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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effect of heterostructure engineering on electronic structure and transport properties of two-dimensional halide perovskites

Organic-inorganic halide perovskite solar cells have attracted much attention due to their low-cost fabrication, flexibility, and high-power conversion efficiency. More recent efforts show that the reduction from three- to two-dimensions (2D) of organic–inorganic halide perovskites promises an exciting opportunity to tune their electronic properties. Here, we explore the effect of reduced dimensionality and heterostructure engineering on the intrinsic material properties, such as energy stability, bandgap and transport properties of 2D hybrid organic–inorganic halide perovskites using first-principles density functional theory. We show that the energy stability of engineered perovskite heterostructures is significantly enhanced. The heterostructures with improved stability also show excellent transport properties similar to their bulk counterparts. Furthermore, these layered chemistries demonstrate the advantage of a broad range of tunable bandgaps and high-absorption coefficient in the visible spectrum. The proposed 2D heterostructured material holds potential for nano-optoelectronic devices as well as for effective photovoltaics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Transgenic Poplar Designed for Biofuels

Members of the genus Populus (i.e., cottonwood, hybrid poplar) represent a promising source of lignocellulosic biomass for biofuels. However, one of the major factors negatively affecting poplar’s efficient conversion to biofuel is the inherent recalcitrance to enzymatic saccharification due to cell wall components such as lignin. To this effect, there have been efforts to modify gene expression to reduce biomass recalcitrance by changing cell wall properties. Furthermore, we review recent genetic modifications of poplar that led to change cell wall properties and the resulting effects on subsequent pretreatment efficacy and saccharification. Although genetic engineering’s impacts on cell wall properties are not fully predictable, recent studies have shown promising improvement in the biological conversion of transgenic poplar to biofuels.

09 BIOMASS FUELS↗
DOE OSTI2019

Attempts to develop an enzyme converting DHIV to KIV

Abstract Dihydroxy-acid dehydratase (DHAD) catalyzes the dehydration of R-2,3-dihydroxyisovalerate (DHIV) to 2-ketoisovalerate (KIV) using an Fe-S cluster as a cofactor, which is sensitive to oxidation and expensive to synthesize. In contrast, sugar acid dehydratases catalyze the same chemical reactions using a magnesium ion. Here, we attempted to substitute the high-cost DHAD with a cost-efficient engineered sugar acid dehydratase using computational protein design (CPD). First, we tried without success to modify the binding pocket of a sugar acid dehydratase to accommodate the smaller, more hydrophobic DHIV. Then, we used a chemically activated substrate analog to react with sugar acid dehydratases or other enolase superfamily enzymes. Mandelate racemase from Pseudomonas putida (PpManR) and the putative sugar acid dehydratase from Salmonella typhimurium (StPutD) showed beta-elimination activity towards chlorolactate (CLD). CPD combined with medium-throughput selection improved the PpManR kcat/KM for CLD by four-fold. However, these enzyme variants did not show dehydration activity towards DHIV. Lastly, assuming phosphorylation could also be a good activation mechanism, we found that mevalonate-3-kinase (M3K) from Picrophilus torridus (PtM3K) exhibited adenosine triphosphate (ATP) hydrolysis activity when mixed with DHIV, indicating phosphorylation activity towards DHIV. Engineering PpManR or StPutD to accept 3-phospho-DHIV as a substrate was performed, but no variants with the desired activity were obtained.

Oki, Kenji↗
DOE OSTI2022

Developing novel electrodes with ultralow catalyst loading for high-efficiency hydrogen production in proton exchange membrane electrolyzer cells

Hydrogen plays more crucial roles for decarbonizing the planets and meeting the climate challenges because of its high energy density and zero-emission. It can be produced with proton exchange membrane electrolyzer cells (PEMECs) driven by sustainable and renewable energy resources. Although PEMECs have a number of advantages, including high purity production, quick response, and the ability to operate at high pressure facilitating the gas delivering, their performance and cost greatly hinder their commercial-scale applications. To achieve high-efficiency and cost-reduced hydrogen production in PEMECs, we proposed thin engineered liquid/gas diffusion layers (LGDLs) and associated electrodes, i.e., catalyst-coated LGDLs (CCLGDLs), over conventional porous transport layers (PTLs) and catalyst-coated membranes (CCMs). The research approaches in this project are based on material synthesis, in-situ and ex-situ characterizations, component design and treatment, numerical modeling, and cost analysis. The thin and tunable LGDLs (TT-LGDLs) and CCLGDLs were successfully developed with great performance improvement as demonstrated in lab-scale, bench-scale, and system-scale electrolyzer tests. The electrode thickness was reduced from 370 µm to less than 100 µm with simplified fabrication processes. With the catalytically enhanced Ir-based catalyst coating, the as-developed CCLGDLs with a catalyst loading of 0.34 mg Ir /cm 2 achieved a cell performance of 1.77 V at 2 A cm -2 , exhibiting the catalyst mass activity enhanced by >20 times with significant catalyst saving over conventional catalyst cell design. In-situ PEMEC characterizations, including the current distribution mapping and high-speed and multiscale visualizations, were conducted for a deeper understanding of mass transport and electrochemical reactions within an electrolyzer with LGDLs and CCLGDLs. A 2D cell model was developed and validated for the enhanced performance on TT-LGDL through reducing ohmic losses due to nonuniform hydration and water transport. Further, the cost analysis results have shown a path to move beyond equivalency and surpass costs associated with the project baseline. In this project, the design and fabrication of TT-LGDLs and CCLGDLs will contribute to the performance enhancement, manufacturing simplification, and cost reduction for PEMECs and other energy conversion devices, thus shortening their pathways towards commercialization. This project also provides a good foundation for furthering the in-situ reaction interface research.

08 HYDROGEN↗
DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗
DOE OSTI2024

Design, Synthesis, and Validation: Genome Scale Optimization of Energy Flux through Compartmentalized Metabolic Networks in a Model Photosynthetic Eukaryotic Microbe (Final Report)

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methodology and modeling, coupled with high throughput genome engineering strategies, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require significant advances in methodological throughput as well as improvements in detailed systems biology understanding of photosynthetic processes and cellular metabolism. The ability of diatoms to thrive in upwelling-induced, periodically nutrient-rich conditions makes them the base for the world’s shortest and most energy-efficient food webs. Diatom photosynthesis is estimated to account for between 25% and 40% of the 45-50 billion tons of organic carbon fixed annually in the sea.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Gradient flow based phase-field modeling using separable neural networks

Allen–Cahn equation is a reaction–diffusion equation and is widely used for modeling phase separation. Machine learning methods for solving the Allen–Cahn equation in its strong form suffer from inaccuracies in collocation techniques, errors in computing higher-order spatial derivatives, and the large system size required by the space–time approach. To overcome these challenges, we propose solving the gradient flow of the Ginzburg–Landau free energy functional, which is equivalent to the Allen–Cahn equation, thereby avoiding the second-order spatial derivatives associated with the Allen–Cahn equation. A minimizing movement scheme is employed to solve the gradient flow problem, eliminating the complexities of a space–time approach. We utilize a separable neural network that efficiently represents the phase field through low-rank tensor decomposition. As we use the minimizing movement scheme to numerically solve the gradient flow problem, we thus, refer to the proposed method as the Separable Deep Minimizing Movement (SDMM) method. The evaluation of the functional in the minimizing movement scheme using the Gauss quadrature technique bypasses the inaccuracies associated with collocation techniques traditionally used to solve partial differential equations. A hyperbolic tangent transformation is introduced on the phase field prior to the evaluation of the functional to ensure that it remains strictly bounded within the values of the two phases. For this transformation, theoretical guarantee for energy stability of the minimizing movement scheme is established. Our results suggest that this transformation helps to improve the accuracy and efficiency significantly. The proposed method resolves the challenges faced by state-of-the-art machine learning techniques, outperforming them in both accuracy and efficiency. It is also the first machine learning method to achieve an order of magnitude speed improvement over the finite element method. In addition to its formulation and computational implementation, several case studies illustrate the applicability of the proposed method.

42 ENGINEERING↗
DOE OSTI2020

Consideration of temperature-dependent emissivity of selective emitters in thermophotovoltaic systems

Spectral emissivity control is paramount for designing a high-efficiency selective emitter surface required for thermophotovoltaic (TPV) applications. Owing to the temperature dependency of materials optical constants, the spectral properties of a selective emitter surface changes with the emitter temperature. This paper presents the fabrication of a multilayer metal-dielectric ( ${\rm Si}_{3}{\rm N}_{4}/\rm W/Si_{3}{\rm N_{4}}$ ) coated tungsten selective emitter aimed for GaSb-based TPV systems and studies the dependence of its surface spectral emissivity, $\varepsilon (\lambda)$ , upon a temperature ranging from 300 K to 1500 K. Both the simulation and experimental methods were used to characterize $\varepsilon (\lambda)$ as a function of temperature. For wavelengths less than 1.4 µm, $\varepsilon (\lambda)$ was found to have a minimal dependence on temperature. Beyond 1.4 µm, $\varepsilon (\lambda)$ increases with the temperature. At 1.55 µm, the simulation and experimental data estimated a ${\sim}{{4}}\%$ greater emissivity at 1500 K than at room temperature. At 1500 K, the increased $\varepsilon (\lambda)$ at longer wavelengths lowered the spectral conversion efficiency of the selective emitter from 58% to 47%. The output power density, sub-bandgap loss, and TPV conversion efficiency ( ${\eta _{\rm TPV}}$ ) for a GaSb cell illuminated by the selective thermal emitter at 1500 K were estimated. ${\eta _{\rm TPV}}$ drops from 13.7% to 11% due to the increased sub-bandgap emission at 1500 K. Essential approaches for mitigating the sub-bandgap losses to further improve ${\eta _{\rm TPV}}$ are also discussed.

42 ENGINEERING↗
DOE OSTI2025

Key insights from US Department of Energy Better Plants workforce development bootcamps (2022–2025)

This study examines the effectiveness of the US Department of Energy’s Better Plants Program Bootcamps, which are designed to enhance participants’ technical skills in improving energy efficiency and optimizing operations in manufacturing facilities. Through the analysis of survey data collected from 529 participants across 9 bootcamps, the research investigates the motivations, benefits, and demographic trends of attendees. The findings reveal that skill acquisition and improvement are primary drivers for participation, with key benefits including hands-on training on diagnostic equipment and software tools, networking opportunities, and access to technical resources. The analysis shows strong participation from sectors characterized by high energy consumption and employment, such as chemical and transportation equipment manufacturing. Over 50% of participants have job titles that include “EHS” or “Energy” showing their key roles in leading energy efficiency and energy management efforts in manufacturing. Furthermore, the analysis highlights the distribution of participants across managerial, engineering, and technical roles, revealing a higher representation of managers and engineers. This observation suggests a need for targeted outreach to engage technicians, equipment operators, maintenance staff, and floor workers to ensure comprehensive workforce development. The post-bootcamp survey showed that the participants highly valued the opportunities for peer learning and idea exchange, and the benefits they gained from them. This research contributes to the advancement of manufacturing education by demonstrating the efficacy of specialized training in addressing critical industry challenges and fostering a more competent and empowered workforce.

Energy efficiency↗
DOE OSTI2026

High-throughput small-angle X-ray scattering reveals effective structure factor transitions linked to high-concentration antibody viscosity

High-concentration monoclonal antibody (mAb) formulations are often constrained by elevated viscosity, largely driven by protein–protein interactions, which complicates manufacturing and limits subcutaneous delivery. Early viscosity risk assessment is essential during discovery, yet traditional measurements require large sample volumes, and lack high-throughput capability. Here, we develop a high-throughput small-angle X-ray scattering (SAXS) protocol to detect mAb self-association at dilute concentrations, enabling early predictive insights into high-concentration viscosity. Synchrotron SAXS measurements were conducted for 21 mAbs formulated in a histidine buffer at pH 6.0. An initial subset of 10 mAbs analyzed across 1–150 mg/mL revealed that effective structure factor transitions in the low-q region, indicative of interparticle interactions, consistently emerged below 25 mg/mL. Subsequently, 11 additional mAbs were analyzed at 1–25 mg/mL using automated liquid handling and flow cells to enable high-throughput screening. High-viscosity mAbs exhibited detectable low-q upturns at concentrations ≤10 mg/mL, whereas low-viscosity mAbs showed downturns. A classification criterion based on effective structure factor transitions accurately classified all high- and low-viscosity mAbs at 150 mg/mL, offering a scalable, sample-efficient alternative to conventional methods. These results extend recent findings on the concentration-dependent sensitivity of SAXS to short-range attractions, demonstrating that they can emerge at lower concentrations than previously reported. This study presents the most comprehensive and diverse SAXS dataset for mAbs reported to date within a single formulation, providing a valuable resource for developing and validating coarse-grained models that can more accurately capture intermolecular interactions governing high-concentration solution behavior, thereby enabling rational antibody engineering and improved developability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Experimental Investigation of the Effect of Air-Handling and DME-Propane Blends on the Performance and Emissions of a 4-Cylinder CI Engine

Dimethyl ether (DME) is considered an excellent alternative to diesel because of its higher cetane number and lower carbon content. Additionally, DME can be blended with abundantly available propane with minimal modifications to the propane infrastructure. This paper focuses on an experimental investigation of the effect of air-handling i.e., boost pressure and exhaust gas recirculation (EGR), and DME-propane blends on the combustion and emissions performance of a light-duty, four-cylinder, compression ignition (CI) engine. Here, the boost pressure and EGR sweeps were carried out and showed that higher boost pressures resulted in increased brake thermal efficiencies (BTE) at the expense of higher NOx emissions which could be reduced by an increase in EGR. The fuel sweeps were carried out at 0, 15 and 25% propane (neat, 85% and 75% DME) with 0% and 25% EGR. The fuel sweeps indicated that the ignition delay (ID) increased and burn duration (BD) decreased monotonically when the blend increased to 25% propane/75% DME. The results suggest optimum engine performance with neat DME at 105 kPa boost pressure and 25% EGR with propane addition improving the BTE with negligible increase in emissions. Higher contents of Propane, up to 25%, did not affect the variability of combustion, with standard deviations of burn duration and peak cylinder pressures below 1% for all test cases.

air-handling↗
DOE OSTI2025

Orchestrating Spontaneous Emission With Metasurfaces: Recent Advances in Engineering Thermal, Luminescent, and Quantum Emissions

Metasurfaces have emerged as powerful tools for controlling spontaneous emission, offering unprecedented control over light-matter interactions at sub-wavelength scales. While metasurfaces are traditionally utilized for shaping coherent electromagnetic waves, they have recently extended their capabilities to control incoherent or spontaneous emission. This examines review how metasurfaces can enhance and precisely control properties of thermal, luminescent, and quantum emission. In thermal emission, metasurfaces enable control over spatial, temporal, and spin coherence, offering new possibilities for applications such as energy harvesting, radiative cooling and heat assisted ranging and detection. For luminescent emission, metasurfaces significantly improve emission rates, quantum efficiency, and directionality, driving innovations in lighting and display technologies. For controlling quantized spontaneous emission, metasurfaces are instrumental in enhancing single-photon sources and enabling novel functionalities in quantum states through photon-pair generation, which is vital for quantum communication, meteorology, and computing. Here, despite these advancements several challenges to increase the operational bandwidths, accelerate and develop simulation strategies, and fabrication complexities persist. Emerging trends are also discussed, such as dynamic metasurfaces and their integration with nanophotonic platforms, which could further expand the capabilities of light-emitting metasurfaces.

Luminescence↗
DOE OSTI2024

Material selection and manufacturing for high‐temperature heat exchangers: Review of state‐of‐the‐art development, opportunities, and challenges

Abstract Many energy systems demand heat transfer at high temperatures to keep up with high demand for power, so high‐temperature material that can perform and last under these harsh conditions is needed for heat exchangers. The engineering requirements for these high‐temperature heat exchanger material call for high thermal conductivity, high resistance to fracture, high resistance to creep deformation, environmental stability in environments associated with the application, and high modulus of elasticity while maintaining low cost to make and maintain. Naturally, ceramics are a good solution for this endeavor. In the past, high‐temperature heat exchangers made from ceramics have been used. We provide examples of ceramics in relevant heat exchange applications and provide motivation where additive manufacturing (AM) can improve efficiency. AM for the relevant material is under development, and we provide insight on the AM of ceramic materials and examples of AM heat exchangers keeping cost in mind. The motivation of the review paper is to provide a framework for material and manufacturing selection for high‐temperature heat exchangers for AM to keep up with the demand for better efficiency, better material, better manufacturing, and cost moving forward with AM technology in high‐temperature ceramic heat exchangers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Additive-Induced Morphology Change of Polymer Film Enables Enhanced Charge Mobility and Faster Organic Electrochemical Transistor Switching

Conjugated polymers (CPs) play an important role in organic electrochemical transistors (OECTs) for bioelectronics and related applications, where they serve as channel materials. Currently, most successful polymers for CPs are re-engineered from traditional CPs by replacing hydrophobic alkyl side chains with hydrophilic ethylene glycol or ionic groups. Frustratingly, the enhanced ion transport often compromises the charge mobility of the original CP. In this work, we present an additive-mediated method to construct a modified poly(3-hexylthiophene) (P3HT) film to enable efficient ion migration. The additive is designed with a cleavable diazo group that releases nitrogen and 2-methoxyethanol, a volatile compound, to alter the P3HT film morphology. OECTs based on the film exhibit improved response times. Interestingly, the process also enhances the crystallinity of P3HT, leading to higher hole mobility compared with pristine P3HT. This study proposes an in situ strategy to achieve the functionality of the OMIEC via morphological regulation, offering a promising route to simultaneously enhance both ion accessibility and charge mobility.

Organic polymers↗
DOE OSTI2021

In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery

Abstract We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or “drone” to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.

42 ENGINEERING↗
DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗
DOE OSTI2026

Chloride modification of vacuum-assisted blade-coated perovskite solar cells and mini-modules in ambient environment

Scalable deposition of perovskite thin films under ambient conditions remains a key challenge for the commercialization of perovskite photovoltaics, primarily due to the difficulty of controlling crystallization kinetics and suppressing non-radiative recombination during film formation. In this work, we present an effective strategy to modulate crystallization kinetics and non-radiative recombination in vacuum-quenched blade-coated perovskite films processed in air using chloride-based additives. By systematically comparing PbCl 2 and 3-chloropropylamine hydrochloride (Cl-PACl), we elucidate how different chloride incorporation pathways influence crystallization behavior and optoelectronic properties. In situ transmission and in situ photoluminescence measurements reveal that PbCl 2 acts as a transient additive that slows intermediate/perovskite phase formation while suppressing non-radiative recombination, leading to improved film quality. In contrast, Cl-PACl introduces additional trap states that deteriorate optoelectronic performance. As a result, PbCl 2 -modified perovskite solar cells achieve a champion power conversion efficiency (PCE) of 24.2% and remarkable operational stability, retaining over 90% of their initial performance after 800 hours of continuous illumination. Furthermore, we successfully scaled the process, fabricating a perovskite mini-module that delivered a champion efficiency of 18.0%. This study underscores the critical importance of coupling vacuum-assisted blade coating with transient additive engineering for achieving high-performance, stable, and scalable perovskite photovoltaics processed in ambient air.

14 SOLAR ENERGY↗