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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 OSTI2022

Communication—Electrodeposition of Indium Directly on Silicon

Direct electrodeposition of indium onto silicon paves the way for advances in microelectronics, photovoltaics, and optoelectronics. Indium is generally electrodeposited onto silicon utilizing a physically or thermally deposited metallic seed layer. Eliminating this layer poses benefits in microelectronics by reducing resistive interfaces and in vapor-liquid-solid conversion to III-V material by allowing direct contact to the single-crystal silicon substrate for epitaxial conversion. We investigated conditions to directly electrodeposit indium onto n-type Si(100). We illustrate that a two-step galvanostatic plating at low temperatures can consistently produce smooth, continuous films of indium over large areas, in bump morphologies, and conformally into inverted pyramids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Communication—Controlling Etching of Germanium through Surface Charge Manipulation

Potassium hydroxide (KOH) aqueous solutions can effectively etch germanium. Etch rates were determined in an electrolytic etch cell. Electrically isolated Ge wafers were subject to an etch rate of 1.45 ± 0.07 nm min −1 , increasing to 12.6 ± 0.2 nm min −1 when grounded, 97 ± 2 nm min −1 when biased at −0.9 V, and 138 ± 2 nm min −1 with periodic biasing. Results suggest that the previously reported limited etching in KOH is associated with the recombination of holes with electrons injected from the surface reaction. The results of this study demonstrate that changing the hole concentration through biasing is an effective tool to control electrolytic etch rates, enabling future selective etching processes for germanium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Communication—First-Principles Simulations of LiPF 6 Decomposition in Ethylene Carbonate-Based Electrolytes

We revisit a theoretical result by Okamoto (2013 Journal of The Electrochemical Society , 160 , A404) who calculated the energy barrier for the decomposition of lithium hexafluorophosphate (LiPF 6 ) into LiF + PF 5 when solvated in Ethylene carbonate (EC)-based electrolyte. Using different numerical techniques to discretize the Density Functional Theory (DFT) equations, and different continuum solvation models with the same dielectric constant, our results largely confirm the original calculation. However, simulations with a higher dielectric permittivity value, closer to that of EC, show a lower energy barrier. More importantly, First-Principles simulations with an explicit solvent show a substantially lower energy barrier.

25 ENERGY STORAGE↗
DOE OSTI2024

Communication — The Impact of Co-solvent Selection for Dimethyl-2,5-dioxahexane carboxylate in Sodium Ion Batteries

Traditional linear carbonates including dimethyl carbonate (DMC), diethyl carbonate (DEC), and ethyl methyl carbonate (EMC) were investigated as co-solvents for the dimethyl-2,5-dioxahexane carboxylate (DMOHC)-based electrolyte in Na 0.97 Ca 0.03 [Mn 0.39 Fe 0.31 Ni 0.22 Zn 0.08 ]O 2 (NCMFNZO)/hard carbon (HC) pouch cells. The EMC-containing cell displays excellent electrochemical performance, exhibiting only a 1.6 mAh irreversible capacity loss during 500 h of storage at 4 V and 40 °C, and maintaining over 80% capacity retention after 200 cycles up to 4 V at 40 °C. Severe gas evolution and Na plating issues are present in all the tested systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Code Description for "Brief Communication: Monitoring snow depth using small, cheap, and easy-to-deploy ground surface temperature sensors"

Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We train a random forest machine learning model to predict snow depth from variability in ground surface temperature. To our knowledge, this is the first time that small ground surface temperature sensors have been used to estimate snow depth. The model performs well at sites where the model was trained and at pan-arctic evaluation sites (RMSE <= 0.15 m). Small temperature sensors are cheap and easy-to-deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring to an extent previously infeasible. The model is flexible and can be applied to datasets retroactively to retrieve snow depth estimates at additional sites. This code package includes a *.joblib file of the trained random forest model and a *.ipynb file showing how to clean input data, train the random forest model, and apply the model.

Bachand, Claire↗
DOE OSTI2025

Enabling Real-Time Communication in Multi-Agent Systems: A Graph Neural Network Based Approach

Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.

connectivity maintenance↗
DOE OSTI2026

Spot-Checking for Reliable Quantum Communications

We demonstrate a spot-checking approach for real-time drift estimation and mitigation in continuously operating quantum links. Using polarization-entangled photon pairs, we achieve stable 16-hour operation, validating efficient, minimally invasive monitoring of quantum-network performance.

Zhang, Yanbao [ORNL] (ORCID:0000000245530561)↗
DOE OSTI2024

Quantum teleportation coexisting with classical communications in optical fiber

The ability for quantum and conventional networks to operate in the same optical fibers would aid the deployment of quantum network technology on a large scale. Quantum teleportation is a fundamental operation in quantum networking, but has yet to be demonstrated in fibers populated with high-power conventional optical signals. Here we report, to the best of our knowledge, the first demonstration of quantum teleportation over fibers carrying conventional telecommunications traffic. Quantum state transfer is achieved over a 30.2-km fiber carrying 400-Gbps C-band classical traffic with a Bell state measurement performed at the fiber’s midpoint. To protect quantum fidelity from spontaneous Raman scattering noise, we use optimal O-band quantum channels, narrow spectro-temporal filtering, and multi-photon coincidence detection. Fidelity is shown to be well maintained with an elevated C-band launch power of 18.7 dBm for the single-channel 400-Gbps signal, which we project could support multiple classical channels totaling many terabits/s aggregate data rates. These results show the feasibility of advanced quantum and classical network applications operating within a unified fiber infrastructure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

GLUE Code: A framework handling communication and interfaces between scales

Many scientific applications are inherently multiscale in nature. Such complex physical phenomena often require simultaneous execution and coordination of simulations spanning multiple time and length scales. This is possible by combining expensive small-scale simulations (such as molecular dynamics simulations) with larger scale simulations (such continuum limit/hydro solvers) to allow for considerably larger systems using task and data parallelism. However, the granularity of the tasks can be very large and often leads to load imbalance. Traditionally, we use approximations to streamline the computation of the more costly interactions and this introduces trade-offs between simulation cost and accuracy. In recent years, the available computational power and the advances in machine learning have made computing these scale-bridging interactions and multiscale simulations more feasible. One driving application has been plasma modeling in inertial confinement fusion (ICF), which is fundamentally multiscale in nature. This requires deep understanding of how to extrapolate microscopic information into macroscopically relevant scales. For example, in ICF one needs an accurate understanding of the connection between experimental observables and the underlying microphysics. The properties of the larger scales are often affected by the microscale behavior incorporated usually into the equations of state and ionic and electronic transport coefficients (Liboff, 1959; Rinderknecht et al., 2014; Rosenberg et al., 2015; Ross et al., 2017). Instead of incorporating this information using reliable molecular dynamics (MD) simulations, one often needs to use theoretical models, due to the inability of MD to reach engineering scales (Glosli et al., 2007; Marinak et al., 1998). One approach to resolve this issue is by coupling two MD simulations of different scales via force interpolation, e.g., the AdResS method (Krekeler et al., 2018; Nagarajan et al., 2013). Another approach, which we will pursue in the scope of this work, is by enabling scale bridging between MD simulations and meso/macro-scale models through the development and support of application programming interfaces that these different applications can interact through.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Identification of Critical Process Parameters for Knife Milling and Alternative Communication Strategies

An assessment of knife milling operations was performed to identify parameters that require consideration to model wear and degradation of knife blades. A quality-by-design (QbD) paradigm was followed to identify material and feedstock attributes, and processing parameters that impact the quality attributes of the blades used in the comminution of biomass into forms compatible with downstream operations. Based on analytical and experimental observations of knife and tool wear, a QbD model is proposed that considers three wear mechanisms (erosion, abrasion, and gouging) to predict volumetric wear of knife blades and sharpness. These factors, if properly applied, provide a science-based approach to predict the mechanical efficiency of the mill as a function of feedstock properties, knife blade material properties, and processing parameters (feed rate, speed, and design parameters).

09 BIOMASS FUELS↗
DOE OSTI2021

A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

High-resolution predictions of hydrometeorological variables are critical for supporting hydropower generation decisions and flood control at hydroelectric power plants. Traditional climate and hydrologic models rely on the numerical simulation of detailed physical processes. Therefore, running these simulations is time-, labor-, and computation-intensive. Improving the spatial and temporal resolution in these modeling outputs could lead to cubic increases in both the simulation time and computational demands, rendering high-resolution hydrometeorological predictions expensive and impractical. Many past studies apply the super resolution (SR) technique to downscale climate models using deep learners. However, deep learners are deemed “black-boxes,” as their derivation processes from low-resolution outputs to high-resolution outputs are often hidden. Their results are difficult for domain scientists to interpret and validate. Thus, there is a need for an exploratory machine learning approach that can partially integrate domain-specific theory and knowledge into the data-driven mapping process between simulation outputs of different spatial scales. The domain-specific theory and knowledge can be incorporated into the data model through an inductive approach in which process-related environmental variables are used and analyzed as key drivers (i.e., environmental surrogates) to reflect the complex physical processes. Many of these variables, such as land use land cover, soil types, topography, digital elevation, air temperature, and various watershed characteristics, can be directly measured through sensors or remote sensing techniques. Additionally, SR applications that can downscale hydrological and hydrodynamics models to efficiently produce high-resolution (1 m) flood depth grids are still rare. Since the flood depth grid can be used to support critical decisions for flood control operation at hydroelectric power plants, it is crucial to enable an SR-based capability for interpolating high-resolution flood inundation maps.

13 HYDRO ENERGY↗
DOE OSTI2020

Information Products & Communication Materials for SSEB, SGA, SLC and Partners [Slides]

Contents include the following: 1) Southeastern Regional Carbon Sequestration Partnership (SECARB)–Southern States Energy Board's 48th Annual Meeting, August 8, 2008; 2) Southern States Energy Board Carbon Management Program SSEB Associate Members Meeting, August 21, 2009; 3) Carbon Capture and Sequestration (CCS) Demonstration Project, August 21, 2009; 4) Clean Energy in the Southern Region, August 27, 2010; 5) Carbon Management–Southern Leadership of a Regional Partnership, October 17, 2011; 6) Southeastern Regional Carbon Sequestration Partnership "Knowledge Sharing", August 10, 2012; 7) Southeastern Regional Carbon Sequestration Partnership; 8) Site Visit by Southern Legislative Group, July 30, 2013; 9) SECARB Awards Anthropogenic Test, March 4, 2014; 10) Southern Company/MHI Ltd. 500 TPD CCS Demonstration; 11) SECARB Anthropogenic Test Project Assessment, March 4-5, 2014; 12) Advanced Monitoring Technologies SECARB Phase III CO 2 Storage Citronell, Alabama, March 4, 2014; 13) Geomechanics at Cranfield, March 5, 2014; 14) Gulf Coast Carbon Center Near-Surface Update, March 4-5, 2014; 15) Monitoring Design Best Practices, March 5, 2014; 16) Special Projects–Carbon Management Program, 2015; 17) Special Projects, November 2-3, 2016; 18) Carbon Management Program Carbon Capture & Storage Projects, September 24, 2017; 19) Remarks Before the 58th Annual Meeting of the Executive Committee, September 16, 2018; 20) Annual Business Session, September 24, 2019; and 21) Recent Activities & 2019-2020 Elections Insights, February 7, 2020.

01 COAL, LIGNITE, AND PEAT↗