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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bilayer Electron Transport Layers for High–Performance Rigid and Flexible Perovskite Solar Cells

While great progress is being made in achieving high power conversion efficiency (PCE), durability, and reliability in rigid and flexible n–i–p perovskite solar cells (PSCs), there is still room for improvement. Among myriad ways this can be achieved, one way is to improve the processing and quality of electron transport layers (ETLs) used in PSCs. To that end, here we explore the use of SnO 2 /TiO 2 bilayer ETLs in both rigid and flexible PSCs. In the case of rigid PSCs, chemical bath deposition (CBD) is used where the bilayer architecture affords the CBD of high-quality ETL, which results in PSCs with up to 25.13% PCE and operational stability T 80 (80% of initial PCE retained) of 2220 h under 1-sun continuous illumination with maximum power-point tracking. In the case of flexible PSCs, once again, the bilayer architecture allows us to fabricate high-quality ETL using spin coating, which results in PSCs with up to 22.54% PCE and excellent mechanical durability, withstanding 20 000 bending cycles with ≈92% of the initial PCE retained. Mechanisms underlying the enhanced performance and stability/durability of rigid and flexible PSCs that use SnO 2 /TiO 2 bilayer ETLs are elucidated. Furthermore, this approach could be extended to other ETL systems for PSCs for further improvements in PCE, durability, and reliability.

14 SOLAR ENERGY↗
DOE OSTI2026

Identifying Differential Equations in Fourier Domain (FourierIdent)

We investigate identifying differential equations in the frequency domain. Fourier analysis is an important tool in theoretical analysis and numerical solvers of differential equations, yet there is limited work in exploring this connection in the identification of differential equations. This paper aims to identify the underlying differential equation in the frequency domain, from a given single realization of the differential equation perturbed by noise. Such setting imposes difficulties which are different from other identification methods where computation is carried out in the physical domain. We propose several ways to mitigate the challenges arising from noise in data and large differences in the magnitudes of frequency responses. The main takeaways are that identifying differential equations solely in the frequency domain is challenging, the method we propose is based on a form of domain partitions in the frequency domain, and this method shows benefits for complex data even with high level of noise. We introduce a Fourier feature denoising, and define the meaningful data region and the core regions of features to reduce the effect of noise in the frequency domain and to enhance the accuracy in coefficient identification. The proposed method is tested on various differential equations with linear, nonlinear, and high-order derivative feature terms, and shows advantages on complex data with many frequency modes, even under high level of noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Consequential improvement acquisition function for efficient multi-fidelity Bayesian optimization

Abstract Surrogate-based Bayesian optimization has been widely applied in design optimization to increase sampling efficiency. However, the cost for each evaluation of the objective function can still be very high when physical experiments or large-scale simulations are involved. Multi-fidelity Bayesian optimization is the new approach to further improve the sampling efficiency by reducing the number of expensive samples at the highest fidelity level and supplementing them with less expensive ones at low-fidelity levels. In this paper, a new consequential improvement (CI) acquisition function is proposed to allow for the simultaneous selection of the solution and the fidelity level in problems with a known hierarchy of fidelity levels. The new CI acquisition function incorporates the consequential effectiveness of objective improvement with the considerations of cost, accuracy, and validity differences between high- and low-fidelity samples in engineering practice. The new method of multi-fidelity Bayesian optimization based on the CI is demonstrated with several analytical and simulation-based design examples. In the simulation-based design optimization example, the results show that the CI acquisition function has a decisive advantage in the sampling efficiency over the other methods of multi-fidelity Bayesian optimization with simultaneous selection. The results indicate that the proposed method is particularly advantageous in solving high-dimensional problems and when large cost ratios between high- and low-fidelity evaluations exist and high-fidelity validation is mandatory. Furthermore, the method robustly avoids the prevalent issue of over sampling at low-fidelity levels.

Aydogdu, Ibrahim [Georgia Institute of Technology,↗
DOE OSTI2025

Quantifying the effects of artificial aging on the ignition and self-propagating reactions of Ni(V)/Al multilayers

Bimetallic, reactive multilayers are uniformly structured materials composed of alternating nanoscale layers that may be ignited to produce self-propagating intermetallic-formation reactions. When reactive multilayers age, there is a change in local composition and loss of stored chemical energy due to mass transport and rearrangement at the interfaces. Here, to quantify the long-term reliability of commercial Ni(V)/Al multilayers, the effects of accelerated aging on both ignition sensitivity and self-propagating reactions have been examined. Thermally aged samples were characterized using transmission electron microscopy, differential scanning calorimetry, and laser ignition combined with high-speed videography. The analytically quantifiable nature of both continuous wave laser ignition and reactive wave propagation enabled calculations of Arrhenius rate constants for as-received and various heat-treated multilayers. With heat treatment, there is a change in the intermixed thickness and interfacial chemistry that decreases the activation energy for point ignition but increases it for self-propagating reactions. This finding implies that with increased thermal aging, the reaction becomes easier to facilitate in the solid state but harder in the liquid phase.

Energetic material↗
DOE OSTI2025

Optimizing the design and operation of water networks: Two decomposition approaches

We consider the design and operation of water networks simultaneously. Water network problems can be divided into two categories: the design problem and the operation problem. The design problem involves determining the appropriate pipe sizing and placements of pump stations, while the operation problem involves scheduling pump stations over multiple time periods to account for changes in supply and demand. Our focus is on networks that involve water co-produced with oil and gas. While solving the optimization formulation for such networks, we found that obtaining a primal (feasible) solution is more challenging than obtaining dual bounds using off-the-shelf mixed-integer nonlinear programming solvers. Therefore, we propose two methods to obtain good primal solutions. One method involves a decomposition framework that utilizes a convex reformulation, while the other is based on time decomposition. To test our proposed methods, we conduct computational experiments on a network derived from the PARETO case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Surrogate modeling and optimization of the leaching process in a rare earth elements recovery plant

Critical minerals (CMs) and Rare Earth Elements (REEs) play a vital role in crucial infrastructure technologies such as renewable energy generation and batteries. Recovering them from waste materials has recently been found to significantly reduce environmental impact and supply chain costs related to these materials. In this work, we investigate surrogate modeling techniques aimed to simplify the modeling, simulation, and optimization of the leaching processes involved in CM and REE recovery flowsheets. As there is currently a lack of systematic studies on this topic, we perform extensive computational testing to ascertain which surrogate models are easier to construct and offer high predictive accuracy. Further, our results suggest that sparse quadratic models balance predictive accuracy and computational efficiency. Training and using these surrogates for global optimization of the leaching process requires two orders of magnitude fewer measurements and is up to four orders of magnitude faster than optimizing the original simulation using equation-oriented optimization or derivative-free optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Co-training of multiple neural networks for simultaneous optimization and training of physics-informed neural networks for composite curing

This paper introduces a Physics-Informed Neural Network (PINN) technique that co-trains neural networks (NNs) that represent each function in a system of equations to simultaneously solve equations representing an out-of-autoclave (OOA) cure process while conducting optimization in adherence to process requirements. Specifically, this co-training approach benefits from using NNs to represent OOA inputs (air temperature profile) and outputs (part and tool temperature profiles and degree of cure). Production requirements can then be levied on the inputs, such as maximum air temperature and minimum cure cycle, and simultaneously on the outputs, such as degree of cure, maximum part temperature, and part temperature rate limits. The technique is validated with finite element (FE) simulations and physical experiments for curing a Toray T830H-6 K/3900-2D composite panel. Furthermore, this novel approach efficiently models and optimizes the OOA cure process.

Composite curing↗
DOE OSTI2025

Ensemble cure kinetics network (ECK-Net): A method to derive cure kinetics of thermosetting resin

This paper introduces an Ensemble Cure Kinetics Network (ECK-Net), a neural network (NN)–based framework for modeling the cure kinetics of thermosetting resins within a phenomenological context. ECK-Net replaces traditional analytic models, which require extensive chemical insight and multiple isothermal/non-isothermal experiments, with a data-driven surrogate that maps nonlinear relationships between temperature, degree of cure, and reaction rate from differential scanning calorimetry data. The proposed approach predicts input-dependent kinetic coefficients of a generalized nth-order reaction equation rather than reaction rates directly, enabling a single unified model to represent various epoxy systems without relying on iso-conversional analysis or predefined functional forms. To ensure robustness, multiple independently trained networks under different random initializations are blended through an ensemble strategy, effectively mitigating the stochastic variability inherent to neural networks. The framework is validated using experimental datasets from multiple resin systems, including aerospace-grade materials (Toray 3900-2, Cycom 5320-1, and Hexcel 8552) and a windmill-grade resin (RIMR 035c). The model accurately reproduces the temporal evolution of the degree of cure under manufacturers’ recommended cure cycles across all tested resins systems, yielding Pearson’s correlation coefficients of 0.992, 0.994, 0.993, 0.997, respectively. To demonstrate process-level applicability, the trained network was implemented within the Abaqus environment to simulate out-of-autoclave (OOA) curing process of the CFRP panel composed of Toray T830H-6K/3900-2D prepreg. The simulation results showed excellent agreement with experimental temperature response (maximum peak temperature, simulation: 189.6 °C, experiment: 188.5 °C) and the final degree of cure (simulation: 0.948, experiment: 0.960 ± 0.013), confirming ECK-Net’s capability as a reliable alternative to conventional cure kinetics modeling methods.

Composite curing↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2024

Reproducible emission from nonlinear random lasers

Multiple scattering of light serves as a mechanism for feedback in random lasers. Consequently, internal spatial mode patterns, lasing wavelengths, and output directionality can all be random. Strong mode interaction can occur in such devices due to spatially overlapping modes resulting in nonlinearity with respect to the pump input power. Nevertheless, temporal coherence and lasing mode amplitude can be fixed at a constant pumping rate. This is a property desirable for applications where unique randomness is exploited but expected to be reliable over time, such as physical unclonable functions. Random lasers can also be cheaply and easily fabricated, exhibit relatively low lasing thresholds and high emission intensity. However, the precise scattering properties of such structures and fluctuations in the pump field can make device emission irreproducible, thereby limiting random laser applications. Here, in this work, we directly compare the random lasing spectra from zinc oxide samples fabricated in four distinct ways: spin-coating, sputtering, solgel deposition, and atomic layer deposition. The particular method of fabrication has a strong impact. Samples made through atomic layer deposition here exhibit both reproducibility and strong nonlinearity desirable for applications. Randomness in emission spectra persists across hundreds of repeated and averaged measurements irrespective of spatial location and is demonstrably nonlinear with respect to input signal intensity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Morphotype-resolved characterization of microalgal communities in a nutrient recovery process with ARTiMiS flow imaging microscopy

Microalgae-driven nutrient recovery represents a promising technology for phosphorus removal from wastewater while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based dense neural network (DNN) models. Across a two-year study period, Scenedesmus spp. appeared most important for phosphorus removal, and were negatively impacted by elevated temperatures and increase in nitrite/nitrate concentrations. Chlorella and Monoraphidium also played an important role in phosphorus removal. For both Scenedesmus and Chlorella, smaller morphological types were more often associated with better system performance, whereas larger morphotypes likely associated with stress response(s) correlated with poor phosphorus recovery rates. Furthermore, these results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Helium Incorporation into Scandium Fluoride, a Model Negative Thermal Expansion Material

Scandium trifluoride is a model negative thermal expansion (NTE) material. Its simple structure can be described as an A-site vacant perovskite, and it shows isotropic NTE over a very wide temperature range (up to ~1100 K), due to transverse vibrational motion of the fluoride. Like many framework NTE materials, it undergoes a phase transition at low pressures, adopting a rhombohedral (R3̅c) structure at >0.7 GPa and 300 K in commonly used nonpenetrating pressure media, such as silicone oil. High pressure X-ray diffraction data and gas uptake/release measurements indicate that, on compression in helium above ~200 K, helium is inserted into ScF 3 to form the defect perovskite He x ScF 3 . The incorporation of helium stiffens the structure and changes its phase behavior. At room temperature, complete filling of the structure with helium does not occur until >1.5 GPa. On compression, a cubic perovskite structure is maintained until ~5 GPa. As the pressure was increased to ~9.5 GPa, a further transition occurred at ~7 GPa. The first transition at ~5 GPa is likely to a tetragonal (P4/mbm) perovskite, but the detailed structure of the perovskite phase formed on compression above ~7 GPa is unclear. Cooling down from 300 to 100 K in helium at ~0.4 GPa leads to an approximate composition of He 0.1 ScF 3 . High pressure neutron diffraction measurements, in the temperature range 15–150 K show that the incorporation of helium increases the pressure at which the cubic (Pm3̅m) to rhombohedral (R3̅c) putative quantum structural phase transition occurs from close to 0 GPa to ~0.2 GPa at 0 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Non-Covalent Interactions and Helical Packing in Thiophene-Phenylene Copolymers: Tuning Solid-State Ordering and Charge Transport for Organic Field-Effect Transistors

In this study, we introduce two thiophene-phenylene-thiophene (TPT) polymers designed to leverage noncovalent intramolecular interactions to regulate main-chain conformation and enhance solid-state ordering. By incorporating unsubstituted thiophene (T) or bithiophene (2T) units, we reveal striking divergence in the thermal, morphological, and optoelectronic properties of the resulting films, facilitated by these noncovalent interactions. Using a combination of computational and experimental approaches, we show that annealing yields remarkably different polymer conformations and, consequently, charge transport properties. TPT-T undergoes a significant structural transformation, adopting a more planar backbone conformation and a highly crystalline, edge-on molecular orientation. In contrast, the introduction of a single additional thiophene unit in TPT-2T leads to a more isotropic molecular orientation with a slight preference for face-on alignment, resulting in a heterogeneous film structure that hinders charge transport despite achieving tighter molecular packing. Remarkably, despite being composed of achiral components, TPT-2T develops chirality upon annealing, indicating the formation of a helical conformation. Organic field-effect transistor measurements reveal that the well-ordered alignment in annealed TPT-T films results in higher charge carrier mobility and a narrower distribution of mobility values than in TPT-2T. These findings provide critical insights into the structure−property relationships of conjugated polymers, offering guidance for optimizing molecular design and processing strategies for highperformance organic electronic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Impact of Cation Insertion on Semiconducting Polymer Thin Films toward Electrochemical Energy Conversion

Semiconducting polymers are being explored for electrochemical and photoelectrochemical energy transformation and storage applications. For these applications, it is critical to understand how ion insertion from the electrolyte into polymer electrodes modulates the polymer electronic structure and electron doping levels. Here, this study explores electrochemical cation insertion in the n-type conjugated redox polymer P90, composed of alternating naphthalene diimide (NDI) acceptor and bithiophene (T2) donor units, where the NDI units are functionalized with heptaethylene glycol (HEG, 90%) and 2-octyl dodecyl (OD, 10%) side chains. By combining in situ techniques (UV-vis absorption and Raman spectroscopies with electrochemistry), structural analysis using ex situ grazing-incidence wide-angle X-ray scattering (GIWAXS), and density functional theory (DFT) calculations, we reveal that dications enable negative polaron and bipolaron formation in the P90 at less reducing potentials while supporting more bipolaron formation than the monocations; moreover, larger dications with smaller hydrated radii increase the maximum P90 electron doping level. We also determine that the monocations lead to more thermodynamically stabilized polarons compared with the dications. These findings highlight the critical role of cation identity in tuning electrochemical charging, charge stabilization, and electronic structure of n-type conjugated redox polymers, providing guidance on the rational design of polymer-based (photo)electrochemical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Influence of Transition Metal Ion Contaminants on the Performance of Amine-Based Solid Sorbents in Direct Air Capture

Amine-functionalized solid sorbents are a class of sorbent materials proposed for direct air capture (DAC) of CO 2 , yet their long-term performance is susceptible to degradation under realistic operating conditions. Many amines are not thermodynamically stable in air, and amine sorbents oxidize while in use during DAC temperature swing adsorption processes. In this study, we investigate the role of transition metal ion contaminants, specifically Cu 2+ , Fe 2+ , and Ni 2+ , on the oxidative degradation of poly(ethylenimine) (PEI)-impregnated SBA-15 sorbents. By introducing metal ions via different modes mimicking both synthesis-related impurities and impurities derived from environmental exposure, we systematically evaluate sorbent stability after exposure to dry air at an elevated temperature. Thermogravimetric CO 2 uptake measurements reveal that even trace levels of Cu and Fe (as low as ∼4 ppm) can lead to measurable sorbent deactivation after oxidative aging, despite negligible loss in the performance of the control samples. In situ infrared, UV–vis, and X-ray photoelectron spectroscopies indicate that these metals catalyze radical-driven oxidation pathways, altering the chemical structure of the sorbent and accelerating degradation. Our findings underscore the need to account for trace metal contamination during DAC sorbent synthesis and deployment and highlight the importance of environmental contamination pathways.

CO2 capture↗
DOE OSTI2025

Structurally Driven Selective Adsorption of Hydrocarbons by Metal Substitution in Isostructural Rare-Earth Metal–Organic Frameworks

The design and realization of highly selective nanoporous materials are necessary to target critical separations across industries. By leveraging pore size, pore shape, and linker functionalization, the design of nanoporous solid adsorbents will enable the rapid production of energy efficient separation materials for high-value gas mixtures. This study uses a combination of modeling, synthesis, and gas adsorption testing to investigate a new class of small-pore isostructural rare-earth (RE) 2,5-dihydroxyterephthalic acid (DOBDC) metal–organic frameworks (MOFs) (RE: Pr-, Gd-, Er-, Yb; DOBDC = 2,5-dihydroxyterephthalic acid) and their adsorption selectivity for acetylene/ethylene mixtures. Density functional theory simulations identified that selective binding of acetylene over ethylene in the Gd-, Er-, and Yb-DOBDC MOFs was due to hydrogen-bonding between acetylene and the linker hydroxyl. Adsorption experiments validated the computational results by identifying mechanisms that control the acetylene/ethylene adsorption selectivity and high acetylene adsorption. Furthermore, dynamic column breakthrough experiments with the Gd-DOBDC MOF validated the simulations and indicated that ethylene can be separated from acetylene in a mixture containing 1 vol % acetylene and 39 vol % ethylene (balance argon). In conclusion, the results highlight the complexity of gas binding in functional porous materials and how combining modeling and experiment enables a fundamental understanding of gas–framework interactions that can be leveraged for the design of future separation materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Pervaporative Dehydration of 2,3-Butanediol by Dense Poly(vinylidene fluoride) Hollow Fiber Membranes: Parameter Estimation, Process Design, and Technoeconomic Evaluation under Uncertainty

Pervaporation, combined with other separation processes, can effectively remove water from fermentation product streams, making it highly suitable for purifying alcohols like 2,3-butanediol (BDO). In this study, a dense poly(vinylidene fluoride) (PVDF) hollow fiber membrane module prototype was fabricated for BDO dehydration, achieving >0.2 LMH total flux and >95% BDO rejection. With a Markov chain Monte Carlo (MCMC) approach, Bayesian inference was used to quantify the uncertainty of the permeance parameters. A membrane cascade model was developed to scale up a process that purifies a preconcentrated BDO feed (70 wt %) to high purity (90 wt %). Through propagation of the uncertainty of the parameters and sensitivity analyses of the process variables, a cascade design was recommended. Despite data and model limitations, the framework enabled a reliable system analysis and economic evaluation, validated through tight confidence intervals in key process metrics, establishing the foundation for future applications of Bayesian methods in membrane-based processes.

Animal feed↗
DOE OSTI2026

A Guide to Nonaqueous Electrochemistry of f-Element Complexes

Electrochemistry is a powerful tool for assessing and understanding the redox chemistry of molecular complexes. Cyclic voltammetry enables the f-element community to study molecules in unusually high or low oxidation states, which potentially pose important broad-scope questions of electronic structure. In the pursuit of boundary-pushing compounds, reactive air- and moisture-sensitive species are often encountered, which can be challenging to characterize, especially when they are chemically incompatible with certain solvents, electrolytes, or electrodes or when their potentials lie outside of common electrochemical windows. Nonaqueous solvents and pseudo reference electrodes complicate many of the standard practices in acquiring high-quality and reproducible electrochemical data. This guide presents a detailed discussion of selecting appropriate cell conditions and referencing and addresses metrics for evaluating electrochemical and chemical reversibility. These methodological approaches have been extended to best practices for the electrochemical analysis of radioactive transuranic complexes.

Electrodes↗