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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations

Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics‐informed in vitro – in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics‐informed bottom‐up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine‐trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle‐out optimization of mechanistic models.

Scotcher, Daniel↗
DOE OSTI2019

Single Fe Atom on Hierarchically Porous S, N-Codoped Nanocarbon Derived from Porphyra Enable Boosted Oxygen Catalysis for Rechargeable Zn-Air Batteries

Iron-nitrogen-carbon materials (Fe-N-C) are known for their excellent oxygen reduction reaction (ORR) performance. Unfortunately, they generally show a laggard oxygen evolution reaction (OER) activity, which results in a lethargic charging performance in rechargeable Zn-air batteries. Here porous S-doped Fe-N-C nanosheets are innovatively synthesized utilizing a scalable FeCl 3 -encapsulated-porphyra precursor pyrolysis strategy. The obtained electrocatalyst exhibits ultrahigh ORR activity ( E 1/2 = 0.84 V vs reversible hydrogen electrode) and impressive OER performance ( E j=10 = 1.64 V). The potential gap (Δ E = E j=10 - E 1/2 ) is 0.80 V, outperforming that of most highly active bifunctional electrocatalysts reported to date. Furthermore, the key role of S involved in the atomically dispersed Fe-N x species on the enhanced ORR and OER activities is expounded for the first time by ultrasound-assisted extraction of the exclusive S source (taurine) from porphyra. Moreover, the assembled rechargeable Zn-air battery comprising this bifunctional electrocatalyst exhibits higher power density (225.1 mW cm -2 ) and lower charging-discharging overpotential (1.00 V, 100 mA cm -2 compared to Pt/C + RuO 2 catalyst). Here, the design strategy can expand the utilization of earth-abundant biomaterial-derived catalysts, and the mechanism investigations of S doping on the structure-activity relationship can inspire the progress of other functional electrocatalysts.

25 ENERGY STORAGE↗
DOE OSTI2022

The Importance of Nonequilibrium to Equilibrium Transition Pathways for the Efficiency and Stability of Organic Solar Cells

Controlled morphology of solution-processed thin films have realized impressive achievements for non-fullerene acceptor (NFA)–based organic solar cells (OSCs). Given the large set of donor–acceptor pairs, employing various processing conditions to realize optimal morphology for high efficiency and stable OSCs is a strenuous task. Therefore, comprehensive correlations between processing conditions and morphology evolution pathways have to be developed for efficient performance and stability of devices. Within the framework of the blend system, crystallization transitions of NFA molecules are tracked utilizing the first heating scan of differential scanning calorimeter (DSC) measurement correlating with respective morphology evolution of blend films. Real-time dynamics measurements and morphology characterizations are combined to provide optimal morphology transition pathways as NFA molecules are shown to be released from the mixed phase to form balanced ordered packing with variant processing conditions. Polymer:NFA films are fabricated using blade coating incorporating solvent additive or thermal annealing as processing conditions as a correlation is formulated between performance and stability of solar cells with morphology transition pathways. In conclusion, this work demonstrates the significance of processing condition-controlled transition pathways for the realization of optimal morphology leading to superior OSC devices.

14 SOLAR ENERGY↗
DOE OSTI2022

Toward Superb Perovskite Oxide Electrocatalysts: Engineering of Coupled Nanocomposites

Abstract A significant issue that bedeviled the commercialization of renewable energy technologies, ranging from low‐temperature water electrolyzers to high‐temperature solid oxide cells, is the lack of high‐performance catalysts. Among various candidates that could tackle such a challenge, perovskite oxides are rising‐star materials because of their unique structural and compositional flexibility. However, single‐phase perovskite oxides are challenging to satisfy all the requirements of electrocatalysts concurrently for practical applications, such as high catalytic activity, excellent stability, good ionic and electronic conductivities, and superior chemical/thermo‐mechanical robustness. Impressively, perovskite oxides with coupled nanocomposites are emerging as a novel form offering multifunctionality due to their intrinsic features, including infinite interfaces with solid interaction, tunable compositions, flexible configurations, and maximum synergistic effects between assorted components. Considering this new configuration has attracted great attention owing to its promising performances in various energy‐related applications, this review timely summarizes the leading‐edge development of perovskite oxide‐based coupled nanocomposites. Their state‐of‐art synthetic strategies are surveyed and highlighted, their unique structural advantages are highlighted and illustrated through the typical oxygen reduction reaction and oxygen evolution reactions in both high and low‐temperature applications. Opinions on the current critical scientific issues and opportunities in this burgeoning research field are all provided.

08 HYDROGEN↗
DOE OSTI2025

Anionic‐Based Layered Oxide Cathodes with High Electrochemical Performance through Dual‐Site Substitutions for Sodium‐Ion Batteries

Mn-rich layered oxide cathodes with anionic redox promise high energy density for sodium-ion batteries (SIBs) due to ultra-high capacity derived from both Mn and O redox couples. Nevertheless, instability of the reactions that lead to poor electrochemical stability hinders the cathodes from practical applications. Here, the Al and Zn dual-site substitution strategy is proposed to enhance electrochemical performance. Here, the designed cathode, Na 0.73 Zn 0.03 Li 0.25 Mn 0.76 Al 0.01 O 2 (AlZn), delivers a high discharge capacity of 242 mAh g −1 with an impressive rate capability (162 mAh g −1 at 1000 mA g −1 ) and excellent capacity retention (89.69% over 150 cycles). In addition, full-cell SIB based on AlZn coupled with hard carbon exhibits a high energy density of 317 Wh kg −1 (based on both cathode and anode mass) and a reasonable capacity retention of 80.8% after 250 cycles. Revealed by advanced investigations, the synergy of robust Al–O in TM layers and O–Zn–O pillars in Na layers helps alleviate severe inactive spinel/rock-salt phase transformation and intragranular cracks in the AlZn cathode. This consequently leads to greatly enhanced electrochemical performance over the pristine cathode. This work provides insight into improving electrochemical properties of anionic-redox-based layered oxides by Al/Zn co-substitutions toward high-energy SIBs.

25 ENERGY STORAGE↗
DOE OSTI2018

Graphitic Carbon Materials for Advanced Sodium-Ion Batteries

Lithium-ion batteries (LIBs) have dominated the energy storage market for more than two decades; however, the quest for lower-cost battery alternatives is rapidly expanding, especially for large-scale applications. Sodium-ion batteries (SIBs) have recently experienced an impressive resurgence owing to the earth's abundance of sodium resources and the similar electrochemistry of SIBs and the well-established LIBs. Nonetheless, whereas cost-effective and reliable graphite anodes have served as a cornerstone in current LIB technology, one of the major limitations of SIBs has been the inability to exploit graphite as an electrode because of its negligible sodium storage capability. Recently, however, clear progress has been made in preparing high-performance graphitic carbon anodes for SIBs with new findings on the mechanisms of sodium storage. This paper aims to review the progress made in understanding the sodium storage mechanisms in graphitic carbon materials and comprehensively summarize the start-of-the-art achievements by surveying the correlations among the type of graphitic material, microstructure, sodium storage mechanisms, and electrochemical performance in SIBs. In addition, perspectives related to practical applications, including the electrolyte, coulombic efficiency, and applicability in sodium-ion full cells, are also presented.

25 ENERGY STORAGE↗
DOE OSTI2024

A Novel Non‐Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Abstract Amino‐acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

Chemistry↗
DOE OSTI2019

In Situ Structure Characterization in Slot-Die-Printed All-Polymer Solar Cells with Efficiency Over 9%

Herein, high-performance printed all-polymer solar cells (all-PSCs) based on a bulk-heterojunction (BHJ) blend film are demonstrated using PTzBI as the donor and N2200 as the acceptor. A slot-die process is used to prepare the BHJ blend, which is a cost-effective, high-throughput approach to achieve large-area photovoltaic devices. The real-time crystallization of polymers in the film drying process is investigated by in situ grazing incidence wide-angle X-ray scattering characterization. Printing is found to significantly improve the crystallinity of the polymer blend in comparison with spin coating. Moreover, printing with 1,8-diiodooctane as the solvent additive enhances the polymer aggregation and crystallization during solvent evaporation, eventually leading to multi-length-scale phase separation, with PTzBI-rich domains in-between the N2200 crystalline fibers. This unique morphology achieved by printing fabrication results in an impressively high power conversion efficiency of 9.10%, which is the highest efficiency reported for printed all-PSCs. Finally, these findings provide important guidelines for controlling film drying dynamics for processing all-PSCs.

14 SOLAR ENERGY↗
DOE OSTI2024

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Study on Efficient Reinforcement Learning Through Knowledge Transfer

Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. Here this survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Exploring Gradient-Based Multi-directional Controls in GANs

Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions. However, despite its impressive applications, the structure of the latent space in GANs largely remains as a black-box, leaving its controllable generation an open problem, especially when spurious correlations between different semantic attributes exist in the image distributions. To address this problem, previous methods typically learn linear directions or individual channels that control semantic attributes in the image space. However, they often suffer from imperfect disentanglement, or are unable to obtain multi-directional controls. Furthermore, in this work, in light of the above challenges, we propose a novel approach that discovers nonlinear controls, which enables multi-directional manipulation as well as effective disentanglement, based on gradient information in the learned GAN latent space. More specifically, we first learn interpolation directions by following the gradients from classification networks trained separately on the attributes, and then navigate the latent space by exclusively controlling channels activated for the target attribute in the learned directions. Empirically, with small training data, our approach is able to gain fine-grained controls over a diverse set of bi-directional and multi-directional attributes, and we showcase its ability to achieve disentanglement significantly better than state-of-the-art methods both qualitatively and quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantifying limits on CP violating phases from EDMs in supersymmetry

We revisit the calculation of the electron, neutron, and proton electric dipole moments (EDMs) in the constrained minimal supersymmetric standard model (CMSSM). The relatively large mass of the Higgs boson, m H ≃ 125 GeV coupled with the (as yet) lack of discovery of any supersymmetric particle at the LHC, has pushed the supersymmetry breaking scale to several TeV or higher. Though one might expect this decoupling to have relaxed completely any bounds on the two CP violating phases in the CMSSM (θ μ and θ A ), the impressive experimental improvements in the limits on the EDMs (particularly the electron EDM) still allow us to set constraints of order (0.01–0.1)π on θ A and (0.001–0.1)π on θ μ . We also discuss the impact of future improvements in the experimental limits on supersymmetric models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Lepton flavorful fifth force and depth-dependent neutrino matter interactions

Here, we consider a fifth force to be an interaction that couples to matter with a strength that grows with the number of atoms. In addition to competing with the strength of gravity a fifth force can give rise to violations of the equivalence principle. Current long range constraints on the strength and range of fifth forces are very impressive. Amongst possible fifth forces are those that couple to lepton flavorful charges $L_e-L_{\mu}$ or $L_e-L_{\tau}$. They have the property that their range and strength are also constrained by neutrino interactions with matter. In this brief note we review the existing constraints on the allowed parameter space in gauged $U(1)_{L_e-L_{\mu}, L_{\tau}}$. We find two regions where neutrino oscillation experiments are at the frontier of probing such a new force. In particular, there is an allowed range of parameter space where neutrino matter interactions relevant for long baseline oscillation experiments depend on the depth of the neutrino beam below the surface of the earth.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Is infrared-collinear safe information all you need for jet classification?

Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high-energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this discriminating power, and whether jet observables that are tractable in perturbative QCD such as those obeying infrared-collinear (IRC) safety serve as sufficient inputs. In this article, we introduce a new classifier, Jet Flow Networks (JFNs), in an effort to address the question of whether IRC unsafe information provides additional discriminating power in jet classification. JFNs are permutation-invariant neural networks (deep sets) that take as input the kinematic information of reconstructed subjets. The subjet radius and a cut on the subjet’s transverse momenta serve as tunable hyperparameters enabling a controllable sensitivity to soft emissions and nonperturbative effects. We demonstrate the performance of JFNs for quark vs. gluon and Z vs. QCD jet tagging. For small subjet radii and transverse momentum cuts, the performance of JFNs is equivalent to the IRC-unsafe Particle Flow Networks (PFNs), demonstrating that infrared-collinear unsafe information is not necessary to achieve strong discrimination for both cases. As the subjet radius is increased, the performance of the JFNs remains essentially unchanged until physical thresholds that we identify are crossed. For relatively large subjet radii, we show that the JFNs may offer an increased model independence with a modest tradeoff in performance compared to classifiers that use the full particle information of the jet. These results shed new light on how machines learn patterns in high-energy physics data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Learning from Higgs physics at future Higgs factories

Future Higgs factories can reach impressive precision on Higgs property measurements. In this paper, instead of conventional focus of Higgs precision in certain interaction bases, we explored its sensitivity to new physics models at the electron-positron colliders. In particular, we studied two categories of new physics models, Standard Model (SM) with a real scalar singlet extension, and Two Higgs Double Model (2HDM) as examples of weakly-interacting models, Minimal Composite Higgs Model (MCHM) and three typical patterns of the more general operator counting for strong interacting models as examples of strong dynamics. We performed a global fit to various Higgs search channels to obtain the 95% C.L. constraints on the model parameter space. In the SM with a singlet extension, we obtained the limits on the singlet-doublet mixing angle sin(theta), as well as the more general Wilson coefficients of the induced higher dimensional operators. In the 2HDM, we analyzed tree level effects in tan(beta) vs. cos(beta-alpha) plane, as well as the one-loop contributions from the heavy Higgs bosons in the alignment limit to obtain the constraints on heavy Higgs masses for different types of 2HDM. In strong dynamics models, we obtained lower limits on the strong dynamics scale. In addition, once deviations of Higgs couplings are observed, they can be used to distinguish different models. Here, we also compared the sensitivity of various future Higgs factories, namely Circular Electron Positron Collider (CEPC), Future Circular Collider (FCC)-ee and International Linear Collider (ILC).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Speculation on measurements of the viscosity of shocked fluid water

Different reports of the viscosities of shocked water, relying on measurements of ionic drift speeds in dilute aqueous solution, of propagation of corrugations impressed on the shock front, and of the acceleration of entrained cylinders, disagree by six orders of magnitude across overlapping ranges of shock pressure. Measurements made at static pressures have proven that viscosities vary little along the Hugoniot, verifying the results from ionic drift speed. Why the other two proceedures yield such different results has been a matter of speculation. Here, I show reason to believe that the trajectories of the entrained cylinders were in a region of drag in which viscosities of 0.001 Pa-s and 1 Pa-s were experimentally indistinguishable, resulting in much larger supposed viscosities than warranted by the data. It follows that damping of corrugations are best explained as due to dissipative processes distinct from the shear viscosity of the equilibrated fluid.

36 MATERIALS SCIENCE↗