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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dual aggregation steering in bulk-heterojunction via solvent engineering toward efficient and stable binary organic solar cells

In high-performance organic solar cells (OSCs), efficient charge transport hinges on a well-optimized morphology of the photoactive layer, which depends critically on controlled aggregation and favorable interactions between donor and acceptor materials. In this work, we introduce a cascade solvent system comprising high-boiling-point ethylbenzene (EB) and low-boiling-point chloroform (CF) to finely tune the aggregation behavior of the D18 donor and L8-BO acceptor. The incorporation of EB not only promotes the H-aggregation of D18 and the J-aggregation of L8-BO but also facilitates the formation of ideal nanoscale phase separation, thereby suppressing bimolecular recombination. As a result, devices processed with the EB/CF solvent blend achieve a best power conversion efficiency (PCE) of 19.6 % and enhanced operational stability, outperforming those fabricated with pure CF (17.1 %). In conclusion, this study offers a reliable and effective strategy for optimizing donor and acceptor aggregation, providing a viable pathway toward higher-performance OSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Elucidating interfacial active sites in ruthenium–boron nitride nanotube catalysts for efficient low-temperature ammonia-to-hydrogen conversion

Tailoring the interaction between metal nanoparticles and catalyst support presents a prominent strategy to enhance both the activity and durability in hydrogen (H 2 ) production catalysts. In this work, ruthenium nanoparticles (NPs) supported on boron nitride nanotubes (Ru/BNNT) are introduced as efficient and thermally robust catalysts for low-temperature ammonia (NH 3 ) decomposition. The unique curvature and ionic nature of BNNTs enable uniform Ru dispersion and metal-support interactions (MSIs), resulting in exceptional H 2 generation efficiency and long-term operational stability. In-situ transmission electron microscopy (TEM) reveals remarkable thermal resistance of Ru/BNNT with minimal nanoparticle sintering, while density functional theory (DFT) calculations uncover a dual-site mechanism in which interfacial Ru atoms promote NH 3 dissociation and adjacent Ru sites facilitate 2H* recombination and H 2 desorption. This cooperative interaction between metal NPs and the BNNT support underpins the outstanding catalytic performance and durability observed. In conclusion, the findings highlight the strategic potential of BNNTs as versatile supports for high-performance and stable catalysts in sustainable H 2 energy conversion and related catalytic processes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A de novo sacrificial-MOF strategy to construct enhanced-flux nanofiltration membranes for efficient dye removal

Nanofiltration membanes play a critical role in alleviating global water scarcity, affecting more than 4 billion people. Here, a new metal organic framework (MOF) sacrificicial strategy is designed to synthesize flux-enhancing nanofiltration membrane for efficient dye removal. With the aid of polyphenol chemistry, ZIF-67 MOF was first coated onto a porous support and in situ transformed into a selective layer by virtue of pyrrole polymerization. Trigerred by ammonium persulfate, polypyrrole selective layer functioalized nanofiltration membrane was fabricated when accompanying pore regulation by MOF self-sacrifice. The membrane demonstrated a flux enhancement of 745% than pristine polypyrrole (PPy) one with maintained rejection. Furthermore, the synergistic interactions among polypyrrole, polyphenol, and porous support endowed membrane with excellent stability during long term test. The designed MOF sacrificed route may open a new avenue for fabricating advanced energetic-efficient membranes with the reconstructed architecture towards diverse separation applications.

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

An efficient high-order numerical solver for diffusion equations with strong anisotropy

In this paper, we present an interior penalty discontinuous Galerkin finite element scheme for solving diffusion problems with strong anisotropy arising in magnetized plasmas for fusion applications. Additionally, we demonstrate the accuracy produced by the high-order scheme and develop an efficient preconditioning technique to solve the corresponding linear system, which is robust to the mesh size and anisotropy of the problem. Several numerical tests are provided to validate the accuracy and efficiency of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Gapped-filtering for efficient Chebyshev expansion of the density projection operator

In this paper, we develop the gapped-filtering method, whereby a short Chebyshev expansion accurately represents the density-matrix operator. The method optimizes the Chebyshev coefficients to give the correct density matrix at all energies except within the gapped region where there are no eigenstates. Gapped filtering reduces the number of required terms in the Chebyshev expansion compared to traditional expansion methods, as long as one knows or can determine efficiently the HOMO and LUMO positions. The reduction is especially noticeable (factors of 2-3) when high accuracy is sought. To exemplify the method, we use gapped-filtering to increase the efficiency of stochastic-GW calculations.

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

Molecular packing modulation enabling optimized blend morphology and efficient all small molecule organic solar cells

The molecular planarity of a small molecule donor is one of the key factors affecting its molecular packing behavior, the morphology of the related active blend and thus the photovoltaic properties of all small molecule organic solar cells (ASM-OSCs). Here, a series of new benzodithiophene (BDT)-based A-π-D-π-A type small molecules were developed, namely SD1, SD2 and SD3, by combining the chemical modification of side chain (sulfuration), π-bridge (alkoxy groups introducing) and end group (different type or different alkyl chain). The sulfuration of the BDT side chains and introducing alkoxy group to the π-bridge could induce intermolecular S … S and intramolecular S … O non-covalent interactions for higher planarity of these small molecules. The rhodanine and cyanoacetate end groups showed less effect on the optical and electronic properties but significantly affected the molecular packing behavior and blend morphology. From SD1:Y6-T to SD2:Y6-T and SD3:Y6-T, the face-to-face packing was gradually reduced, while the face-to-edge packing was increased, which resulted in lowered exciton dissociation efficiency, further decreased the device performance. SD1:Y6-T blend exhibited more favorable face-to-face packing and ideal morphology for efficient charge dissociation and transport, contributing to a champion PCE of 10.12%. This work provided a deep insight of the donor and acceptor packing behavior effect on the photovoltaic property by molecular design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Efficient Screening of Bi–Metallic Electrocatalysts for Glycerol Valorization

Glycerol is a byproduct of biodiesel production and, as such, it is of limited economic value. By means of electrooxidation, glycerol can be used as a feedstock for scalable hydrogen production, in addition to conversion to value-added products. The development of novel and efficient catalytic electrode materials for the anodic side of the reaction is a key towards a hydrogen-based energy economy. In the present study, a computational screening protocol combining DFT, scaling relations, and microkinetic modeling allows for a rational selection of novel catalysts that can deliver efficient glycerol electrooxidation, low cost of production, and environmental sustainability. Activity and chemical selectivity towards hydrogen production on pure metal catalysts is discussed in terms of volcano-shaped plots. We find that the selectivity in the glycerol oxidation reaction is influenced by a different energy landscape when in the presence of water and best classified by a comparison of O—H and C—H bond-breaking barriers. In addition, we screened 3570 bi-metallic catalysts in the AB (L1 0 ) and A 3 B (L1 2 ) ordered structures for activity, stability, price, and toxicity. By filtering based on the criteria for toxicity, resistance to oxidation, miscibility, and price, we have identified 5 L1 0 structured catalysts (AgPd, AuPd, PtSb, CuPt, and AgPt) and 20 L1 2 catalysts (Ga 3 Ta, In 3 Ta, Ir 3 W, Ir 3 Mo, Cu 3 Pt, Ir 3 Ta, Ir 3 Re, Pd 3 Bi, Pd 3 Cu, Pd 3 W, Pd 3 Co, Pd 3 Sn, Pd 3 Mo, Pd 3 Ag, Pd 3 Ga, Pd 3 Ta, Au 3 Ru, Pd 3 In, Au 3 Ir, and Pd 3 Au) that are all predicted to show high activity. We also identify an additional 37 L1 0 and 92 L1 2 structured electrocatalysts with an anticipated medium-high activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modified alkaline peroxide pretreatment: An efficient path forward for bioethanol production from bamboo

To overcome the typical delignification saturation point of alkaline peroxide pretreatment and further facilitate lignin removal, a novel modified alkaline hydrogen peroxide pretreatment (MAHP) was proposed by introducing ethanol into the reaction system. The dosages of H 2 O 2 , ethanol, and pretreatment temperature were optimized, and the results revealed that a maximum lignin removal as high as 79.25% could be achieved at only 100 °C, 3 wt% H 2 O 2 concentration and 1 wt% ethanol concentration. Meanwhile, 76.5% of glucan and 56.0% of xylan were preserved at this pretreatment condition. By overcoming the delignification saturation point, enzymatic hydrolysis efficiency was remarkably enhanced, achieving 96.76% and 97.38% of glucan and xylan conversion, respectively, which are 7.4 and 11.4 times as compared to that of the untreated bamboo. Furthermore, the simultaneous saccharification and fermentation (SSF) result indicated an identical ethanol yield of ~75% when elevating the SSF solid loading from 5% to 30%. Based on the sequential SSF and xylose fermentation results, about 5.6 tons of bamboo would be consumed to produce 1 ton of ethanol. Finally, the energy balance revealed that a positive balance of 1255.4 KJ could be generated via processing 1 kg bamboo. Here, the results demonstrate that the MAHP is a promising high-efficiency pretreatment technology for bamboo due to the mild pretreatment severity and robust ethanol yield.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Experiments on a vapor compression air conditioner with liquid desiccants for efficient dehumidification

Buildings require air conditioning systems that not only cool and dehumidify supply air but also provide sufficient ventilation to ensure indoor air quality and occupant comfort. However, standard recirculation systems-which introduce about a 10 % to 20 % fraction of outdoor air-often fail to deliver air that is precisely cooled and dry, particularly because 80-90 % of the ventilation cooling load is latent. Mixing humid ventilation air with recirculated indoor air increases the energy and costs required to condition the air to comfortable levels. Dedicated outdoor air systems (DOASs) are designed to handle this latent dominated ventilation load and thus need to have efficient humidity removal. Many cooling cycles can perform this task. Here we describe a liquid desiccant DOAS, which combines a vapor compression cycle and a liquid desiccant absorber and desorber pair. We present its performance at 26 operating conditions and a thermodynamic model which can accurately predict the moisture removal efficiency. The model's performance predictions have a mean percentage error of 2.5 % and a coefficient of variation of the root mean square error of 7.5 %. We also compare the performance of this vapor-compression-coupled liquid desiccant system with a standard vapor compression system with the same components but no liquid desiccant. For the 26 conditions tested in this study, this comparison shows that adding liquid desiccants lowers the required evaporator cooling load by 21 %, allows for 25 % lower compressor volumetric capacity, and 25 % lower electricity use. Future work will leverage this model to quantify the reduction in annual electricity use across different climates, including the need for a standard vapor compression system to reheat the air during some of the year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Enhanced energy conversion efficiency promoted by cavitation in gasoline direct injection

High-pressure direct fuel injection plays the most crucial role in energy conversion and improving engine combustion efficiency and emission. The optimization of turbulent and multiphase fuel injection has focused on controlling hydrodynamic parameters such as injection pressure. While the thermodynamic influence is often considered in the flash boiling situation, we inquire into how gasoline-type fuel’s hydro- and thermodynamic properties impact the injection dynamics by fuel-temperature induced cavitation. The turbulent and cavitating flows emanating from the direct-injection nozzle is visualized by ultrafast x-ray imaging with an unprecedented spatiotemporal resolution. The ultrafast liquid-fuel dynamics are dominated by injection pressure as well as fuel temperature through cavitation, an important thermodynamic parameter but often difficult to control in engine combustion. With the most direct and quantitative measurement, we discovered that the near-nozzle fuel-jet dynamics can be perfectly scaled by a single dimensionless parameter, cavitation number, particularly sensitive to the fuel temperature, in a wide operation range. This universal scaling shows that cavitation can be harnessed to elevate the pneumatic-hydraulic to kinetic energy conversion efficiency, critical for promoting fuel atomization and engine combustion performance. This enhancement effect will have even more impact on engine combustion using alternative low-emission fuels with higher saturated vapor pressure.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Retrofitting stoves with forced jets of primary air improves speed, emissions, and efficiency: Evidence from six types of biomass cookstoves

Incorporating jets of forced air into biomass cookstove combustion has been shown to potentially decrease harmful emissions, leading to a variety of designs in recent years. These have shown mixed success in terms of real-world performance, usability, and durability. The Jet-Flame forced draft retrofit accessory was recently developed to implement forced jets of primary air at a low cost into a wide range of types of cookstoves using a small 1.5-W fan housed in a low-cost cast iron body to be inserted beneath the fuel bed of a biomass cooking fire. This research sought to quantify the potential efficiency and emissions performance impacts of the Jet-Flame when installed in six different types of biomass cookstoves (three open or shielded fires and three rocket stoves) versus the natural draft performance of each. The effect of the operating fan voltage was also measured. A series of tests following a modified ISO 19867-1:2018 protocol were performed in the laboratory using the Aprovecho Laboratory Emissions Measurement System (LEMS) equipped with additional oxygen and temperature sensors. Results for each stove carefully tended with a single layer of sticks showed that the global average PM 2.5 reduction with the Jet-Flame was 89 % relative to the natural draft cases, with larger relative improvements seen in the most rudimentary stoves. CO was reduced by a global average of 74 %, reaching tier 4 or 5 for all stoves. Thermal efficiency was also improved by 34 % when calculated without taking into account the energy content of the remaining char (or 21 % with char), illustrating the value of burning char to provide cooking energy rather than leaving it unburned in the combustion chamber as is common in many natural draft stoves. Time to boil was also reduced by 8 %. In addition, adjusting the voltage of the jet-flame assisted in modulating firepower, improving the usability of the stove. These results indicate a strong potential for the Jet-Flame to help reduce emissions and fuel consumption in a wide range of cookstove designs at a relatively low cost or need for changes in behavior, fuel, or cooking device. The unit also provides enhanced usability in terms of ease of startup, cooking speed, low PM emissions at high power, and burning char. Additional studies are needed to measure performance in the field and under a variety of operational conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Favorable morphology and electronic conductivity of functional sublayers for highly efficient water splitting electrodes

Low electronic conductivities and improper morphologies of anode electrodes greatly limit the reaction area, catalyst utilization and efficiency in proton exchange membrane water electrolyzers. In this study, conductive sublayers with different conductivities and morphologies were introduced into anode electrodes in membrane-based water electrolyzers. In-situ and ex-situ investigation results showed that conductive sublayers (Au mesh and carbon nanotube (CNT) film) augmented the sheet conductivity of anode electrodes by up to 4000 times (from 2000 to 0.5 ohm square -1 ), and the ohmic resistance of water electrolyzers was reduced to 1/3 when inserting conductive sublayers. In addition, CNT film provided a higher electrochemical active area than Au mesh, because of favorable morphologies (large porosity and surface area) of CNT fibers on CNT films. Therefore, the current density of water splitting was increased by 3 times (from 4.55 mA cm -2 to 14.83 mA cm -2 ) at 2.5 V compared to a conventional anode electrode. Visualizations on bubble dynamics showed improved performances with conductive sublayers; this was mainly due to greatly increased number of reaction sites, highly spread reaction area (from 50 to 1000 um), and reduced activation overpotential. Therefore, a balance between high electronic conductivity and nanoporous morphology is essential to the anode electrode for larger reaction sites and areas in highly efficient water electrolyzers.

25 ENERGY STORAGE↗
DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗
DOE OSTI2021

Efficient kinetic thermal inverse modeling for organic material decomposition

The prevalent use of organic materials in manufacturing is a fire safety concern, and motivates the need for predictive thermal decomposition models. A critical component of predictive modeling is numerical inference of kinetic parameters from bench scale data. Currently, an active area of computational pyrolysis research focuses on identifying efficient, robust methods for optimization. This paper demonstrates that kinetic parameter calibration problems can successfully be solved using classical gradient-based optimization. We explore calibration examples that exhibit characteristics of concern: high nonlinearity, high dimensionality, complicated schemes, overlapping reactions, noisy data, and poor initial guesses. The examples demonstrate that a simple, non-invasive change to the problem formulation can simultaneously avoid local minima, avoid computation of derivative matrices, achieve a computational efficiency speedup of 10x, and make optimization robust to perturbations of parameter components. Techniques from the mathematical optimization and inverse problem communities are employed. By re-examining gradient-based algorithms, we highlight opportunities to develop kinetic parameter calibration methods that should outperform current methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Efficient and robust stability analysis in the internal energy, volume, and moles (UVN) space

Hydrodynamic simulation of fluids in vessels is often based on the internal energy (U), volume (V), and the number of moles of all components ( re N = N 1 , N 2 , ..., N n ) as the working variables to determine the thermodynamic state by finding the global maximum of entropy. The procedure of finding the equilibrium state may be divided into two steps: stability analysis and phase-split computations. Stability analysis is performed first, and the phase-split computation proceeds if the system is unstable. Most of the past work on UVN space has been based on phase-split computations, while the stability analysis is barely investigated. Furthermore, previous studies for the stability analysis use only Newton's method to solve the non-linear algebraic equations. We present an efficient and robust approach for stability analysis where UVN is specified. The successive substitution iteration (SSI) is used to provide good initial guesses for Newton's method. The proposed approach results in a reduced number of unknowns and does not require a large number of iterations to achieve convergence in Newton's method. Overall, our proposed formulation is compatible with different equations of state and is applicable to both pure component and multi-components. The robustness and efficiency of the algorithm for stability analysis are demonstrated in various examples.

02 PETROLEUM↗
DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗