves the highest prediction accuracy compared to established deep learning models when using identical input features. It attains a root mean square error of 4–7 cm in predicting lake levels up to 6 months in advance across the lakes. Additionally, the Dual‐Transformer model runs six orders of magnitude faster than conventional mechanistic models, producing results in less than one second on a typical personal computer. These findings suggest that our deep learning framework has strong potential to advance lake level prediction and carries important implications for water management and disaster mitigation, thereby enhancing the quality of life in coastal regions.

Chen, Yi [Great Lakes Research Center Michigan Tec↗
DOE OSTI2019

Voltage-gated optics and plasmonics enabled by solid-state proton pumping

Devices with locally-addressable and dynamically tunable optical properties underpin emerging technologies such as high-resolution reflective displays and dynamic holography. The optical properties of metals such as Y and Mg can be reversibly switched by hydrogen loading, and hydrogen-switched mirrors and plasmonic devices have been realized, but challenges remain to achieve electrical, localized and reversible control. Here we report a nanoscale solid-state proton switch that allows for electrical control of optical properties through electrochemical hydrogen gating. We demonstrate the generality and versatility of this approach by realizing tunability of a range of device characteristics including transmittance, interference color, and plasmonic resonance. We further discover and exploit a giant modulation of the effective refractive index of the gate dielectric. The simple gate structure permits device thickness down to ~20 nanometers, which can enable device scaling into the deep subwavelength regime, and has potential applications in addressable plasmonic devices and reconfigurable metamaterials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

What dictates soft clay-like lithium superionic conductor formation from rigid salts mixture

Abstract Soft clay-like Li-superionic conductors, integral to realizing all-solid-state batteries, have been recently synthesized by mixing rigid-salts. Here, through computational and experimental analysis, we clarify how a soft clay-like material can be created from a mixture of rigid-salts. Using molecular dynamics simulations with a deep learning-based interatomic potential energy model, we uncover the microscopic features responsible for soft clay-formation from ionic solid mixtures. We find that salt mixtures capable of forming molecular solid units on anion exchange, along with the slow kinetics of such reactions, are key to soft-clay formation. Molecular solid units serve as sites for shear transformation zones, and their inherent softness enables plasticity at low stress. Extended X-ray absorption fine structure spectroscopy confirms the formation of molecular solid units. A general strategy for creating soft clay-like materials from ionic solid mixtures is formulated.

25 ENERGY STORAGE↗
DOE OSTI2017

Electrically driven deep ultraviolet MgZnO lasers at room temperature

Semiconductor lasers in the deep ultraviolet (UV) range have numerous potential applications ranging from water purification and medical diagnosis to high-density data storage and flexible displays. Nevertheless, very little success was achieved in the realization of electrically driven deep UV semiconductor lasers to date. Here, we report the fabrication and characterization of deep UV MgZnO semiconductor lasers. These lasers are operated with continuous current mode at room temperature and the shortest wavelength reaches 284 nm. The wide bandgap MgZnO thin films with various Mg mole fractions were grown on c-sapphire substrate using radio-frequency plasma assisted molecular beam epitaxy. Metal-semiconductor-metal (MSM) random laser devices were fabricated using lithography and metallization processes. Besides the demonstration of scalable emission wavelength, very low threshold current densities of 29-33 A/cm 2 are achieved. Furthermore, numerical modeling reveals that impact ionization process is responsible for the generation of hole carriers in the MgZnO MSM devices. The interaction of electrons and holes leads to radiative excitonic recombination and subsequent coherent random lasing.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Structural analysis of mycobacterial homoserine transacetylases central to methionine biosynthesis reveals druggable active site

Mycobacterium tuberculosis is the cause of the world’s most deadly infectious disease. Efforts are underway to target the methionine biosynthesis pathway, as it is not part of the host metabolism. The homoserine transacetylase MetX converts L-homoserine to O-acetyl-L-homoserine at the committed step of this pathway. In order to facilitate structure-based drug design, we determined the high-resolution crystal structures of three MetX proteins, including M. tuberculosis (MtMetX), Mycolicibacterium abscessus (MaMetX), and Mycolicibacterium hassiacum (MhMetX). A comparison of homoserine transacetylases from other bacterial and fungal species reveals a high degree of structural conservation amongst the enzymes. Utilizing homologous structures with bound cofactors, we analyzed the potential ligandability of MetX. The deep active-site tunnel surrounding the catalytic serine yielded many consensus clusters during mapping, suggesting that MtMetX is highly druggable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exploring orbital angular momentum and spin-orbit correlations for gluons at the Electron-Ion Collider

In our previous work [S. Bhattacharya , ], we introduced a pioneering observable aimed at experimentally detecting the orbital angular momentum (OAM) of gluons. Our focus was on the longitudinal double spin asymmetry observed in exclusive dijet production during electron-proton scattering. We demonstrated the sensitivity of the cos ϕ angular correlation between the scattered electron and proton as a probe for gluon OAM at small x and its intricate interplay with gluon helicity. This current work provides a comprehensive exposition, diving further into the aforementioned calculation with added elaboration and in-depth analysis. We reveal that, in addition to the gluon OAM, one also gains access to the spin-orbit correlation of gluons. We supplement our work with a detailed numerical analysis of our observables for the kinematics of the Electron-Ion Collider. In addition to dijet production, we also consider the recently proposed semi-inclusive diffractive deep inelastic scattering process, which potentially offers experimental advantages over dijet measurements. Finally, we investigate quark-channel contributions to these processes and find an unexpected breakdown of collinear factorization. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Arrested Phase Separation in Double-Exchange Models: Large-Scale Simulation Enabled by Machine Learning

In this work, we present large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model based on deep-learning neural-network potentials trained from small-size exact diagonalization solutions. We uncover an intriguing correlation-induced freezing behavior as doped holes are segregated from half filled insulating background during equilibration. While the aggregation of holes is stabilized by the formation of ferromagnetic clusters through Hund’s coupling between charge carriers and local magnetic moments, this stabilization also creates confining potentials for holes when antiferromagnetic spin-spin correlation is well developed in the background. The dramatically reduced mobility of the self-trapped holes prematurely disrupts further growth of the ferromagnetic clusters, leading to an arrested phase separation. Implications of our findings for phase separation dynamics in materials that exhibit colossal magnetoresistance effect are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures

Data compression has become a crucial technique in addressing performance bottlenecks caused by increasing data volumes in High-Performance Computing (HPC), Big Data, and Deep Learning (DL). Despite its potential to boost system performance, recent studies have identified significant challenges with existing compression methods, mainly due to their high computational demands amidst continuously growing data sizes. Concurrently, the advent of Data Processing Units (DPUs), equipped with programmable System-on-Chip (SoC) and specialized compression accelerators, offers a promising opportunity to alter the landscape of data compression. This paper explores the complexities and potential of leveraging NVIDIA BlueField DPUs to accelerate lossy and lossless compression. Towards this, we introduce PEDAL, an innovative library that leverages the hardware capabilities of DPUs to unify and optimize data compression designs. Moreover, we seamlessly co-design PEDAL with the popular MPICH MPI library, demonstrating up to 101x speedup in compression time and 88x decrease in communication latency. Drawing on these achievements, we share our experience with various research communities about accelerating data compression on DPUs in communication-oriented HPC scenarios.

Li, Yuke↗
DOE OSTI2020

Surface and bulk characterization of reservoir and cap-rocks: Past, present, and future

Geologic formations provide potentially some of the largest volume capacities for CO 2 storage or sequestration. Potential storage sites can be deep saline aquifers, depleted oil reservoirs, and coal seams, surrounded by sealing layers to prevent CO 2 from leaking. It is therefore critical to understand mechanisms contributing to CO 2 trapping and CO 2 leaks. Both phenomena are governed by reactions at the interfaces of the reservoir and cap-rocks and are controlled by the complex chemistry and pore structures of rocks. Mechanisms at the macroscale are affected by the processes occurring at the nanoscale. This review highlights the necessity of multitechnique, multiscale characterization of rocks and points to the importance of surface analysis and surface science studies. Two shale rocks (seals) from Niobrara and Agardhfjellet formations with complex surface chemistry are used as examples throughout the paper. Typically, evaluation of rocks with x-ray diffraction, thermogravimetric analysis, Rock-Eval pyrolysis, gas adsorption, and electron microscopy combined with energy dispersive x-ray spectroscopy is conducted to provide valuable information about the bulk mineralogy, elemental composition, pore volume, and adsorbed species on the sample surface. These studies are necessary prior to designing surface sensitive experiments with x-ray photoelectron spectroscopy (XPS), guiding both sample preparation and sample analysis. XPS has been widely used to study the surface composition of rocks during the investigations of their fine-scale wettability, and the main findings are highlighted here. This paper also reviews the existing literature on ambient-pressure XPS, which provides new opportunities to study in situ chemical alteration due to interactions with CO 2 and offers recommendations for adapting this technique to study rock-fluid interactions, allowing for the identification of fundamental interactions during CO 2 sequestration and guide selection of formation sites for improved storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

DNN Approximation of Nonlinear Finite Element Equations

We investigate the potential of applying (D)NN ((deep) neural networks) for approximating nonlinear mappings arising in the finite element discretization of nonlinear PDEs (partial differential equations). As an application, we apply the trained DNN to replace the coarse nonlinear operator thus avoiding the need to visit the fine level discretization in order to evaluate the actions of the true coarse nonlinear operator. The feasibility of the studied approach is demonstrated in a two level FAS (full approximation scheme) used to solve a nonlinear diffusion-reaction PDE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

DOE SFWST Campaign R&D Roadmap Update

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel and Waste Disposition (SFWD) is conducting research and development (R&D) on deep geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). R&D addressing the disposal of SNF/HLW in the U.S. is currently generic (i.e., "non-site-specific") in scope, following the suspension of the Yucca Mountain Repository Project in 2010. However, to prepare for the eventuality of a repository siting process, the former Used Fuel Disposition Campaign (UFDC) of DOE-NE, which was succeeded by the SFWST Campaign, formulated an R&D Roadmap in 2012 outlining generic R&D activities and their priorities appropriate for developing safety cases and associated performance assessment (PA) models for generic deep geologic repositories in several potential host-rock environments in the contiguous United States. This 2012 UFDC Roadmap also identified the importance of re-evaluating priorities in future years as knowledge is gained from the DOE's ongoing R&D activities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

The synthesis of novel lanthanum hydroxyborate at extreme conditions

The novel structure of lanthanum hydroxyborate La 2 B 2 O 5 (OH) 2 was synthesized by the reaction of partially hydrolyzed lanthanum and boron oxide in a diamond anvil cell under high-pressure/high-temperature (HPHT) conditions of 30 GPa and ~2,400 K. The single-crystal X-ray structure determination of the lanthanum hydroxyborate revealed: P$\overline{3}$c1 , a = 6.555(2) Å, c = 17.485(8) Å, Z = 6, R 1 = 0.056. The three-dimensional structure consists of discrete planar BO 3 groups and three crystallographically different La ions: one is surrounded by 9, one by 10, and one by 12 oxygen anions. The band gap was estimated using ab initio calculations to be 4.64 eV at ambient pressure and 5.26 eV at 30 GPa. The current work describes the novel HPHT lanthanum hydroxyborate with potential application as a deep-ultraviolet birefringent material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Microbial Methylation of Iodide in Unconfined Aquifer Sediments at the Hanford Site, USA

Incomplete knowledge of environmental transformation reactions limits our ability to accurately inventory and predictably model the fate of radioiodine. The most prevalent chemical species of iodine include iodate (IO 3 - ), iodide (I - ), and organo-iodine. The emission of gaseous species could be significant loss or flux term but these processes have not been investigated at radioiodine impacted sites. We examined iodide methylation and volatilization for Hanford Site sediments from 3 different locations under native and organic substrate amended conditions at 3 iodide concentrations. Aqueous and gaseous sampling revealed methyl-iodide to be the only iodinated compound produced under biotic conditions. No abiotic transformations of iodide were measured. Methyl-iodide was produced by 52 out of 54 microcosms, regardless of prior exposure to iodine contamination or the experimental concentration. Interestingly, iodide volatilization activity was consistently higher under native (oligotrophic) Hanford sediment conditions. Carbon and nutrients were not only unnecessary for microbial activation, but supplementation resulted in > 3 fold reduction in methyl-iodide formation. This investigation not only demonstrates the potential for iodine volatilization in deep, oligotrophic subsurface sediments at a nuclear waste site, but also emphasizes an important role for biotic methylation pathways to the long-term management and monitoring of radioiodine in the environment.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Pioneer 10 and 11 mission support

Possible improvements in the Deep Space Network and their potential effect on the telecommunications limit of Pioneer 10 and on Pioneer 11 Saturn encounter are discussed.

Miller, R. B.↗
NASA NTRS1987

Constructions for finite-state codes

A class of codes called finite-state (FS) codes is defined and investigated. These codes, which generalize both block and convolutional codes, are defined by their encoders, which are finite-state machines with parallel inputs and outputs. A family of upper bounds on the free distance of a given FS code is derived from known upper bounds on the minimum distance of block codes. A general construction for FS codes is then given, based on the idea of partitioning a given linear block into cosets of one of its subcodes, and it is shown that in many cases the FS codes constructed in this way have a d sub free which is as large as possible. These codes are found without the need for lengthy computer searches, and have potential applications for future deep-space coding systems. The issue of catastropic error propagation (CEP) for FS codes is also investigated.

Pollara, F.↗
NASA NTRS1994

Enhancement of free tropospheric ozone production by deep convection

It is found from model simulations of trace gas and meteorological data from aircraft campaigns that deep convection may enhance the potential for photochemical ozone production in the middle and upper troposphere by up to a factor of 60. Examination of half a dozen individual convective episodes show that the degree of enhancement is highly variable. Factors affecting enhancement include boundary layer NO(x) mixing ratios, differences in the strength and structure of convective cells, as well as variation in the amount of background pollution already in the free troposphere.

Pickering, Kenneth E.↗