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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

First Assessment of Cloud‐Land Coupling in LASSO Large‐Eddy Simulations

Abstract To enhance our understanding of cloud simulations over land, this study provides the first assessment of coupling between cloud and land surface in the Large‐Eddy Simulation (LES) Atmospheric Radiation Measurement Symbiotic Simulation and Observation (LASSO) activity for the shallow convection scenario. The analysis of observation data reveals a diurnal cycle of cloud‐land coupling, which co‐varies with surface fluxes. However, coupled (or decoupled) cumulus clouds are inadequately simulated, manifesting as a too‐high (or low) occurrence frequency during the afternoon. This discrepancy is mirrored by the overestimated cloud liquid water path and cloud‐top height. These overestimations are linked to the overpredicted boundary‐layer development and the easier trigger of shallow convection misrepresented in LES runs. Our study underscores the need to improve the representations of boundary‐layer processes and cloud‐land interactions within LES to better simulate shallow clouds in the future.

58 GEOSCIENCES↗
DOE OSTI2024

Eliminating chemo-mechanical degradation of lithium solid-state battery cathodes during >4.5 V cycling using amorphous Nb2O5 coatings

Abstract Lithium solid-state batteries offer improved safety and energy density. However, the limited stability of solid electrolytes (SEs), as well as irreversible structural and chemical changes in the cathode active material, can result in inferior electrochemical performance, particularly during high-voltage cycling (>4.3 V vs Li/Li + ). Therefore, new materials and strategies are needed to stabilize the cathode/SE interface and preserve the cathode material structure during high-voltage cycling. Here, we introduce a thin (~5 nm) conformal coating of amorphous Nb 2 O 5 on single-crystal LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathode particles using rotary-bed atomic layer deposition (ALD). Full cells with Li 4 Ti 5 O 12 anodes and Nb 2 O 5 -coated cathodes demonstrate a higher initial Coulombic efficiency of 91.6% ± 0.5% compared to 82.2% ± 0.3% for the uncoated samples, along with improved rate capability (10x higher accessible capacity at 2C rate) and remarkable capacity retention during extended cycling (99.4% after 500 cycles at 4.7 V vs Li/Li + ). These improvements are associated with reduced cell polarization and interfacial impedance for the coated samples. Post-cycling electron microscopy analysis reveals that the Nb 2 O 5 coating remains intact and prevents the formation of spinel and rock-salt phases, which eliminates intra-particle cracking of the single-crystal cathode material. These findings demonstrate a potential pathway towards stable and high-performance solid-state batteries during high-voltage operation.

Science & Technology - Other Topics↗
DOE OSTI2023

Exciton fine structure in twisted transition metal dichalcogenide heterostructures

Abstract Moiré superlattices of transition metal dichalcogenide (TMD) heterostructures give rise to rich excitonic phenomena associated with the interlayer twist angle. Theoretical calculations of excitons in such systems are typically based on model moiré potentials that mitigate the computational cost. However, predictive understanding of the electron-hole coupling dominating the excitations is crucial to realize the twist-induced modifications of the optical selection rules. In this work, we use many-body perturbation theory to evaluate the relation between twist angle and exciton properties in TMD heterostructures. We present an approach for unfolding excitonic states from the moiré Brillouin zone onto the separate-layer ones. Applying this method to a large-angle twisted MoS 2 /MoSe 2 bilayer, we find that the optical spectrum is dominated by mixed electron–hole transitions with different momenta in the separate monolayers, leading to unexpected hybridization between interlayer and intralayer excitons. Our findings offer a design pathway for exciton layer-localization in TMD heterostructures.

Chemistry↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2024

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Particle composition measurements for ultrafine particles collected at the EPCAPE Mount Soledad site from 04-27-2023 to 06-13-2023 using a Thermal Desorption Chemical Ionization Mass Spectrometer

The dataset contains particle composition data for both the positive and negative reagent ion modes of the Thermal Desorption Chemical Ionization Mass Spectrometer (TDCIMS). The dataset is split into two directories: one for particles with diameters of 30 nm and the other for particles with diameters of less than 100 nm. The positive reagent ion mode uses H3O+ as the reagent ion, and ionization usually occurs through hydrogen addition. The negative mode uses O2- as the reagent ion. Negative mode ionization generally occurs through hydrogen abstraction, but O2- addition is also possible. Ion concentrations were normalized to total ion counts, and unknown ions were then removed from the data. The name of each ion fraction time series includes the mass to charge ratio and the chemical formula for the ion. Time is recorded in seconds since 1/1/1904. The time zone is UTC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Tailoring the Weight of Surface and Intralayer Edge States to Control LUMO Energies

Abstract The energies of the frontier molecular orbitals determine the optoelectronic properties in organic films, which are crucial for their application, and strongly depend on the morphology and supramolecular structure. The impact of the latter two properties on the electronic energy levels relies primarily on nearest‐neighbor interactions, which are difficult to study due to their nanoscale nature and heterogeneity. Here, an automated method is presented for fabricating thin films with a tailored ratio of surface to bulk sites and a controlled extension of domain edges, both of which are used to control nearest‐neighbor interactions. This method uses a Langmuir–Schaefer‐type rolling transfer of Langmuir layers (rtLL) to minimize flow during the deposition of rigid Langmuir layers composed of π‐conjugated molecules. Using UV–vis absorption spectroscopy, atomic force microscopy, and transmission electron microscopy, it is shown that the rtLL method advances the deposition of multi‐Langmuir layers and enables the production of films with defined morphology. The variation in nearest‐neighbor interactions is thus achieved and the resulting systematically tuned lowest unoccupied molecular orbital (LUMO) energies (determined via square‐wave voltammetry) enable the establishment of a model that functionally relates the LUMO energies to a morphological descriptor, allowing for the prediction of the range of accessible LUMO energies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optically Induced Static Magnetization in Metal Halide Perovskite for Spin‐Related Optoelectronics

Abstract Understanding the feasibility to couple semiconducting and magnetic properties in metal halide perovskites through interface design opens new opportunities for creating the next generation spin‐related optoelectronics. In this work, a fundamentally new phenomenon of optically induced magnetization achieved by coupling photoexcited orbital magnetic dipoles with magnetic spins at perovskite/ferromagnetic interface is discovered. The depth‐sensitive polarized neutron reflectometry combined with in situ photoexcitation setup, constitutes key evidence of this novel effect. It is demonstrated that a circularly polarized photoexcitation induces a stable magnetization signal within the depth up to 7.5 nm into the surface of high‐quality perovskite (MAPbBr 3 ) film underneath a ferromagnetic cobalt layer at room temperature. In contrast, a linearly polarized light does not induce any detectable magnetization in the MAPbBr 3 . The observation reveals that photoexcited orbital magnetic dipoles at the surface of perovskite are coupled with the spins of the ferromagnetic atoms at the interface, leading to an optically induced magnetization within the perovskite’s surface. The finding demonstrates that perovskite semiconductor can be bridged with magnetism through optically controllable method at room temperature in this heterojunction design. This provides the new concept of utilizing spin and orbital degrees of freedom in new‐generation spin‐related optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Chip-integrated metasurface full-Stokes polarimetric imaging sensor

Abstract Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Chip-integrated, fast, cost-effective, and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a chip-integrated Meta surface-based Full-Stokes Polar imetric Im aging sensor (MetaPolarIm) realized by integrating an ultrathin (~600 nm) metasurface polarization filter array (MPFA) onto a visible imaging sensor with CMOS compatible fabrication processes. The MPFA is featured with broadband dielectric-metal hybrid chiral metasurfaces and double-layer nanograting polarizers. This chip-integrated polarimetric imaging sensor enables single-shot full-Stokes imaging (speed limited by the CMOS imager) with the most compact form factor, records high measurement accuracy, dual-color operation (green and red) and a field of view up to 40 degrees. MetaPolarIm holds great promise to enable transformative applications in autonomous vision, industry inspection, space exploration, medical imaging and diagnosis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multiparameter laser performance characterization of liquid crystals for polarization control devices in the nanosecond regime

Abstract Interactions of liquid crystals (LC’s) with polarized light have been studied widely and have spawned numerous device applications, including the fabrication of optical elements for high-power and large-aperture laser systems. Currently, little is known about both the effect of incident polarization state on laser-induced–damage threshold (LIDT) and laser-induced functional threshold (LIFT) behavior at sub-LIDT fluences under multipulse irradiation conditions. This work reports on the first study of the nanosecond-pulsed LIDT’s dependence on incident polarization for several optical devices employing oriented nematic and chiral-nematic LC’s oriented by surface alignment layers. Accelerated lifetime testing was also performed to characterize the ability of these devices to maintain their functional performance under multipulse irradiation as a function of the laser fluence at both 1053 nm and 351 nm. Results show that the LIDT varies as a function of input polarization by 30–80% within the same device, while the multipulse LIFT (which can differ from the nominal LIDT) depends on irradiation conditions such as laser fluence and wavelength.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Wildfire exacerbates high-latitude soil carbon losses from climate warming

Abstract Arctic and boreal permafrost soil organic carbon (SOC) decomposition has been slower than carbon inputs from plant growth since the last glaciation. Anthropogenic climate warming has threatened this historical trend by accelerating SOC decomposition and altering wildfire regimes. We accurately modeled observed plant biomass and carbon emissions from wildfires in Alaskan ecosystems under current climate conditions. In projections to 2300 under the RCP8.5 climate scenario, we found that warming and increased atmospheric CO 2 will result in plant biomass gains and higher litterfall. However, increased carbon losses from (a) wildfire combustion and (b) rapid SOC decomposition driven by increased deciduous litter production, root exudation, and active layer depth will lead to about 4.4 PgC of soil carbon losses from Alaska by 2300 and most (88%) of these loses will be from the top 1 m of soil. These SOC losses offset plant carbon gains, causing the ecosystem to transition to a net carbon source after 2200. Simulations excluding wildfire increases yielded about a factor of four lower SOC losses by 2300. Our results show that projected wildfire and its direct and indirect effects on plant and soil carbon may accelerate high-latitude soil carbon losses, resulting in a positive feedback to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhancing Cycling Stability of Lithium Metal Batteries by a Bifunctional Fluorinated Ether

Abstract The lifespan of lithium (Li) metal batteries (LMBs) can be greatly improved by the formation of inorganic‐rich electrode‐electrolyte interphases (EEIs) (including solid‐electrolyte interphase on anode and cathode‐electrolyte interphase on cathode). In this work, a localized high‐concentration electrolyte containing lithium bis(fluorosulfonyl)imide (LiFSI) salt, 1,2‐dimethoxyethane (DME) solvent and 1,2‐bis(1,1,2,2‐tetrafluoroethoxy)ethane (BTFEE) diluent is optimized. BTFEE is a fluorinated ether with weakly‐solvating ability for LiFSI so it also acts as a co‐solvent in this electrolyte. It can facilitate anion decomposition at electrode surfaces and promote the formation of more inorganic‐rich EEI layers. With an optimized molar ratio of LiFSI:DME:BTFEE = 1:1.15:3, LMBs with a high loading (4 mAh cm −2 ) lithium nickel manganese cobalt oxide (LiNi 0.8 Mn 0.1 Co 0.1 ) cathode can retain 80% capacity in 470 cycles when cycled in a voltage range of 2.8–4.4 V. The fundamental understanding on the functionality of BTFEE revealed in this work provides new perspectives on the design of practical high‐energy density battery systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Observational Evidence of State‐Dependent Positive and Negative Land Surface Feedback on Afternoon Deep Convection Over the Southern Great Plains

Abstract The detection of soil moisture‐precipitation feedback is complicated by the dependence of feedback sign on atmospheric state and cloud types. We evaluated responses of convective clouds to changes in surface evaporative fraction (EF) from satellite and ground‐based measurements over the Southern Great Plains in summer months. Strong responses to EF were inferred only after accounting for tropospheric states supporting systematically positive or negative responses. State dependence (inferred from controlled model experiments) explains the weak response found in previous studies. The probability of transitions from shallow to deep cumulus, and subsequent precipitation onset, increases by 58% over drier surfaces in weakly stratified environments having deeper boundary layers. Boundary layer dynamics play a more important role than low‐level relative humidity in these environments. Wetter surfaces yield increased deep convection in moist and thermally stratified environments. The distinct pathways of convective initiation over dry and wet land surfaces can play important roles in drought onset and termination.

Qiu, Shaoyue↗
DOE OSTI2025

New Era Towards Autonomous Additive Manufacturing: A Review of Recent Trends and Future Perspectives

Abstract The Additive Manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of Artificial Intelligence (AI) and Digital Twin (DT). However, current Intelligent Additive Manufacturing (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction (HMI). This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to Autonomous Additive Manufacturing (AAM), featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing (ISM). Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.

Fan, Haolin↗
DOE OSTI2024

Formation kinetics and thermodynamic stability of the Aurivillius compounds in Bi 4 Ti 3 O 12 –BiFeO 3 system

Abstract The Aurivillius compounds in the Bi 2 O 3 –Fe 2 O 3 –TiO 2 system, combining ferroelectric, semiconducting, and ferromagnetic properties, have attracted particular interest. Formation kinetics and thermodynamic stability are the fundamental knowledge needed for modeling and predicting the temporal microstructure and property evolution during materials processing but have not yet been addressed by quantitative experimental measurement. This article focuses on the Bi n +1 Fe n –3 Ti 3 O 3 n +3 Aurivillius compounds on the Bi 4 Ti 3 O 12 –BiFeO 3 tie‐line to elucidate the mechanisms and thermodynamic controls responsible for phase formation of compounds with various perovskite‐like layers. Five high‐purity Aurivillius compounds Bi 4 Ti 3 O 12 , Bi 5 FeTi 3 O 15 , Bi 6 Fe 2 Ti 3 O 18 , Bi 7 Fe 3 Ti 3 O 21 , and Bi 8 Fe 4 Ti 3 O 24 with integer n = 3–7 values were synthesized and their phase transformation properties and enthalpies of formation were studied by X‐ray diffraction in situ, high temperature differential scanning calorimetry, and high temperature oxide melt solution calorimetry. Thermodynamic stability of the compounds decreases with increasing n , and formation kinetics gradually slow down, demonstrating the inherent difficulty to synthesize pure Aurivillius compounds with n larger than 8. This difficulty was confirmed by an impurity phase coexisting with Bi 9 Fe 5 Ti 3 O 27 .

Gong, Weiping↗
DOE OSTI2020

Temperature dependence of nickel ion release from nitinol medical devices

Abstract Nitinol exhibits unique (thermo)mechanical properties that make it central to the design of many medical devices. However, nitinol nominally contains 50 atomic percent nickel, which if released in sufficient quantities, can lead to adverse health effects. While nickel release from nitinol devices is typically characterized using in vitro immersion tests, these evaluations require lengthy time periods. We have explored elevated temperature as a potential method to expedite this testing. Nickel release was characterized in nitinol materials with surface oxide thickness ranging from 12 to 1564 nm at four different temperatures from 310 to 360 K. We found that for three of the materials with relatively thin oxide layers, ≤ 87 nm nickel release exhibited Arrhenius behavior over the entire temperature range with activation energies of 80 to 85 kJ/mol. Conversely, the fourth ''black–oxide'' material, with a much thicker, complex oxide layer, was not well characterized by an Arrhenius relationship. Power law release profiles were observed in all four materials; however, the exponent from the thin oxide materials was approximately 1/4 compared with 3/4 for the black–oxide material. To illustrate the potential benefit of using elevated temperature to abbreviate nickel release testing, we demonstrated that a > 50 day 310 K release profile could be accurately recovered by testing for less than 1 week at 340 K. However, because the materials explored in this study were limited, additional testing and mechanistic insight are needed to establish a protective temperature scaling that can be applied to all nitinol medical device components.

Saylor, David M.↗
DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗