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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cobalt‐Doped Bismuth Nanosheet Catalyst for Enhanced Electrochemical CO 2 Reduction to Electrolyte‐Free Formic Acid

Electrochemical carbon dioxide (CO 2 ) reduction reaction (CO 2 RR) to valuable liquid fuels, such as formic acid/formate (HCOOH/HCOO − ) is a promising strategy for carbon neutrality. Enhancing CO 2 RR activity while retaining high selectivity is critical for commercialization. To address this, we developed metal-doped bismuth (Bi) nanosheets via a facile hydrolysis method. These doped nanosheets efficiently generated high-purity HCOOH using a porous solid electrolyte (PSE) layer. Among the evaluated metal-doped Bi catalysts, Co-doped Bi demonstrated improved CO 2 RR performance compared to pristine Bi, achieving ~90 % HCOO − selectivity and boosted activity with a low overpotential of ~1.0 V at a current density of 200 mA cm −2 . In a solid electrolyte reactor, Co-doped Bi maintained HCOOH Faradaic efficiency of ~72 % after a 100-hour operation under a current density of 100 mA cm −2 , generating 0.1 M HCOOH at 3.2 V. Density functional theory (DFT) results revealed that Co-doped Bi required a lower applied potential for HCOOH generation from CO 2 , due to stronger binding energy to the key intermediates OCHO* compared to pure Bi. In conclusion, this study shows that metal doping in Bi nanosheets modifies the chemical composition, element distribution, and morphology, improving CO 2 RR catalytic activity performance by tuning surface adsorption affinity and reactivity.

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

Building Durable Multimetallic Electrocatalysts from Intermetallic Seeds

When combined with earth-abundant metals, Pt-based alloy nanoparticles (NPs) can be cost-effective electrocatalysts. However, these NPs can experience leaching of non-noble-metal components under harsh electrocatalytic conditions. In this work, the Skrabalak group has demonstrated a novel NP construct in which Pt-based random alloy surfaces are stabilized against non-noble-metal leaching by their deposition onto intermetallic seeds. These core@shell NPs are highly durable electrocatalysts, with the ability to tune catalytic performance by the core@shell architecture, surface alloy composition, and NP shape. This versatility was demonstrated in a model system in which random alloy (ra-) PtM surfaces were deposited onto ordered intermetallic (i-) PdCu seeds using seed-mediated co-reduction (SMCR). In the initial demonstration, ra-PtCu shells were deposited on i-PdCu seeds, with these core@shell NPs exhibiting higher specific and mass activities for the oxygen reduction reaction (ORR) when compared to similarly sized ra-PtCu NPs. These NPs also showed outstanding durability, maintaining ~85% in specific activity after 5000 cycles. Characterization of the NPs after use revealed minimal loss of Cu. The activity enhancement was attributed to the strained surface that arises from the lattice mismatch between the intermetallic core and random alloy surface. The outstanding durability was attributed to the ordered structure of the intermetallic core. The origin of this durability enhancement was investigated by classical molecular dynamics simulations, where Pt atoms were found to have a lower potential energy when deposited on an intermetallic core than when deposited on a random alloy core. Also, ordering of Cu atoms at the core@shell interface appears to enhance the overall binding between the core and the shell materials. Inspired by this initial demonstration, SMCR has been used to achieve shells of different random alloy compositions, PtM (M = Ni, Co, Cu, or Fe). This advance is significant because ligand effects vary as a function of PtM identity and Pt/M ratio. These features also influence the degree of surface strain imparted from the lattice mismatch between the core and shell materials. Like the initial demonstration, standout features of these core@shell NPs were high durability and resistance to non-noble metal leaching. Moving forward, efforts have been directed toward integrating shape-control to this core@shell NP construct. This integration is motivated by the shape-dependent catalytic performance of NPs derived from the selective expression of specific facets. Considering the initial i-PdCu@ra-PtCu system, NPs with a cubic shape have been achieved by judicious selection of capping ligands during SMCR. Evaluation of these NPs as catalysts for the electrooxidation of formic acid found that the nanocubic shape enhances catalytic performance compared to similar core@shell NPs with a spherical morphology. We envision that SMCR can be applied to other NP systems to achieve highly durable catalysts as the syntheses of monodisperse and shape-controlled intermetallic seeds are advanced. This Account highlights the role of intermetallic cores in providing more durable electrocatalysts. More broadly, the versatility of SMCR is highlighted as a route to integrate architecture, alloy surfaces, and shape within one NP system, and how this achievement is inspiring new high-performance and robust catalysts is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Influence of Linker Identity on the Photochemistry of Uranyl-Organic Frameworks

While uranyl-based metal–organic frameworks (MOFs) boast impressive photocatalytic abilities, significant questions remain regarding their excitation pathways and methods to fine-tune their performance due to the lack of information regarding heterogeneous uranyl catalysis. Herein, we investigated how linker identity and photoexcitation impact uranyl photocatalysis when the uranyl coordination environment remains constant. Toward this end, we prepared three uranyl-based MOFs (NU-1301, NU-1307, and ZnTCPP-U2) and then examined the structural and photochemical properties of each through X-ray diffraction, X-ray absorption, and photoluminescence. We then correlated our observations to the photocatalytic performance for fluorination of cyclooctane. The excitation profile from NU-1301 and NU-1307 exhibited spin-forbidden linker transitions and uranyl vibronic progressions, with uranyl excitation and emission being most dominant in NU-1301. Consequently, NU-1301 was a more active photocatalyst than NU-1307. In contrast, the excitation profile from ZnTCPP-U2 contained transitions associated with the porphyrin linker exclusively. Photocatalytic activity from ZnTCPP-U2 significantly underperformed in comparison to that of the other two MOFs. Furthermore, these data suggest that linkers’ photophysical properties can be used to predict the photocatalytic behavior of uranyl-containing MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enhanced rare-earth separation with a metal-sensitive lanmodulin dimer

Technologically critical rare-earth elements are notoriously difficult to separate, owing to their subtle differences in ionic radius and coordination number. The natural lanthanide-binding protein lanmodulin (LanM) is a sustainable alternative to conventional solvent-extraction-based separation. Here we characterize a new LanM, from Hansschlegelia quercus (Hans-LanM), with an oligomeric state sensitive to rare-earth ionic radius, the lanthanum(III)-induced dimer being >100-fold tighter than the dysprosium(III)-induced dimer. X-ray crystal structures illustrate how picometre-scale differences in radius between lanthanum(III) and dysprosium(III) are propagated to Hans-LanM’s quaternary structure through a carboxylate shift that rearranges a second-sphere hydrogen-bonding network. Comparison to the prototypal LanM from Methylorubrum extorquens reveals distinct metal coordination strategies, rationalizing Hans-LanM’s greater selectivity within the rare-earth elements. Finally, structure-guided mutagenesis of a key residue at the Hans-LanM dimer interface modulates dimerization in solution and enables single-stage, column-based separation of a neodymium(III)/dysprosium(III) mixture to >98% individual element purities. This work showcases the natural diversity of selective lanthanide recognition motifs, and it reveals rare-earth-sensitive dimerization as a biological principle by which to tune the performance of biomolecule-based separation processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scalable, biologically sourced depolymerizable polydienes with intrinsically weakened carbon–carbon bonds

Currently, there are few examples of circularly recyclable polymers with all-carbon backbones, probably owing to the challenge of using selective C–C bond cleavage to efficiently produce monomers in recycling processes. Furthermore, here we demonstrate a series of biologically sourced polymuconate polymers synthesized via simple free-radical polymerization that exhibit intrinsically weakened C–C bonds and controlled chemical recycling to monomers. Modifying the side chains and copolymerization ratios allows a wide range of mechanical property tuning, achieving performances comparable to those of commercial plastics such as polystyrene, polymethyl methacrylate and polybutadiene. Techno-economic analysis and life cycle assessment for production at a scale of 100 kilotons per year show that the materials are currently slightly more expensive and environmentally intensive compared with conventional rubbers. However, use of recycled materials via depolymerization can greatly decrease the cost and environmental impacts of polymuconate production (for example, down to US$1.59 per kilogram) to outperform its commercial counterparts.

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

Disentangling cation effects on ion mobility and structure in ionic liquid electrolytes

Ionic liquids (ILs) are low-temperature molten salts, where ion transport is primarily governed by ion–ion interactions. Yet, the impact of organic IL cations on critical electrolyte properties such as ion dissociation and overall transport behavior in lithium-salt-doped ILs remains poorly understood. Moreover, despite their critical role in designing IL-based electrolytes for energy storage applications, ion–ion interactions and ion-specific transport under an applied electrical potential are seldom quantified, largely due to the unique experimental and computational challenges involved. Herein, we compare transport properties obtained using 1 H, 7 Li, and 19 F pulsed-field gradient nuclear magnetic resonance (NMR) and electrophoretic NMR (eNMR) with those measured by electrochemical impedance spectroscopy. Non-equilibrium molecular dynamics (MD) simulations and eNMR confirm the presence of negatively charged [Li(TFSI) n ] (1−n) aggregates that migrate towards the positive electrode, resulting in negative lithium transference numbers. Equilibrium MD simulations reveal a vehicular Li ion transport mechanism facilitated by long-lived aggregates with Li + cations strongly bound to multiple TFSI − anions. Finally, we observe an inverse relationship between the apparent charge of the TFSI − anion in the neat IL, which is dictated by the IL cation, and Li + transport in the salt-doped systems. This highlights the opportunity to tune electrolyte performance by tailoring cation chemistry.

Li-ion batteries↗
DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Experimental demonstration of accelerating a beam with a large transverse emittance ratio in the relativistic heavy ion collider for the electron-ion collider

The electron-ion collider (EIC), to be constructed at Brookhaven National Laboratory, will collide polarized high-energy electron beams with hadron beams, achieving luminosities of up to 1.0 × 10 34 cm −2 s −1 in the center-of-mass energy range of 20–140 GeV. To reach such high luminosity, the EIC will employ small, flat beams at the interaction point. According to the design of the EIC hadron storage ring (HSR), hadron beams with a large transverse emittance ratio of 11:1 will be generated at the injection energy using an electron cooling technique and then accelerated to high energies for collisions. Accelerating hadron beams with such a large emittance ratio had never been demonstrated elsewhere—until our recent beam experiment at the relativistic heavy ion collider (RHIC). In this experiment, we successfully generated a large transverse emittance ratio of 13:1 with a gold-ion beam at 31 GeV/nucleon using stochastic cooling. We then accelerated this beam, with a transverse emittance ratio of 11:1, from 31 to 100 GeV/nucleon. Thanks to RHIC’s high-performance orbit, tune, and decoupling feedback systems, the large emittance ratio was well maintained throughout the 5-min-long acceleration process. This experiment fully validated the EIC/HSR design assumptions—namely, that large-emittance-ratio hadron beams can be generated at injection energy and then accelerated to high energies for collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Quantum properties of non-Dirichlet boundary conditions in gravity

The Euclidean path integral for gravity is enriched by the addition of boundaries, which provide useful probes of thermodynamic properties. Common boundary conditions include Dirichlet conditions on the boundary induced metric; microcanonical conditions, which refers to fixing some components of the Brown-York boundary stress tensor; and conformal conditions, in which the conformal structure of the induced metric and the trace of the extrinsic curvature are fixed. Boundaries also present interesting problems of consistency. The Dirichlet problem is known, under various (and generally different) conditions, to be inconsistent with perturbative quantization of graviton fluctuations, to exhibit thermodynamic instability, or to require infinite fine-tuning in the presence of matter fluctuations. We extend some of these results to other boundary conditions. We find that similarly to the Dirichlet problem, the graviton fluctuation operator is not elliptic with microcanonical boundaries, and the nonelliptic modes correspond to “boundary-moving diffeomorphisms.” However, we argue that microcanonical factorization of path integrals—essentially, the insertion of microcanonical constraints on two-sided surfaces in the bulk—is not affected by the same issues of ellipticity. We also show that for a variety of matter field boundary conditions, matter fluctuations renormalize the gravitational bulk and boundary terms differently, so that the classical microcanonical or conformal variational problems are not preserved unless an infinite fine-tuning is performed.

Draper, Patrick [Univ. of Illinois at Urbana-Champ↗
DOE OSTI2022

Topology Optimization of 3D Flow Fields for Flow Batteries

We report as power generated from renewables becomes more readily available, the need for power-efficient energy storage devices, such as redox flow batteries, becomes critical for successful integration of renewables into the electrical grid. An important aspect of a redox flow battery is the planar flow field, which is usually composed of two-dimensional channels etched into a backing plate. As reactant-laden electrolyte flows into the flow battery, the channels in the flow field distribute the fluid throughout the reactive porous electrode. We utilize topology optimization to design flow fields with full three-dimensional geometry variation, i.e., 3D flow fields. Specifically, we focus on vanadium redox flow batteries and use the optimization algorithm to generate 3D flow fields evolved from standard interdigitated flow fields by minimizing the electrical and flow pressure power losses. To understand how these 3D designs improve performance, we analyze the polarization of the reactant concentration and exchange current within the electrode to highlight how the designed flow fields mitigate the presence of electrode dead zones. While interdigitated flow fields can be heuristically engineered to yield high performance by tuning channel and land dimensions, such a process can be laborious; this work provides a framework for automating that design process.

25 ENERGY STORAGE↗
DOE OSTI2022

Scalable deep learning for watershed model calibration

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The 2023 National Offshore Wind data set (NOW-23)

Abstract. This article introduces the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. In this article, we present the new data set which underwent regional tuning and performance validation against available observations and has data available from 2000 through, depending on the region, 2019–2022. We also provide a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404 (Bodini et al., 2020).

17 WIND ENERGY↗
DOE OSTI2025

Performance Impact and Trade-Offs for Tuning Key Architectural Parameters on CPU+GPU Systems

In this work, we performed an initial design space exploration of an accelerated processing unit (APU)—a hybrid CPU+GPU architecture that integrates both compute units (CUs) and memory into a unified system. This integration aims to reduce data movement, enhance memory locality, and improve energy efficiency by enabling the CPU and GPU to share memory directly. This effort focused on the interplay of key design components—cache line size, the number of CUs, and main memory technology—and the trade-offs of each configuration were analyzed. This paper highlights the various configurations’ impact on memory accesses, data reuse, and power utilization. The results provide valuable insights that can be leveraged to optimize APU architectures for high-performance and energy-efficient computing and thus create a balanced architecture. This optimization can be achieved by adopting dynamic cache management, runtime CU scaling, and advanced memory integration, highlighting the potential of APUs to address critical challenges in compute, data movement, and memory power consumption.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗
DOE OSTI2023

Gradient-Descent-Based Learning Gain for Backstepping Controller and Disturbance Observer of Nonlinear Systems

This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-to-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.

42 ENGINEERING↗
DOE OSTI2025

Inertial Confinement Fusion Design Search Using Bayesian Optimization

Inertial confinement fusion (ICF) experiments rely on complex multi-physics simulation codes such as the Lawrence Livermore National Laboratory-developed HYDRA to guide design work. However, these simulations have several dozen tunable parameters and can be computationally expensive. This makes searching the parameter space challenging and time-consuming. Recently developed automated tools utilize Bayesian optimization to search these high-dimensional parameter spaces for optimal designs. The optimization tools run 2D integrated simulations in HYDRA to converge on a design that produces specified scalar or vector outputs. In this paper, we apply the Bayesian optimization tools to two common tuning scenarios. First, we tune simulation inputs to match measurements of a well-characterized experiment at the National Ignition Facility. This type of tuning is commonly performed to compensate for the use of simplified simulation settings (e.g. reduced resolution) or to account for missing physics in the simulations. Second, we search for an ICF simulation design that has a particular radiation drive profile. These optimizations replicate the kinds of tuning researchers routinely perform, but do so with significantly reduced manual effort. This approach demonstrates a powerful and efficient pathway toward autonomous, high-fidelity design optimization for future ICF experiments.

Bayesian optimization↗
DOE OSTI2022

Verifying the structure-property-performance relationship of Y6-based small molecule acceptors by alkoxy-side chain isomerization and conjugated skeleton asymmetry

Non-fullerene small molecule acceptors (NF-SMAs) such as Y6 derivatives were the current working horse for the top performing polymer solar cells (PSCs). For this reason, numerous chemical modifications have been explored for establishing structure–property-performance relationship for Y6-based NF-SMAs. In this work, we explored the isomerization on asymmetrical Y6 derivatives by varying the position of alkoxy side chain on the thiophene linkage between the core and one of the end groups of Y6. The isomers with differences in conformational restriction raised by noncovalent interactions as well as steric hindrance revealed distinctive behavior in crystallization and polymorphism, which led to a significant contrast in PSC performances. Here the X-TO1 molecule with alkoxy side chain facing the end group (outward) displayed preferred molecular configuration and polymorph, delivering power conversion efficiency (PCE) of 15.63%, while its isomer X-TO2 showed coexisted polymorphs significantly increased the charge recombination in the PSC devices led to a low PCE around 3%. This structure–property-performance relationship not only highlighted the elegancy of isomerization in tuning the photovoltaic performances but also identified polymorphism as one of the causes for impairing photovoltaic performances of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Fluorinated Lewis Acidic Organoboron Tunes Polysulfide Complex Structure for High–Performance Lithium–Sulfur Batteries

Many challenges in lithium-sulfur (Li–S) batteries are associated with the radical change in lithium polysulfide (LPS) solubility during cycling, but chemical approaches to address such inconsistency are still lacking. Here, the use of a strong Lewis acidic fluorinated organoboron, tri(2,2,2-trifluoroethyl) borate (TFEB), is reported as a multi-functional mediator to simultaneously overcome multiple technical barriers in practical Li–S batteries. TFEB acts as an anion acceptor and forms strong molecular complexes with Lewis basic LPS. The TFEB-LPS complexes have consistent solubility across the full polysulfide spectrum and deliver several times improved better redox kinetics, unlocking a true redox catalytic mechanism that covers the majority of redox events in thick sulfur cathodes. As a result, Li–S batteries evaluated under practical conditions exhibit significantly improved discharge capacity, rate capability, and cycling stability with the addition of the TFEB additive. More importantly, TFEB also contributes to the stabilization of lithium anode in the presence of polysulfides by generating strong interfacial film. These attributes significantly improve the cycling stability of practical Li–S pouch cells, which are assembled with a unit energy density of 219 Wh kg –1 . Finally, the results provide new molecular insights on the design of unlocking solvation networks of practical Li–S systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗