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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Direct numerical simulations of three-component Rayleigh–Taylor mixing and an improved model for multicomponent reacting mixtures

We present direct numerical simulations of a three-layer Rayleigh–Taylor instability (RTI) problem with a configuration based on the experiments of Suchandra & Ranjan ( J. Fluid Mech. , vol. 974, 2023, A35) and Jacobs & Dalziel ( J. Fluid Mech. , vol. 542, 2005, pp. 251–279). The problem consists of a layer of light fluid between two layers of heavy fluid with an Atwood number of 0.3. These simulations are first validated through comparison with available experimental data. The validated simulations are then utilized to analyse statistics in this three-component flow. First, length scales are examined utilizing spectra and two-point spatial correlations of velocity and species concentration fluctuations. Next, joint probability density functions (p.d.f.s) of species concentration are compared against several model p.d.f.s representing generalizations of the bivariate beta distribution. Notably, the joint p.d.f.s do not appear to be accurately described by a Dirichlet distribution, indicating the marginal distributions do not conform to a beta distribution. Finally, similarity of the present configuration to three-component mixing found in inertial confinement fusion (ICF) applications is exploited to develop and validate an improved model for the impact of multicomponent mixing on thermonuclear (TN) reaction rates. A single time instant from the present simulations is chosen for a TN burn calculation under the hypothetical assumption of ICF materials and temperatures. Total TN output from this second calculation is then compared against the prediction of the improved model. The new model is found to accurately predict TN reaction rates in both premixed and non-premixed configurations.

42 ENGINEERING↗
DOE OSTI2023

Improved representation of laminar and turbulent sheet flow in subglacial drainage models

Subglacial hydrology models struggle to reproduce seasonal drainage patterns that are consistent with observed subglacial water pressures and surface velocities. We modify the standard sheet-flow parameterization within a coupled sheet–channel subglacial drainage model to smoothly transition between laminar and turbulent flow based on the locally computed Reynolds number in a physically consistent way (the ‘transition’ model). We compare the transition model to standard laminar and turbulent models to assess the role of the sheet-flow parameterization in reconciling observed and modelled water pressures under idealized and realistic forcing. Relative to the turbulent model, the laminar and transition models improve seasonal simulations by increasing winter water pressure and producing a more prominent late-summer water pressure minimum. In contrast to the laminar model, the transition model remains consistent with its own internal assumptions across all flow regimes. Based on the internal consistency of the transition model and its improved performance relative to the standard turbulent model, we recommend its use for transient simulations of subglacial drainage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning (DL) research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high-dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace–Beltrami equation on a high-dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. This paper is a significant extension of our earlier work published in NeurIPS, 2018. For reproducibility, the code is available at https://github.com/BaoWangMath/DNN-DataDependentActivation .

Mathematics↗
DOE OSTI2020

Improved Single-Cell Proteome Coverage Using Narrow-Bore Packed NanoLC Columns and Ultrasensitive Mass Spectrometry

Single-cell proteomics can provide unique insights into biological processes by resolving heterogeneity that is obscured by bulk measurements. Gains in the overall sensitivity and proteome coverage through improvements in sample processing and analysis increase the information content obtained from each cell, particularly for less abundant proteins. Here we report on improved single-cell proteome coverage through the combination of the previously developed Nanodroplet Processing in One Pot for Trace Samples (nanoPOTS) platform with further miniaturization of liquid chromatography (LC) separations and implementation of an ultrasensitive latest-generation mass spectrometry (MS) instrument. Following nanoPOTS sample preparation, protein digest from single cells were separated using a 20-µm-i.d. in-house-packed nanoLC column. Separated peptides were ionized using an etched fused silica emitter capable of stable ionization at the ~20 nL/min flow rate provided by the LC separation. Ultrasensitive LC-MS analysis was achieved using the Orbitrap Eclipse Tribrid mass spectrometer. An average of 362 protein groups were identified by MS/MS from single HeLa cells, and 874 protein groups were identified using the Match Between Runs (MBR) feature of MaxQuant. This represents a >70% increase in label-free proteome coverage for single cells relative to previous efforts using larger-bore (30-µm-i.d.) LC columns coupled to a previous-generation MS (Orbitrap Fusion Lumos).

Cong, Yongzheng↗
DOE OSTI2022

Dissolving Nonionic Surfactants in CO 2 to Improve Oil Recovery in Unconventional Reservoirs via Wettability Alteration

CO 2 injection is a promising method for enhanced oil recovery (EOR) in unconventional shale reservoirs. Here, in this work, we postulate that CO 2 EOR may be improved by the dissolution of surfactants into CO 2 . Although CO 2 is a relatively good solvent for oil, we show that CO 2 and Eagle Ford oil are immiscible at compositions above 70 wt % CO 2 , even at pressures as high as 62 MPa. The presence of a CO 2 –oil interface at reservoir conditions indicates that the addition of a surfactant has the potential to improve oil recovery–via wettability alteration from oil-wet to CO 2 -wet, CO 2 –oil interfacial tension (IFT) reduction, or both. Three nonionic surfactants (branched tridecyl ethoxylate Indorama SURFONIC TDA-9, branched nonylphenol ethoxylate Indorama SURFONIC N-100, and linear dodecyl ethoxylate Indorama SURFONIC L12-6) were evaluated for CO 2 -solubility, shale wettability alteration, effect on CO 2 –oil IFT, ability to generate CO 2 –oil foams, and ability to increase oil extraction from Eagle Ford, Mancos, and Bakken shale cores. Each surfactant dissolved in CO 2 up to 1 wt % at pressures and temperatures commensurate with CO 2 EOR. CO 2 -dissolved surfactants did not significantly affect CO 2 –oil IFT or generate CO2–oil foams, but they did induce a dramatic change in the contact angle of an oil droplet on an oil-aged shale chip in CO 2 from strongly oil-wet (11°) toward intermediate CO 2 –oil wettability (82°) (at 80 °C, 27.6 MPa). The branched tridecyl ethoxylated surfactant, SURFONIC TDA-9, afforded the highest oil recovery in core soaking experiments–75%, compared to 71% by pure CO 2 . Analysis of oil extracts by gas chromatography revealed that heavier oil components were produced when the surfactant was added to CO 2 . These results indicate that CO 2 -dissolved surfactants may increase oil recovery from shale by wettability alteration from oil-wet toward CO 2 -wet.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

Improving Conversion of Captured CO 2 on Mixed-Oxide Dual-Function Materials by Low-Concentration Metal Promotion

Reactive carbon capture (RCC) is a promising nascent approach to harness the untapped resource of waste CO 2 to produce fuels and platform chemicals. However, process economics relies on the design of dual-function materials (DFMs) that exhibit maximal conversion of captured CO 2 and high selectivity toward valuable carbon products (e.g., CO). Herein, we report the use of low-concentration (ca. 0.5 wt %) metal promotors (Pt and Au) to improve the conversion of captured CO 2 on a Zn-Al mixed-oxide DFM by >30%, while also more than doubling per-cycle CO yield due to enhanced retention of captured CO 2 and H 2 activation. In situ DRIFTS of bound CO 2 on the DFMs showed distinctions in surface carbonate formation for the Pt- and Au-modified materials that were correlated with product evolution during the reactive desorption step of RCC. Particularly, Pt afforded superior H 2 activation but formed irreversibly-bound bicarbonates, compromising overall productivity, while the Na/Au/ZA DFM predominantly adsorbed CO 2 on Na sites, resulting in polydentate carbonates that are retained until higher temperatures before conversion to CO. In conclusion, these results indicate that adding a low concentration of oxidation-resistant noble metals, especially Au, is an effective strategy for improving key performance metrics of Zn-Al mixed-oxide DFMs to further advance the development of scalable RCC technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

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

Systematic Improvement of Quantum Monte Carlo Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gap Prediction

Accurate determination of the electronic properties of correlated oxides remains a significant challenge for computational theory. Traditional Hubbard-corrected density functional theory (DFT+U) frequently encounters limitations in precisely capturing electron correlation, particularly in predicting band gaps. We introduce a systematic methodology to enhance the accuracy of diffusion Monte Carlo (DMC) simulations for both ground and excited states, focusing on LiCoO 2 as a case study. By employing a selected configuration interaction (sCI) approach, we demonstrate the capability to optimize wavefunctions beyond the constraints of single-reference DFT+U trial wavefunctions. Here, we show that the sCI framework enables accurate prediction of band gaps in LiCoO 2 , closely aligning with experimental values and substantially improving traditional computational methods. The study uncovers a nuanced mixed state of t 2g and e g orbitals at the band edges that is not captured by conventional single-reference methods, further elucidating the limitations of PBE+U in describing d-d excitations. Our findings advocate for the adoption of beyond-DFT methodologies, such as sCI, to capture the essential physics of excited-state wavefunctions in strongly correlated materials. The improved accuracy in band gap predictions and the ability to generate more reliable trial wavefunctions for DMC calculations underscore the potential of this approach for broader applications in the study of correlated oxides. This work not only provides a pathway for more accurate simulations of electronic structures in complex materials but also suggests a framework for future investigations of the excited states of other challenging systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Local Spin Density Approximation Strongly Improved by a Better-Informed Local Scaling of Its Self-Interaction Correction

The Perdew−Zunger self-interaction correction (PZSIC) makes density functional approximations (DFAs) exact for all one-electron densities. However, it overcorrects in manyelectron regions, introducing errors for the uniform-density limit, where uncorrected DFAs are exact. The locally scaled PZSIC (LSIC), based on the iso-orbital indicator zσ [which distinguishes single-orbital and slowly varying density regions and is used with the local spin density approximation (LSDA)], restores the uniform-density limit and significantly improves results for many properties, including chemical reaction barrier heights, atomization energies, and ionization potentials. Yet, LSIC performs poorly for weakly bonded systems, leaving many unbound, due to limitations of its iso-orbital indicator. To correct this, in this work we propose a new local scaling, LSIC-α, based on the iso-orbital indicator ασ (which additionally identifies regions of overlapping density tails). A two-parameter scaling function of ασ is fitted to a subset of the nonbonded appropriate norms for the SCAN and r2SCAN meta- GGAs, and tested on many properties of main-group atoms, molecules, and molecular complexes. LSIC-α greatly improves the interaction energies of weakly bonded systems in the S22 data set while retaining LSIC’s accuracy for other properties. This work shows that the errors of LSDA (and presumably of higher-level DFAs) can be largely but not entirely repaired by a proper “do no harm” self-interaction correction.

Approximation↗
DOE OSTI2024

Adjusting the Energy Profile for CH–O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Further many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Reparameterizing a Lipid Force Field Using Small-Angle X-ray Scattering to Improve Predictions of Multicomponent Membranes under Organic Solvent Stress

Understanding and predicting properties of lipid-bilayer membranes are essential to elucidating organismal physiology and pathophysiology. Therefore, substantial efforts have been undertaken to derive accurate molecular-mechanics force fields (FF) to allow simulation of their properties; however, much of these past efforts focused on tuning force fields to accurately reproduce the properties of model single-component membranes and not multicomponent or stressed membranes. Here, we tuned the CHARMM36 FF for a simple 2-component model of a Gram-positive bacterium. This updated force field is parametrized against a single condensed-phase property, the small-angle X-ray scattering (SAXS) intensities of the 2-component lipid system, using a version of ForceBalance implemented previously (named ForceBalance-SAS) with SAXS and small-angle neutron scattering (SANS) intensities as optimization targets. After tuning, we observe improved agreement with the experimental SAXS of the membrane under 1-butanol and tetrahydrofuran solvent stresses, with a factor of 10.0 and 7.5 reduction, respectively, in χ 2 , the measure of the discrepancy between experimental and computed SAXS intensities. Furthermore, this reparametrized force field yielded improved agreement between experimental and simulated membrane thicknesses for the pure lipid systems. However, we note limitations in transferability and diagnose the source of such limitations, particularly the need for SANS in addition to SAXS.

Demerdash, Omar N. A. [Oak Ridge National Laborato↗
DOE OSTI2023

Improving Oxygen Reduction Performance of Surface-Layer-Controlled Pt–Ni Nano-Octahedra via Gaseous Etching

This study demonstrates an atomic composition manipulation on Pt–Ni nano-octahedra to enhance their electrocatalytic performance. By selectively extracting Ni atoms from the {111} facets of the Pt–Ni nano-octahedra using gaseous carbon monoxide at an elevated temperature, a Pt-rich shell is formed, resulting in an ~2 atomic layer Pt-skin. The surface-engineered octahedral nanocatalyst exhibits a significant enhancement in both mass activity (~1.8-fold) and specific activity (~2.2-fold) toward the oxygen reduction reaction compared with its unmodified counterpart. After 20,000 potential cycles of durability tests, the surface-etched Pt–Ni nano-octahedral sample shows a mass activity of 1.50 A/mg Pt , exceeding the initial mass activity of the unetched counterpart (1.40 A/mg Pt ) and outperforming the benchmark Pt/C (0.18 A/mg Pt ) by a factor of 8. DFT calculations predict this improvement with the Pt surface layers and support these experimental observations. Therefore, this surface-engineering protocol provides a promising strategy for developing novel electrocatalysts with improved catalytic features.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Chemical and Physical Drivers for Improvement in Permeance and Stability of Linker-Free Graphene Oxide Membranes

Graphene oxide (GO) is a promising membrane material for chemical separations, including water treatment. However, GO has often required postsynthesis chemical modifications, such as linkers or intercalants, to improve either the permeability, performance, or mechanical integrity of GO membranes. In this work, we explore two different feedstocks of GO to investigate chemical and physical differences, where we observe up to a 100× discrepancy in the permeability–mass loading trade-off while maintaining nanofiltration capacity. GO membranes also show structural stability and chemical resilience to harsh pH conditions and bleach treatment. Here we probe GO and the resulting assembled membranes through a variety of characterization approaches, including a novel scanning-transmission-electron-microscopy-based visualization approach, to connect differences in sheet stacking and oxide functional groups to significant improvements in permeability and chemical stability.

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

Improved Single-Ion Conductivity of Polymer Electrolyte via Accelerated Segmental Dynamics

Single-ion conducting polymer electrolytes (SICPEs) have many advantages for solid-state battery applications, while low conductivities remain the bottleneck for their practical applications. In this paper, a strategy that can significantly improve the ionic conductivity of SICPEs based on the concept of accelerated segmental dynamics was demonstrated by the evaluation of the thermal property, rheological behavior, morphology analysis, molecular dynamics simulation, and conductivity performance. With both “soft” poly(dimethylsiloxane) (PDMS) backbone and poly(ethylene glycol) (PEG) side chains, the obtained SICPE possesses faster segmental relaxation and higher lithium-ion conductivity. With lithium “transference” number close to unity, the obtained SICPE also shows excellent electrochemical stability against lithium metal electrodes. The clear relationship established between the segmental dynamics of polymer electrolyte and its ionic conductivity should contribute to achieve a solid electrolyte with improved ionic conductivity toward the next-generation solid-state battery.

Segmental Dynamics↗
DOE OSTI2020

Improving Interface Stability of Si Anodes by Mg Coating in Li-Ion Batteries

Silicon (Si) is a promising anode material for high-energy-density lithium-ion batteries (LIBs), but its short calendar life and poor cycling performance prevent its large-scale adoption. Introducing magnesium (Mg) salt into the electrolyte has been recently shown to form a ternary Li–Mg–Si Zintl phase upon lithiation of Si and improve the cycling performance. However, the ternary Zintl phase formation mechanism and its impact on the solid electrolyte interphase (SEI) are not yet well understood. In this work, we demonstrate the formation of a ternary Li–Mg–Si Zintl phase by Mg coating of the Si anode, where Mg diffuses into the Si film upon deposition and intermixes further during the lithiation process. The presence of the Zintl phase improves the interface stability, alters the nature of the SEI, and enhances the cycling performance of the Si anode. This study provides insights into the formation mechanism of the ternary Zintl phase and guidelines for the future design of Si anodes.

25 ENERGY STORAGE↗
DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗
DOE OSTI2026

Strategic Melamine Coating on Lithium Metal for Li 3 N-Rich Solid Electrolyte Interphase and Improved Battery Cycling Stability

The practical applications of lithium (Li) metal batteries (LMBs) are limited by challenges such as dendrite formation and unstable solid electrolyte interphase (SEI), especially at higher C-rates. Here, this study introduces melamine-coated Li metal anodes (LMAs), forming a Li 3 N-rich SEI layer that improves ionic conductivity and mechanical stability. The optimized melamine-coated LMA demonstrated uniform coverage resulting in denser Li deposition, nearly doubled cycle life (~148 cycles at 0.5 C, 1C = 4.1 mA cm -2 ), compared to Bare-Li. These findings emphasize that coating materials-induced beneficial SEI components could lead to improvement of LMB performance.

Batteries↗
DOE OSTI2020

Improving the Thermal Stability of NMC 622 Li-Ion Battery Cathodes through Doping During Coprecipitation

Increasing the Ni content of LiNi x MnyCo 1-x-y O 2 (NMC) cathodes can increase the capacity, but additional stability is needed to improve safety and longevity characteristics. In order to achieve this improved stability, Mg and Zr were added during the coprecipitation to uniformly dope the final cathode material. These dopants reduced the capacity of the material to some extent, depending on the concentration and calcination temperature. However, these dopants can impart substantial stabilization. It was found that the degree of stabilization is strongly dependent on the calcination temperature of the material. In addition, we used synchrotron X-ray diffraction during thermal breakdown to better understand why the different dopants impact the thermal stability and confirm the stabilization effects of the dopants.

25 ENERGY STORAGE↗