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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

New Thermonuclear Rate of 7 Li(d,n)2 4 He Relevant to the Cosmological Lithium Problem

Accurate 7 Li(d,n)2 4 He thermonuclear reaction rates are crucial for precise prediction of the primordial abundances of lithium and beryllium and to probe the mysteries beyond fundamental physics and the standard cosmological model. However, uncertainties still exist in current reaction rates of 7 Li(d,n)2 4 He widely used in big bang nucleosynthesis (BBN) simulations. In this work, we reevaluate the 7 Li(d,n)2 4 He reaction rate using the latest data on the three near-threshold 9 Be excited states from experimental measurements. We present for the first time uncertainties that are directly constrained by experiments. Additionally, we take into account for the first time the contribution from the subthreshold resonance at 16.671 MeV of 9 Be. We obtain a 7 Li(d,n)2 4 He rate that is overall smaller than the previous estimation by about a factor of 60 at the typical temperature of the onset of primordial nucleosynthesis. We implemented our new rate in BBN calculations, and we show that the new rates have a very limited impact on the final light element abundances in uniform density models. Typical abundance variations are in the order of 0.002%. For nonuniform density BBN models, the predicted 7 Li production can be increased by 10% and the primordial production of light nuclides with mass number A > 7 can be increased by about 40%. Our results confirm that the cosmological lithium problem remains a long-standing unresolved puzzle from the standpoint of nuclear physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Flexing the Limits of Land-Based Wind Turbine Rotor Growth

The Big Adaptive Rotor (BAR) project works to design and enable the next generation of land-based wind turbines with rotors that stretch 206 meters (m) in diameter, which will increase capacity factors by 10% or more over a typical land-based turbine. These high-energy-capture turbines have lower specific-power ratings, meaning they have larger rotors relative to the size of their generator, which helps them capture more wind and perform better at lower wind speeds, thereby increasing the turbine capacity factor. High-energy-capture turbines with larger rotors have many advantages, but the length and width of their blades creates transportation challenges that must be overcome to make true on the adage, bigger is better.

wind, big, adaptive, rotor↗
DOE OSTI2024

The Future of Land-Based Wind Turbine Rotor Technology: The Perspective from NREL

The Big Adaptive Rotor (BAR) Project looks at potential innovative pathways for future land-based wind turbine technology. BAR is led by the National Renewable Energy Laboratory and is sponsored by the Wind Energy Technology Office of the US Department of Energy. This talk will describe innovations and challenges faced by the teams working on BAR. The innovations include numerical models predicting the stability and performance of highly flexible blades, downwind rotors to maximize wind farm power, and controlled bending of 100-meter-long blades during rail transport. The challenges of developing predictive numerical models and formulating successful value propositions supporting new technologies such as distributed aero control devices will also be discussed.

BAR↗
DOE OSTI2020

Establishing a Theoretical Landscape for Identifying Basal Plane Active 2D Metal Borides (MBenes) toward Nitrogen Electroreduction

Abstract To achieve efficient ammonia synthesis via electrochemical nitrogen reduction reaction (NRR), a qualified catalyst should have both high specific activity and large active surface area. However, integrating these two merits into one single material remains a big challenge due to the difficulty in balancing multiple reaction intermediates. Here, it is demonstrated that the boron‐analogues of MXenes, namely “MBenes”, could cope with the challenge and achieve the high activity and large reaction region simultaneously toward NRR. Using extensive density functional theory computations and taking 16 MBenes as representatives, it is identified that seven MBenes (CrB, MoB, WB, Mo 2 B, V 3 B 4 , CrMnB 2 , and CrFeB 2 ) not only have intrinsic basal plane activity for NRR with limiting potentials ranging from −0.22 to −0.82 V, but also possess superior capability of suppressing the competitive hydrogen evolution reaction. Particularly, different from the MXenes whose surface oxidation may block the active sites, once oxidized, these MBenes can catalyze NRR via the self‐activating process, reducing O*/OH* into H 2 O* under reaction conditions, and favoring the N 2 electroreduction. As a result, the exceptional activity and selectivity, high active area (≈10 19 m −2 ), and antioxidation nature render these MBenes as pH‐universal catalysts for NH 3 production without introducing any dopants and defects.

Guo, Xiangyu↗
DOE OSTI2023

Fine‐Tuning Alkyl Chains on Quinoxaline Nonfullerene Acceptors Enables High‐Efficiency Ternary Organic Solar Cells with Optimizing Molecular Stacking and Reducing Energy Loss

Abstract Material design of guest acceptor is always a big challenge for improving the efficiency of ternary organic solar cells (OSCs). Here, a pair of isomeric nonfullerene acceptors based on quinoxaline core, Qx–p‐C 7 H 8 O and Qx–m‐C 7 H 8 O, is designed and synthesized. By moving the alkoxy chain attached on side phenyl frommeta‐position topara‐position, both π–π stacking distance and crystallinity are enhanced simultaneously. They obtain the uplifted lowest unoccupied molecular orbital level. Compared to Qx–m‐C 7 H 8 O, Qx–p‐C 7 H 8 O exhibits wider absorption spectrum and higher extinction coefficient. Using D18‐Cl:N3 as host materials, the addition of guest acceptor Qx–p‐C 7 H 8 O significantly improves the power conversion efficiency (PCE) from 17.61% to 18.49% because of higher open‐circuit voltage (0.875 V) and short‐circuit current density (27.85 mA cm −2 ). This can be attributed to the faster exciton dissociation, more balanced carrier mobility, fine fiber morphology, and lower energy loss in the ternary devices. However, Qx–m‐C 7 H 8 O‐based ternary device achieves relatively low PCE of 17.17% because this device shows extremely low electron mobility. The results indicate that molecular stacking, film morphology, etc., can be effectively modulated by fine‐tuning the side chains of guest materials, which may be an effective design rule for further improving the PCE of OSCs.

Chemistry↗
DOE OSTI2023

Preparation of 2D Polyaniline/MoO 3– x Superlattice Nanosheets via Intercalation-Induced Morphological Transformation for Efficient Chemodynamic Therapy

Organic intercalation of layered nanomaterials is an attractive strategy to fabricate organic/inorganic superlattices for a wide range of promising applications. However, the synthesis of 2D organic/inorganic superlattice nanosheets remains a big challenge. In this work, the preparation of 2D polyaniline/MoO 3–x (PANI/MoO 3–x ) superlattice nanosheets via intercalation-induced morphological transformation from MoO 3 nanobelts, as efficient Fenton-like reagents for chemodynamic therapy (CDT), is reported. Micrometer-long MoO 3 nanobelts are co-intercalated with Na + /H 2 O followed by the guest exchange with aniline monomer for in situ polymerization to obtain PANI/MoO 3–x nanosheets. Intriguingly, the PANI intercalation can induce the morphological transformation from long MoO 3 nanobelts to 2D PANI/MoO 3–x nanosheets along with the partial reduction of Mo 6+ to Mo 5+ , and generation of rich oxygen vacancies. More importantly, thanks to the PANI intercalation-induced activation, the PANI/MoO 3–x nanosheets exhibit excellent Fenton-like catalytic activity for generation of hydroxyl radical (·OH) by decomposing H 2 O 2 compared with the MoO 3 nanobelts. It is speculated that the good conductivity of PANI can facilitate electron transport during the Fenton-like reaction, thereby enhancing the efficiency of CDT. Thus, the polyvinylpyrrolidone-modified PANI/MoO 3–x nanosheets can function as Fenton-like reagents for highly efficient CDT to kill cancer cells and eradicate tumors.

2D materials↗
DOE OSTI2022

Charge Transport in Twisted Organic Semiconductor Crystals of Modulated Pitch

Many molecular crystals (approximately one third) grow as twisted, helicoidal ribbons from the melt, and this preponderance is even higher in restricted classes of materials, for instance, charge-transfer complexes. Previously, twisted crystallites of such complexes present an increase in carrier mobilities. In this paper, the effect of twisting on charge mobility is better analyzed for a monocomponent organic semiconductor, 2,5-bis(3-dodecyl-2-thienyl)-thiazolo[5,4-d]thiazole (BDT), that forms twisted crystals with varied helicoidal pitches and makes possible a correlation of twist strength with carrier mobility. Films are analyzed by X-ray scattering and Mueller matrix polarimetry to characterize the microscale organization of the polycrystalline ensembles. Carrier mobilities of organic field-effect transistors are five times higher when the crystals are grown with the smallest pitches (most twisted), compared to those with the largest pitches, along the fiber elongation direction. A tenfold increase is observed along the perpendicular direction. Simulation of electrical potential based on scanning electron microscopy images and density functional theory suggests that the twisting-enhanced mobility is mainly controlled by the fiber organization in the film. A greater number of tightly packed twisted fibers separated by numerous smaller gaps permit better charge transport over the film surface compared to fewer big crystallites separated by larger gaps.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Membrane-Free Zn/MnO 2 Flow Battery for Large-Scale Energy Storage

The traditional Zn/MnO 2 battery has attracted great interest due to its low cost, high safety, high output voltage, and environmental friendliness. However, it remains a big challenge to achieve long-term stability, mainly owing to the poor reversibility of the cathode reaction. Different from previous studies where the cathode redox reaction of MnO 2 /MnOOH is in solid state with limited reversibility, in this work a new aqueous rechargeable Zn/MnO 2 flow battery is constructed with dissolution–precipitation reactions in both cathodes (Mn 2+ /MnO 2 ) and anodes (Zn 2+ /Zn), which allow mixing of anolyte and catholyte into only one electrolyte and remove the requirement for an ion selective membrane for cost reduction. Impressively, this new battery exhibits a high discharge voltage of ≈1.78 V, good rate capability (10C discharge), and excellent cycling stability (1000 cycles without decay) at the areal capacity ranging from 0.5 to 2 mAh cm -2 . More importantly, this battery can be readily enlarged to a bench scale flow cell of 1.2 Ah with good capacity retention of 89.7% at the 500th cycle, displaying great potential for large-scale energy storage.

25 ENERGY STORAGE↗
DOE OSTI2020

Advanced electrolytes for fast-charging high-voltage lithium-ion batteries in wide-temperature range

LiNixMnyCo1-x-yO2 (NMC) cathode materials with Ni=0.8 have attracted great interest for application in high energy-density lithium (Li)-ion batteries (LIBs) because of their high specific capacities at high voltages. However, the practical application of Ni-rich NMC in LIBs under high charge voltages (e.g. 4.4 V and above) still faces big challenges due to the severe capacity fading, which is directly related to the instability of the cathode/electrolyte interface. In this work, we develop new localized high-concentration electrolytes (LHCEs) based on carbonate solvents, which are compatible with graphite (Gr) anode and have a high oxidation potential over 4.9 V vs. Li/Li+. The optimal LHCE enables the Gr||NMC811 cells with 2.8 mAh cm-2 cathode areal capacity loading to achieve an excellent cycling stability over 600 cycles with a capacity retention of 94.2 % under a charge cut-off voltage of 4.4 V at 25 ?C, good rate capabilities under fast charging or discharging up to 3C rate, and superior low-temperature discharge performance down to -30 ?C with a capacity retention of 85.6% at C/5 rate. The findings in this work shed light a very promising strategy to develop new electrolytes for practical high-energy LIBs with Ni-rich NMC cathodes.

Zhang, Xianhui↗
DOE OSTI2022

Combining Experimental and Theoretical Techniques to Gain an Atomic Level Understanding of the Defect Binding Mechanism in Hard Carbon Anodes for Sodium Ion Batteries

Sodium ion batteries (NIBs) are an attractive alternative to lithium–ion batteries in applications that require large–scale energy storage due to sodium's high natural abundance and low cost. Hard carbon (HC) is the most promising anode material for NIBs; however, there is a knowledge gap in the understanding of the sodium binding mechanism that prevents a rational design of HC. This study tunes sucrose–derived HC via synthesis temperature then evaluates the structural, physical, and electrochemical properties. Neutron total scattering is used to generate structural models by fitting pair distribution functions (PDF) with a combination of molecular dynamics and reverse Monte Carlo methods. From this model, the number and type of structural features are identified, quantified, and correlated to the galvanostatic charge/discharge. A method of PDF “fingerprinting” binding sites using Na probe atoms is developed and analyzing these PDFs reveals an atomistic view of ion binding sites responsible for “defect” storage mechanisms. Combining these techniques results in an atomic–level study that provides a big picture of the Na–binding mechanism in NIBs, which allows for more precise tuning of the structure–property relationships in the future. Finally, the methodologies developed will also enable new strategies for the analysis of amorphous functional materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Emerging Strategies for Modifying Lignin Chemistry to Enhance Biological Lignin Valorization

Biological lignin valorization represents a promising approach contributing to sustainable and economic biorefineries. Here, the low level of valuable lignin–derived products remains a major challenge hindering the implementation of microbial lignin conversion. Lignin's properties play a significant role in determining the efficiency of lignin bioconversion. To date, despite significant progress in the development of biomass pretreatment, lignin fractionation, and fermentation over the last few decades, little efforts have gone into identifying the ideal lignin substrates for an efficient microbial metabolism. In this Minireview, emerging and state–of–the–art strategies for biomass pretreatment and lignin fractionation are summarized to elaborate their roles in modifying lignin structure for bioconversion. Fermentation strategies aimed at enhancing lignin depolymerization for microbial utilization are systematically reviewed as well. With an improved understanding of the ideal lignin structure elucidated by comprehensive metabolic pathways and/or big data analysis, modifying lignin chemistry could be more directional and effective. Ultimately, together with the progress of fermentation process optimization, biological lignin valorization will become more competitive in biorefineries.

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

3D printing of anisotropic Sm–Fe–N nylon bonded permanent magnets

Abstract Fabricating a bonded magnet with a near‐net shape in suitable thermoplastic polymer binders is of paramount importance in the development of cost‐effective energy technologies. In this work, anisotropic Sm 2 Fe 17 N 3 (Sm–Fe–N) bonded magnets are additively printed using SmFeN anisotropic magnetic particles in a polymeric binder polyamide‐12 (PA12). The anisotropic SmFeN bonded magnets are fabricated by Big Area Additive Manufacturing followed by post‐printing alignment in a magnetic field. Optimal post‐alignment results in an enhanced remanence of ∼0.68 T in PA12 reflected in a parallel‐oriented (aligned) measured direction. The maximum energy product achieved for the additively printed anisotropic bonded magnet of Sm–Fe–N in PA12 polymer is 78.8 KJ m −3 . Our results show advanced processing flexibility with 3D printing of the development of SmFeN nylon bonded magnets designed for applications with no critical rare earth magnets.

42 ENGINEERING↗
DOE OSTI2024

The 2024 “Hacking Limnology” Workshop Series and Virtual Summit: Increasing Inclusion, Participation, and Representation in the Aquatic Sciences

The 4th Aquatic Ecosystem MOdeling Network—Junior (AEMON-J) Hacking Limnology Workshop and 5th Virtual Summit: Incorporating Data Science and Open Science in the Aquatic Sciences (DSOS) convened 15–19 July 2024. During the week, these joint communities engaged in activities at the intersection of big data, open science, modeling, remote sensing, and the aquatic sciences. The weeklong event, with over 100 aquatic science practitioners and enthusiasts, followed a similar structure to previous years, comprising three days of workshops followed by two days of the virtual summit.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances via Discovery in Physical Sciences

Over several months, representatives from the U.S. Department of Energy (DOE) Office of Science and National Institutes of Health (NIH) had a number of meetings that lead to the conclusion that innovations in the Nation's health care could be realized by more directed interactions between NIH and DOE. It became clear that the expertise amassed and instrumentation advances developed at the DOE physical science laboratories to enable cutting-edge research in particle physics could also feed innovation in medical healthcare. To meet their scientific mission, the DOE laboratories created advances in such technologies as particle beam generation, radioisotope production, high-energy particle detection and imaging, superconducting particle accelerators, superconducting magnets, cryogenics, high-speed electronics, artificial intelligence, and big data. To move forward, NIH and DOE initiated the process of convening a joint work- shop which occurred on July 12th and 13th, 2021. Here, this Special Report presents a summary of the findings of the collaborative workshop and introduces the goals of the next one.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

Abstract There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tucker and tensor train formats, are used to approximate the initial large‐tensor. Further, tensor factorization is used to extract latent features that can facilitate discoveries of new mechanisms and signatures hidden in the data, where the explainability of the latent features is of principal importance. Nonnegative tensor factorization extracts latent features that are naturally sparse and parts of the data, which makes them easily interpretable. However, to take into account available domain knowledge and subject matter expertise, often additional constraints need to be imposed, which lead us to canonical decomposition with linear constraints (CANDELINC), a canonical polyadic decomposition with rank deficient factors. In CANDELINC, Tucker compression is used as a preprocessing step, which lead to a larger residual error but to more explainable latent features. Here, we propose a nonnegative CANDELINC (nnCANDELINC) accomplished via a specific nonnegative Tucker decomposition; we refer to as minimal or canonical nonnegative Tucker. We derive several results required to understand the specificity of nnCANDELINC, focusing on the difficulties of preserving the nonnegative rank of a tensor to its Tucker core and comparing the real valued to nonnegative case. Finally, we demonstrate nnCANDELINC performance on synthetic and real‐world examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A causal data fusion method for the general exposure and outcome

Abstract With the advent of the big data era, the need to combine multiple individual data sets to draw causal effects arises naturally in many medical and biological applications. Especially each data set cannot measure enough confounders to infer the causal effect of an exposure on an outcome. In this article, we extend the method proposed by a previous study to causal data fusion of more than two data sets without external validation and to a more general (continuous or discrete) exposure and outcome. Theoretically, we obtain the condition for identifiability of exposure effects using multiple individual data sources for the continuous or discrete exposure and outcome. The simulation results show that our proposed causal data fusion method has unbiased causal effect estimate and higher precision than traditional regression, meta‐analysis and statistical matching methods. We further apply our method to study the causal effect of BMI on glucose level in individuals with diabetes by combining two data sets. Our method is essential for causal data fusion and provides important insights into the ongoing discourse on the empirical analysis of merging multiple individual data sources.

Li, Hongkai↗
DOE OSTI2023

Dimensionality reduction using elastic measures

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. Here, in this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

97 MATHEMATICS AND COMPUTING↗