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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Imaging Reconfigurable Molecular Concentration on a Graphene Field-Effect Transistor

The spatial arrangement of adsorbates deposited onto a clean surface under vacuum typically cannot be reversibly tuned. Here we use scanning tunneling microscopy to demonstrate that molecules deposited onto graphene field-effect transistors (FETs) exhibit reversible, electrically tunable surface concentration. Continuous gate-tunable control over the surface concentration of charged F 4 TCNQ molecules was achieved on a graphene FET at T = 4.5K. This capability enables the precisely controlled impurity doping of graphene devices and also provides a new method for determining molecular energy level alignment based on the gate-dependence of molecular concentration. Gate-tunable molecular concentration is explained by a dynamical molecular rearrangement process that reduces total electronic energy by maintaining Fermi level pinning in the device substrate. Finally, the molecular surface concentration is fully determined by the device back-gate voltage, its geometric capacitance, and the energy difference between the graphene Dirac point and the molecular LUMO level.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Photovoltaic (PV) System Levelized Cost of Energy (LCOE) Evaluation with Grid Support Function Valuation and Service Lifetime Estimation

Photovoltaic (PV) systems play a critical role in renewable energy resource grid integration, and levelized cost of energy (LCOE) is commonly used to evaluate PV system feasibility in modern power grids. In this work, a revised PV system LCOE calculation model is derived to quantify the potential of LCOE reduction. Particularly, the grid support functions are valuated to offset the investment and operation costs of PV systems, which thereby reduces the LCOE. Meanwhile, PV system service lifetime is also estimated with the derived PV inverter reliability model, considering the critical components (i.e., semiconductor devices and capacitors). The case studies with field datasets are conducted to validate the effectiveness of the developed LCOE calculation model.

Zhao, Shijia↗
DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗
DOE OSTI2020

Exploring Renewable Energy Opportunities in Select Southeast Asian Countries: A Geospatial Analysis of the Levelized Cost of Energy of Utility-Scale Wind and Solar Photovoltaics

The costs of renewable energy-based electricity generation have fallen precipitously in recent years to levels that are increasingly competitive with traditional generation such as fossil fuel-based generation. As these costs become increasingly competitive, private developers, policymakers, and energy system planners are searching for opportunities to harness high-quality renewable energy resources. Developing economies are setting ambitious targets and exploring how cost-effective, grid-connected renewable energy options can help power economic growth and meet growing electricity demands. This includes the member states of the Association of Southeast Asian Nations (ASEAN) that are determined to reach a target of 23% of renewable energy in the region's total primary energy supply by 2025. A critical gap to identifying opportunities and scaling up renewable energy is the lack of quality data and analyses to support decisions on the investment and deployment of renewables - including wind and solar photovoltaics (PV). This work supports decision making by providing high-quality data and spatial analysis of the cost of utility-scale wind and solar PV generation in select countries of Southeast Asia - specifically, the ASEAN member states. Generation costs are expressed as the levelized cost of energy (LCOE) - a commonly used metric that represents the net present value of the unit cost of electricity during the lifetime of a particular electricity generation technology. This is the first spatial estimate of LCOE for these technologies within the ASEAN member states - providing insights into the roles that renewable energy resource quality and other factors may play in generation costs.

14 SOLAR ENERGY↗
DOE OSTI2021

Balancing Interfacial Reactions to Achieve Long Cycle Life in High Energy Lithium Metal Batteries

Rechargeable lithium (Li) metal batteries have attracted wide attentions as the next generation energy storage technologies. However, simultaneously achieving high cell-level energy density and long cycle life in realistic batteries is still a great challenge. Here we investigate the cell degradation mechanisms of Li||LiNi0.6Mn0.2Co0.2O2 pouch cells using different, but representative cell configurations to understand the fundamental linkage among Li thickness, electrolyte depletion and the structure evolution of solid electrolyte interphase (SEI) layers. Different cell failure modes were discovered when tuning the anode to cathode capacity (N/P) ratio in compatible electrolyte. With a thick-Li anode (N/P ratio = 2.5), initial stable cycling is obtained because of the abundant Li supply from the anode together with an artificially inflated high Coulombic Efficiency, followed by a premature sudden cell death appears due to the enrichment of “ineffective SEI” which does not participate in the electrochemical reactions but keep increasing cell impedance. The anode-free cell (N/P 0:1) displays a steady capacity decay because cathode Li loss dominates from the beginning to the end of cell cycle life. An optimized thin-Li (N/P 1:1) well balances the Li consumption rate with the impedance buildup by minimizing the growth of ineffective SEI layer, thus decelerates cell polarization increasing and extends cycling. Contrary to conventional wisdoms, long cycle life is observed by using ultra thin-Li (20 µm) in balanced cells. A prototype 350 Wh kg-1 pouch cell (2.0 Ah) achieves over 600 long stable cycles with 76% capacity retention without sudden cell death.

Niu, Chaojiang↗
DOE OSTI2021

Structural insights into Lewis acid- and F4TCNQ-doped conjugated polymers by solid-state magnetic resonance spectroscopy

Molecular doping strategies facilitate orders of magnitude enhancement in the charge carrier mobility of organic semiconductors (OSCs). Understanding the different doping mechanisms and molecular-level constraints on doping efficiency related to the material energy levels is crucial to develop versatile dopants for OSCs. Given the compositional and structural heterogeneities associated with OSC thin films, insight into dopant–polymer interactions by long-range techniques such as X-ray scattering and electron microscopy is exceedingly challenging to obtain. This study employs short-range probes, solid-state (ss)NMR and EPR spectroscopy, to resolve local structures and intermolecular interactions between dopants such as F4TCNQ (2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane), Lewis acid BCF (tris[pentafluorophenyl] borane) and Lewis base conjugated polymer, PCPDTBT (P4) (poly[2,6-(4,4-bis(2-hexadecyl)-4H-cyclopenta[2,1-b;3,4-b']dithiophene)-alt-4,7(2,1,3-benzothiadiazole)]). Analysis of 1 H and 13 C ssNMR spectra of P4, P4 : F4TCNQ and P4 : BCF blends indicates that the addition of dopants induces local structural changes in the P4 polymer, and causes paramagnetism-induced signal broadening and intensity losses. The hyperfine interactions in P4 : BCF and P4 : F4TCNQ are characterized by two-dimensional pulsed EPR spectroscopy. For P4 : F4TCNQ, 19 F ssNMR analysis indicates that the F4TCNQ molecules are distributed and aggregated into different local chemical environments. By comparison, BCF molecules are intermixed with the P4 polymer and interact with traces of water molecules to form BCF–water complexes that serve as Brønsted acid sites, as revealed by 11 B ssNMR spectroscopy. These results indicate that the P4–dopant blends exhibit complex morphology with different distributions of dopants, whereby the combined use of ssNMR and EPR provides essential insights into how higher doping efficiency is observed with BCF and a mediocre efficiency is associated with F4TCNQ molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mapping the Electronic Structure of the Uranium(VI) Dinitride Molecule, UN 2

A combined anion photoelectron spectroscopic and relativistic electronic-structure computational study of the electronic structure of the UN 2 molecule is reported. Since the photoelectron spectrum of uranium dinitride’s negative ion, UN 2 - , (UN 2 plus an additional electron) directly reflects the electronic structure of neutral UN 2 , we have measured and relied upon the photoelectron spectrum of the UN 2 – anion as a means of mapping the electronic structure of neutral UN 2 . In addition to the electron affinity of the UN 2 ground state (the energy released when attaching an electron to UN 2 molecule), energy levels of the UN 2 excited states (electronic states with higher energies than the ground state) were well characterized by the close interplay between experiment and high-level theory. We found that both electron attachment and electronic excitation significantly bend the UN 2 molecule and elongate its U≡N bond. Implications for the activation of UN 2 are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Carbon Intensities of Refining Products in Petroleum Refineries with Co-Processed Biofeedstocks

Petroleum refineries increasingly seek to generate fuels with lower carbon intensities (CIs; a measure of life cycle greenhouse gas [GHG] emissions per unit of energy of fuel; well-to-wheel [WTW]) to meet growing demand. Co-processing refers to a process that adds biomass-derived feedstocks to the fossil-based feedstocks of existing petroleum refinery process units. With the use of biofeedstocks, it is expected that co-processed fuels would have lower CIs than their petroleum counterparts without requiring changes in the existing infrastructure for producing, transporting, and using fuels. To quantify the GHG emissions reduction benefits of co-processing, this study uses a linear programming model to simulate petroleum refinery conditions with and without co-processing. The co-processing cases include three biofeedstocks (soy oil, tallow, and used cooking oil or UCO) used as 10 vol.% of the feedstock to a hydrotreater or hydrocracker since these lipid-based feedstocks have favorable properties to be treated in a hydrotreater or hydrocracker. In addition, we considered pyrolysis oil used as 10 vol.% of the feedstock to a fluid catalytic cracking (FCC) unit in the modeled refinery due to its higher oxygen content compared to other lipid-based feedstocks. Life cycle analysis (LCA) using two distinct approaches—process-level energy allocation and a refinery-level marginal approach—has been conducted for each case. The LCA results using process-level allocation show that there are no noticeable changes in emissions or energy use impacts at the facility level. The life cycle GHG emission reductions of co-processing cases are mainly related to the fraction of biogenic carbon embedded in each fuel product. For example, co-processed jet fuels (a mixture of fossil and biogenic fuels) made via hydrotreating or hydrocracking have higher biogenic carbon, which results in jet fuel CI reductions of 3.9%–8.6% compared to the CI of baseline petroleum jet fuels on a WTW basis. However, analysis of co-processed pyrolysis oil in an FCC shows that a higher fraction of biofeedstocks (29%) becomes process emissions (i.e., CO and CO 2 ), mainly due to the oxygenates in pyrolysis oil, and so it generates less renewable fuel than biofeedstocks co-processed via hydrotreating or hydrocracking. Using the refinery-level marginal approach, the changes in energy use and emissions of co-processing cases compared to the petroleum-only baseline case are allocated to the changes in fuel production (assuming renewable fuels). This approach generates the life cycle GHG emission values of co-processed renewable fuels, which are comparable to the CIs of standalone biofuel production pathways. However, as this approach relies on a rough assumption that product yields and emissions from co-processing units on fossil feedstocks remain the same with and without biofeedstock inputs, co-processing cases like FCC pyrolysis oil may generate quite skewed results.

02 PETROLEUM↗
DOE OSTI2020

Reserve Model of Energy Storage in Day-ahead Joint Energy and Reserve Markets: A Stochastic UC Solution

With many favorable advantages including fast response ability in particular, utility-level energy storage systems (ESS) are being integrated into energy and reserve markets to help mitigate uncertain renewable resources and fluctuant demands. This paper discusses a stochastic unit commitment (UC) model to explore capabilities of ESSs in providing valuable grid services by simultaneously joining energy and reserve markets. The proposed reserve model of ESSs presents the following features: (i) two constraints are proposed to formulate ESS’s reserve provision ability in each hour via six operation modes, namely increasing/reducing the level of charge, switching to discharge, increasing/reducing the level of discharge, switching to charge; (ii) as an energy-limited asset, constraints on ESS’s reserve deliverability across multiple hours is introduced to hold enough state of charge (SOC) headroom and floor room, guaranteeing that reserves cleared in individual hours are continuously deliverable over multiple successive hours without violating operation limits. Furthermore, a scenario-based UC model, which addresses uncertainties of renewables and demands, is adopted to evaluate reserves provided by ESSs and generators. To effectively solve the stochastic UC problem, the progressive hedging algorithm with heuristic approaches is discussed. A 6-bus system and a modified IEEE 118-bus system are used to illustrate effectiveness of the proposed approaches.

25 ENERGY STORAGE↗
DOE OSTI2022

BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

Array↗
DOE OSTI2020

Chemical trends of deep levels in van der Waals semiconductors

Abstract Properties of semiconductors are largely defined by crystal imperfections including native defects. Van der Waals (vdW) semiconductors, a newly emerged class of materials, are no exception: defects exist even in the purest materials and strongly affect their electrical, optical, magnetic, catalytic and sensing properties. However, unlike conventional semiconductors where energy levels of defects are well documented, they are experimentally unknown in even the best studied vdW semiconductors, impeding the understanding and utilization of these materials. Here, we directly evaluate deep levels and their chemical trends in the bandgap of MoS 2 , WS 2 and their alloys by transient spectroscopic study. One of the deep levels is found to follow the conduction band minimum of each host, attributed to the native sulfur vacancy. A switchable, DX center - like deep level has also been identified, whose energy lines up instead on a fixed level across different hosts, explaining a persistent photoconductivity above 400 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Practically Accessible All-Solid-State Batteries Enabled by Organosulfide Cathodes and Sulfide Electrolytes

The combination of organic electrode materials and sulfide electrolytes is expected to enable the development of all-solid-state organic batteries featuring high energy density and long-term sustainability. In this work, thiuram hexasulfide is reported as a low-cost and high-capacity organic cathode for solid-state batteries based on sulfide electrolytes, delivering a capacity of ~600 mAh g -1 and retaining 80.8 % capacity after 500 cycles. An electrochemically reversible change of the cathode interface was revealed upon cycling. Full cell displays an oscillating stress change up to 0.6 MPa during cycling, predominated by the anode side. The energy density is 1140 Wh kg -1 at the material level and 376 Wh kg -1 at the electrode level, which are among the best-reported organic cathodes to date. A high areal capacity of 10.4 mAh cm -2 is reached with a high mass loading cathode. A dry-film approach is further explored to manufacture sheet-type cells. Free-standing electrolyte film is merely ~48 μm thick and demonstrates an ultralow areal resistance of 3.9 Ω cm 2 , significantly boosts the cell-level energy density and reduces the cell internal resistance.

25 ENERGY STORAGE↗
DOE OSTI2020

Comparative PV LCOE Calculator (Comparative Photovoltaic Levelized Cost of Energy Calculator) [SWR-20-86]

The calculator calculates levelized cost of energy (LCOE) for photovoltaic (PV) systems based on cost, performance, and reliability inputs for a baseline and a proposed technology. The user can choose inputs and watch the effect on LCOE to determine whether a proposed technology is cost-effective, perform trade-off analysis among different technology options, and do break-even analysis of cost or performance.

Silverman, Timothy↗
DOE OSTI2025

Emerging tension between isospin symmetry and nuclear deformation at 𝐴 = 78

Since the nuclear interaction is, to first order, isospin independent, simple relationships can be derived for 𝐸⁢2 strengths across isobaric analog states within an isobaric triplet. At the same time, there is a well-known relationship between the 2 + excitation energy and the 𝐵(𝐸⁢2;2 + → 0 + ) transition strength in deformed even-even nuclei. Here, we highlight a tension between these two trends in the 𝐴 = 78 isobaric triplet. Recent level-energy determinations indicate that the proton-rich member of the triplet, 78 Zr, is strongly deformed while recent lifetime measurements in 78 Sr and 78 Y, as extrapolated into 78 Zr, would imply that 78 Zr is weakly deformed. Furthermore, we explore the implications of this apparent contradiction and explore potential solutions.

59 ≤ A ≤ 89↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

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

Interfacial Engineering Using Covalent Organic Frameworks in Polymer Composites for High‐Temperature Electrostatic Energy Storage

Abstract The use of inorganic nanofillers has been an effective method to improve high‐temperature capacitive performance of dielectric polymers, though there are unmet challenges such as undesirable organic–inorganic compatibility, and low efficiencies and energy densities. Herein, a surface functionalization strategy using covalent organic frameworks (COFs) is employed to address such challenges in realizing high‐performing polymer composites. Specifically, core–shell structured nanoparticles, where ZrO 2 nanoparticles act as the core and a COF material forms the shell, are constructed and composited with the polyetherimide (PEI) matrix. The design leverages the high electron affinity ( E A ) of the outer COF shell to create energy traps, thereby capturing free charges and limiting electrical conduction. Concurrently, the low E A and wide bandgap of the ZrO 2 core introduce energy barriers to impede charge injection and migration. This orchestrated “energy level cascade” results in a marked reduction of leakage current and energy loss. The resulting polymer composite showcases an impressive discharged energy density of 6.21 J cm −3 at an efficiency above 90%, with a maximum discharged energy density reaching 7.43 J cm −3 at 150 °C. These performance metrics position the PEI/ZrO 2 @COF polymer composite to surpass or be on par with state‐of‐the‐art high‐temperature PEI composites and other advanced polymer dielectrics.

Xie, Zongliang↗
DOE OSTI2022

Correlating Electronic Structure and Device Physics with Mixing Region Morphology in High-Efficiency Organic Solar Cells

The donor/acceptor interaction in non-fullerene organic photovoltaics leads to the mixing domain that dictates the morphology and electronic structure of the blended thin film. Initiative effort is paid to understand how these domain properties affect the device performances on high-efficiency PM6:Y6 blends. Different fullerenes acceptors are used to manipulate the feature of mixing domain. It is seen that a tight packing in the mixing region is critical, which could effectively enhance the hole transfer and lead to the enlarged and narrow electron density of state (DOS). As a result, short-circuit current (J SC ) and fill factor (FF) are improved. The distribution of DOS and energy levels strongly influences open-circuit voltage (V OC ). The raised filling state of electron Fermi level is seen to be key in determining device V OC . Energy disorder is found to be a key factor to energy loss, which is highly correlated with the intermolecular distance in the mixing region. A 17.53% efficiency is obtained for optimized ternary devices, which is the highest value for similar systems. The current results indicate that a delicate optimization of the mixing domain property is an effective route to improve the V OC , J SC , and FF simultaneously, which provides new guidelines for morphology control toward high-performance organic solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Intersubband Transitions in Lead Halide Perovskite-Based Quantum Wells for Mid-Infrared Detectors

Due to their excellent optical and electrical properties as well as versatile growth and fabrication processes, lead halide perovskites have been widely considered as promising candidates for green energy and applications related to optoelectronics. Here, we investigate their potential applications at infrared wavelengths by modeling the intersubband transitions in perovskite-based quantum well systems. Both single-well and double-well structures are studied, and their energy levels as well as the corresponding wave functions and intersubband transition energies are calculated by solving the one-dimensional Schrödinger equations. Via adjustment of the quantum well and barrier thicknesses, the intersubband transition energies can be tuned to cover a broad infrared wavelength range. We also find that the lead halide perovskite-based quantum wells possess high absorption coefficients. In conclusion, the widely tunable transition energies and high absorption coefficients of the perovskite-based quantum well systems, combined with their unique material and electrical properties, may enable an alternative material system for infrared photodetector applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗