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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Challenges in Postprocessing of Hybrid Manufacturing of Magnet Polymer Composites to Enhance the Energy Product of NdFeB Bonded Magnets

Bonded permanent magnets need to process magnetic filler particles and binder together which is a form of hybrid or composite manufacturing that needs to simultaneously process multimaterials. Polymer bonding of the permanent magnets decreases the energy product of the bonded magnets which varies with square of loading fraction of magnetic materials. However, due to high viscosity of bonding polymer prohibits loading higher than 80 volume % of magnetic materials in most efficient loading process such as compression molding. To further increase the loading of magnetic fraction in the bonded permanent magnets, it is expected to be beneficial to introduce a fraction of nonreacting thermally sacrificial polymer along with permanent bonding polymer in the least permissible of about 20 volume % of the polymer. This process of first adding the sacrificial polymer for higher processability for composites and later removing it via post processing such as heat treatment provides a simple example of inclusion of both the additive and the subtractive stages of hybrid manufacturing. These hybrid composite magnets can be heat treated to remove the sacrificial polymer to further increase the density and energy product which might require to implement hot isostatic pressing for compaction. Although the identification of such ideal polymer requires an extensive polymer chemistry research, we have compression molded several permanent bonding polymers such as nylon, polycarbonate (PC) and polyphenyl sulfide (PPS) and potential sacrificial polymer such as polyoxymethylene (POM), polystyrene (PS), and Acrylonitrile Butadiene Styrene (ABS) and heat treated to remove the sacrificial polymers. It is discovered that oxygen free low temperature pyrolizable polymers could be the potential candidates for increasing the magnet particles loading for achieving high energy products.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interligand Coupling Drives Fast Triplet Energy Transfer Routes in PbS/Tetracene Quantum Dot Hybrids

The binding of photoactive organic ligands to inorganic quantum dots (QDs) creates a versatile hybrid architecture that allows access to photophysical processes such as efficient triplet exciton generation with near-infrared radiation. Here we report the subnanosecond generation of a hybrid triplet state with mixed ligand-QD character by replacing native oleate ligands on small PbS QDs with 5,12-tetracenepropiolic acid, a bifunctional ligand with two carboxylic acids that tends to lie face-on with the QD surface at low loadings. The face-on geometry engenders a regime of strong electronic coupling that is evident in steady-state absorption and hastens triplet energy flow by several orders of magnitude compared with more typical tetracene-based ligands exhibiting weak coupling. We further determined via Fourier transform infrared (FTIR) and supported by density functional theory (DFT)-based geometry optimizations that high ligand loading causes a shift in QD-ligand mutual disposition toward an edge-on geometry that instigates the formation of intermolecular excited states characterized by triplet excimer-like features in photoluminescence and transient absorption. Our results demonstrate the ability to control strongly coupled ligand-QD systems toward ultrafast generation of photophysically relevant species such as triplets that are valuable for photon upconversion and catalysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A hybrid data–model approach to map soil thickness in mountain hillslopes

Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

58 GEOSCIENCES↗
DOE OSTI2023

Hybrid Al Casting + AM Components for Automotive Applications

Although casting Aluminum (Al) alloys is widely used for automotive application, the conventional casting process is showing limitations for modern vehicle production. A hybrid manufacturing method that is capable of fabricating automotive structures with tailored functionality and tuned attributes at a high production rate is proposed in this project, utilizing the merits of conventional casting methods and additive manufacturing (AM) methods. Ford is interested in supplementing conventional casting processes (e.g., shape casting, high pressure die casting) with local modifications to geometry and microstructure using additive manufacturing techniques. Two additive manufacturing methods, wire-arc and laser hot-wire directed energy deposition AM process, were evaluated by deposition of 4043 filler wire onto high-pressure die cast and permanent mold cast Al-Si alloys. The feasibility of this hybrid manufacturing method was determined through trial deposition and evaluation of defects, microstructure, and mechanical properties. Laser hot-wire deposition was found to be beneficial for this application with low porosity, a refined microstructure, and mechanical properties similar to those reported in the literature for 4043. The mechanical properties of the interface between the A356 casting and deposited 4043 were found to be controlled by the weaker deposit material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Flap Endonuclease 1 Endonucleolytically Processes RNA to Resolve R-Loops through DNA Base Excision Repair

Flap endonuclease 1 (FEN1) is an essential enzyme that removes RNA primers and base lesions during DNA lagging strand maturation and long-patch base excision repair (BER). It plays a crucial role in maintaining genome stability and integrity. FEN1 is also implicated in RNA processing and biogenesis. A recent study from our group has shown that FEN1 is involved in trinucleotide repeat deletion by processing the RNA strand in R-loops through BER, further suggesting that the enzyme can modulate genome stability by facilitating the resolution of R-loops. However, it remains unknown how FEN1 can process RNA to resolve an R-loop. In this study, we examined the FEN1 cleavage activity on the RNA:DNA hybrid intermediates generated during DNA lagging strand processing and BER in R-loops. We found that both human and yeast FEN1 efficiently cleaved an RNA flap in the intermediates using its endonuclease activity. We further demonstrated that FEN1 was recruited to R-loops in normal human fibroblasts and senataxin-deficient (AOA2) fibroblasts, and its R-loop recruitment was significantly increased by oxidative DNA damage. We showed that FEN1 specifically employed its endonucleolytic cleavage activity to remove the RNA strand in an R-loop during BER. We found that FEN1 coordinated its DNA and RNA endonucleolytic cleavage activity with the 3'-5' exonuclease of APE1 to resolve the R-loop. Our results further suggest that FEN1 employed its unique tracking mechanism to endonucleolytically cleave the RNA strand in an R-loop by coordinating with other BER enzymes and cofactors during BER. Our study provides the first evidence that FEN1 endonucleolytic cleavage can result in the resolution of R-loops via the BER pathway, thereby maintaining genome integrity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Structural Diversity and Tunable Emission in Hybrid Organic–Inorganic Copper(I) Bromides

Recently, hybrid organic−inorganic copper(I) metal halides have attracted global attention due to their intriguing optical properties and low-cost solution processability. In this work, we report three hybrid organic−inorganic copper(I) bromides, [TMPA] 2 [Cu 2 Br 4 ], [TMPA] 4 [Cu 6 Br 10 ], and [TMPA] 2 [Cu 4 Br 6 ], synthesized through a slow evaporation method using trimethylphenylammonium (TMPA + ) as the organic cation. By precise control of the CuBr and TMPABr precursors, different copper halide [Cu 2 Br 4 ] 2− , [Cu 6 Br 10 ] 4− , and [Cu 4 Br 6 ] 2− structural units can be obtained. [TMPA] 2 [Cu 2 Br 4 ], [TMPA] 4 [Cu 6 Br 10 ], and [TMPA] 2 [Cu 4 Br 6 ] demonstrate distinct blue, orange, and greenish-yellow light emission, respectively. The first two compounds have zero-dimensional (0D) crystal structures in centrosymmetric triclinic space group P-1 and monoclinic space group P2 1 /n. In contrast, [TMPA] 2 [Cu 4 Br 6 ] features a unique one-dimensional (1D) structure and crystallizes in the centrosymmetric monoclinic space group P2 1 /c. Consequently, the observed greenish-yellow emission of [TMPA] 2 [Cu 4 Br 6 ] is also unique, in contrast to the typical orange-red emission of 0D [Cu 4 Br 6 ]-based compounds. This work provides insights into the design of copper halide light emitters and emphasizes the influence of structural dimensionality on photoluminescence. The tunable optical properties suggest the potential of these materials for multicolor photopatterning, information encryption, and anticounterfeiting applications.

Anions↗
DOE OSTI2024

Unraveling the mechanism of tip-enhanced molecular energy transfer

Electronic Energy Transfer (EET) between chromophores is fundamental in many natural light-harvesting complexes, serving as a critical step for solar energy funneling in photosynthetic plants and bacteria. The complicated role of the environment in mediating this process in natural architectures has been addressed by recent scanning tunneling microscope experiments involving EET between two molecules supported on a solid substrate. These measurements demonstrated that EET in such conditions has peculiar features, such as a steep dependence on the donor-acceptor distance, reminiscent of a short-range mechanism more than of a Förster-like process. By using state of the art hybrid ab initio/electromagnetic modeling, here we provide a comprehensive theoretical analysis of tip-enhanced EET. In particular, we show that this process can be understood as a complex interplay of electromagnetic-based molecular plasmonic processes, whose result may effectively mimic short range effects. Therefore, the established identification of an exponential decay with Dexter-like effects does not hold for tip-enhanced EET, and accurate electromagnetic modeling is needed to identify the EET mechanism.

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

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Seasonal Reconfiguration of Electrical Distribution Systems to Mitigate the Impact of Electric Vehicle Charging

Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO2 emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Iterative hybrid manufacture of a titanium alloy component

Here, this paper describes an iterative hybrid (additive + subtractive) manufacturing approach for a titanium alloy (Ti6Al4V) part using a laser hotwire directed energy deposition system (LHWDED) and a traditional four-axis milling machine tool. The term iterative hybrid manufacturing is used to described hybrid manufacturing where the additive and subtractive operations occur in multiple stages rather than sequentially. It is currently common to produce an entire part by sequential hybrid manufacturing by additively manufacturing (AM) an entire preform geometry that then requires post processing by another machine tool to create final part features. By contrast, a part produced by iterative hybrid manufacturing (IHM) does not produce the entire preform geometry in a single AM process. Instead, a portion of the entire preform geometry is manufactured by an AM process, then that portion is transferred to another machine tool which creates features in that portion, and then that machined portion is transferred back to the AM machine to complete another AM process. There is no limit to the number of iterations that an IHM process can have. IHM offers several advantages over sequential hybrid manufacturing such as the use of shorter and stiffer subtractive tooling, better access to part geometries that require subtractive processes, and the separation of the AM heat source from the subtractive machine tool. A titanium alloy demonstration part was successfully manufactured by IHM with three iterations using a shared pallet system between the AM machine tool and the subtractive machine tool.

Hybrid manufacturing↗
DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Fabrication Development for SPT-SLIM, a Superconducting Spectrometer for Line Intensity Mapping

Line Intensity Mapping (LIM) is a new observational technique that uses low-resolution observations of line emission to efficiently trace the large-scale structure of the Universe out to high redshift. Common mm/sub-mm emission lines are accessible from ground-based observatories, and the requirements on the detectors for LIM at mm-wavelengths are well matched to the capabilities of large-format arrays of superconducting sensors. We describe the development of an R = lambda/Delta lambda = 300 on-chip superconducting filter-bank spectrometer covering the 120-180 GHz band for future mm-LIM experiments, focusing on SPT-SLIM, a pathfinder LIM instrument for the South Pole Telescope. Radiation is coupled from the telescope optical system to the spectrometer chip via an array of feedhorn-coupled orthomode transducers. Superconducting microstrip transmission lines then carry the signal to an array of channelizing half-wavelength resonators, and the output of each spectral channel is sensed by a lumped element kinetic inductance detector (leKID). Key areas of development include incorporating new low-loss dielectrics to improve both the achievable spectral resolution and optical efficiency and development of a robust fabrication process to create a galvanic connection between ultra-pure superconducting thin-films to realize multi-material (hybrid) leKIDs. We provide an overview of the spectrometer design, fabrication process, and prototype devices.

microstrip resonators↗
DOE OSTI2025

Hybrid Additive and Subtractive Manufacturing of Dual‐Wavelength Photopolymer Thermosets

Additive manufacturing (AM) techniques such as digital light processing (DLP) AM and stereolithography enable the production of highly complex structures with speed and ease as not previously possible. However, many traditional thermosetting photopolymers used in these processes result in a permanently crosslinked structure incapable of being broken down or modified without the use of energy-intensive processes such as mechanical machining or ablation. To overcome this limitation, this production is described of thiol-ene photopolymer thermosets with dual-wavelength photopolymerization and photodegradation reaction pathways. The dual wavelength selectivity of the polymerization (405 nm) and degradation (365 nm) processes, combined with dual-wavelength DLP AM technology, enables hybrid manufacturing with the ability to add and sequentially subtract material with excellent spatiotemporal control. 2D lithographs and complex 3D architectures with soft elastomeric properties are produced with resolutions as small as 50 µm. Furthermore, the ability to selectively subtract supports from 3D printed structures with deliberate and accurate control is demonstrated. In conclusion, the ability to correct or modify existing structures enables a new paradigm of adaptable materials for corrective manufacturing techniques and temporary structures.

3D Printing↗
DOE OSTI2022

Coherent Coupling of Two Remote Magnonic Resonators Mediated by Superconducting Circuits

We demonstrate microwave-mediated distant magnon-magnon coupling on a superconducting circuit platform, incorporating chip-mounted single-crystal Y 3 Fe 5 O 12 (YIG) spheres. Coherent level repulsion and dissipative level attraction between the magnon modes of the two YIG spheres are demonstrated. The former is mediated by cavity photons of a superconducting resonator, and the latter is mediated by propagating photons of a coplanar waveguide. Overall, our results open new avenues toward exploring integrated hybrid magnonic networks for coherent information processing on a quantum-compatible superconducting platform.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Real‐Time Optical Process Monitoring for Structure and Property Control of Aerosol Jet Printed Functional Materials

Abstract Aerosol jet printing is a popular digital additive manufacturing method for flexible and hybrid electronics, but it lacks sophisticated real‐time process control schemes that would enable more widespread adoption in manufacturing environments. Here, an optical measurement system is introduced to track the aerosol density upstream of the printhead. The measured optical extinction, combined with the aerosol flow rate, is directly related to deposition rate and accurately predicts functional materials properties such as the electrical resistance of printed graphene films. This real‐time system offers a compelling solution for process drift and batch‐to‐batch variability, rendering it a valuable tool for both real‐time control of aerosol jet printing and fundamental studies of the underlying process science.

Tafoya, Rebecca R.↗
DOE OSTI2023

3D simulation of lower-hybrid drift waves in strong guide field asymmetric reconnection in laboratory experiments

The magnetic reconnection experiment has recently seen short wavelength ([Formula: see text]) lower-hybrid waves near the electron diffusion region in strong guide field reconnection. Based on plasma parameters from the experiment, we perform a three-dimensional fully kinetic simulation in order to investigate the generation of the lower-hybrid waves and their effects on the reconnection process. We find that the low-beta regions around the reconnection site are unstable to the lower-hybrid drift instability propagating in the outflow direction, driven by the difference between the electron and ion outflows. The waves modify the electron distributions, leading to periodic opening and closing of gaps in electron velocity space, and provide a small contribution to the anomalous resistivity. Finally, the simulation results are discussed in the context of space observations and laboratory experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

System Advisor Model (SAM) Improvements for Emerging Solar Thermal Applications

The System Advisor Model (SAM) is an open-source tool developed by NREL to simulate the techno-economic performance of technologies like photovoltaics, solar thermal, wind, and geothermal. NREL has recently completed, active, and planned projects to improve the solar thermal models in SAM and its underlying code base to represent emerging component technologies, systems, and applications. This poster describes new capabilities in the SolarPILOT optical modeling tool, supercritical carbon dioxide (sCO2) power cycles, and solar industrial process heat.

14 SOLAR ENERGY↗
DOE OSTI2021

Pore-scale simulation of drying in porous media using a hybrid lattice Boltzmann: pore network model

In this work, a hybrid method coupling a pseudo-potential lattice Boltzmann model (LBM) and a pore network model (PNM) to simulate drying in porous media is proposed. Based on the watershed method, the porous medium is firstly decomposed into pore regions. According to the liquid–vapor phase distribution at a given time, the pore regions are further divided into four pore types, namely two-phase pores where a liquid–vapor interface exists, buffer pores next to the two-phase pores, single-liquid and single-vapor phase pores. The pseudo-potential LBM is used in the two-phase and buffer pores to simulate liquid drying and track the movement of the interfaces, while the single-phase PNM simulations are conducted in the buffer and single-phase pores to simulate vapor or liquid flow. LBM and PNM are coupled in the buffer pores through exchange of boundary information. The hybrid method is applied to simulate liquid drying in a porous medium. The whole-domain LBM simulation is considered as the reference solution to validate the hybrid method. Liquid saturation variation during the drying process and detailed phase and pressure distributions obtained by the two methods match quite well, demonstrating the accuracy of the hybrid method. For the specific case studied, the hybrid method saves more than 60% computational time compared to the whole-domain LBM simulation. In addition, the speedup of the hybrid method becomes more significant for a larger computational domain. In summary, the hybrid method developed in this work combines the accuracy of LBM and the efficiency of PNM to simulate drying in porous media at pore scale and can lead to significant reduction of computation time, thus allowing the pore-scale consideration of drying in larger porous systems.

36 MATERIALS SCIENCE↗