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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Stochastic reconstruction and microstructure modeling of SMC chopped fiber composites

Here, to establish an integrated Processing-Microstructure-Property workflow for the prediction of material behaviors, this paper presents a new stochastic pseudo-3D microstructure reconstruction method for Sheet Molding Compounds (SMC) chopped fiber composites. The proposed method captures the bi-level microstructural features of SMC composites. At the higher level, a Voronoi diagram-based algorithm is developed to reconstruct the unique substructure features of SMC fiber tows. The geometry of Voronoi cells is adjusted by a Simulated Annealing (SA) algorithm to match the geometrical statistics of the real fiber tows. At the lower level, the algorithm assigns fiber orientation to each Voronoi cell (which represents a fiber tow). The fiber orientation angles are recovered from a statistical fiber orientation tensor. The proposed method is employed to establish a multi-layer pseudo-3D SMC Representative Volume Element (RVE) model for Finite Element Analysis (FEA) of SMC microstructure. This model enables the prediction of mechanical properties based on the material processing information (e.g. fiber orientation tensor obtained from compression molding simulation), and the microstructure information obtained from microscopic imaging for an SMC composite. Lastly, the predicted properties are successfully validated by experimental tensile tests.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Crop models: integrating systems from the molecular to global for agricultural productivity and sustainability

Mathematical models that simulate crop growth in response to environmental conditions and management practices are essential tools for exploring agriculture-based strategies to address food security and environmental sustainability challenges. Early applications of crop models focused on supporting farmers in making management decisions. Applications have since expanded to estimating future impacts on local and global food production from changing climates. Emerging applications of crop models aim to leverage how these models integrate plant processes across biological scales to identify engineering or breeding strategies that account for environmentally-responsive dynamics at field scales and for exploring solutions to improve sustainability. In this review, we highlight recent studies across these four broad application areas and highlight potential future directions for the crop modeling field.

Piao, Ximin [Univ. of Illinois at Urbana-Champaign↗
DOE OSTI2024

Fabrication of yeast engineered porous 13X adsorbent layers for CO 2 capture

This study investigates a novel environmentally friendly coating technique using yeast fermentation to obtain super porous Zeolite 13X adsorbent layers through freeze-drying technique for CO 2 capture applications. Several mass ratios of sugar to yeast were used to control the porosity and pore sizes in the adsorbent layer. A unique peeling process was integrated to obtain foamy, hollow structures to have deeper layer accessibility of adsorbent for adsorption applications. These coated adsorbent layers were experimentally characterized as a function of the mass ratio of sugar to yeast and adsorbent solid fraction. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared spectroscopy (FT-IR) are used to analyze the coated layers. Here, the void fraction for coated samples is found to vary between 0.48 and 0.56, making the samples applicable for adsorption applications. Adsorption performance experiments are performed to test the coated samples' performance compared to 13X powder. Due to their simplistic fabrication, cost-effectiveness, rapid pore formation, and high adsorption capacity, these porous 13X layers hold potential for large-scale fabrication and CO 2 capture applications.

Adsorbent coating↗
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 OSTI2024

Controlling microstructure and B2 ordering kinetics in Fe–Al system through additive manufacturing

Multi-material fabrication between steel and aluminum is challenging because of the formation of several intermetallic phases. Embrittling B2 ordered intermetallics form in the steel rich side and are stable till about 60% of Al dilution in steel. In this work by using multi-length scale characterization coupled with integrated computational process and thermokinetic modeling, we show that the ordered B2 intermetallics in the steel rich side of the Fe–Al system forms via a nucleation and growth mechanism. The extent of B2 ordered intermetallics can be controlled by modifying the directed energy deposition-additive manufacturing (DED-AM) process parameters. Our findings lay the foundation for enabling fabrication of crack-free functionally graded compositions between the two alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications

Here, this paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — Sampler, MultiApp, Reporter, and Surrogate — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Barriers to adopting artificial intelligence and machine learning technologies in nuclear power

Artificial intelligence and machine learning (AI/ML) technologies offer unique opportunities to transform nuclear plant operations and power generation. Benefits will be felt not only within existing analog and digital instrumentation and control, but also within work processes, the integration of people with technology and most importantly, the business case. The application of this new technology can help simplify complex problems and produce more effective decision-making, making nuclear power safer, more efficient, and more economically viable in the current energy market. Nonetheless, there are potential barriers to its adoption that must be overcome. The purpose of this paper is to categorize, review, and discuss barriers to AI/ML adoption within the nuclear power industry, with a focus on existing commercial reactors. Unique considerations for advanced reactors are also offered. Here we provide a comprehensive overview of the historical, technical, and business barriers that the industry faces, as well as stakeholder readiness, and end-user acceptance. We underscore the importance of user experience and offer potential solutions in overcoming each barrier. These include provisions for easier plant data access, a friendly regulatory environment, and investment in user trust and explainable AI.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Noble-Metal-Free, Nickel-Based Dual Functional Materials for Improved Methane Production from In Situ Carbon Dioxide Capture and Conversion

Promoters for dual functional materials have not been well explored, but promoters could improve the efficiency of the process by improving the selectivity of the CO 2 methanation process. Utilizing integrated capture and conversion, where CO 2 is captured and converted to useful products, would allow for a useful avenue to control CO 2 emissions. One such way to accomplish this would be to utilize materials that can both capture and convert CO 2 to useful products. However, these materials are often based on costly noble metals, like ruthenium and platinum, decreasing their viability on an industrial scale. Less expensive metals, for example, nickel, would allow for dual functional materials to be more readily utilized in industrial settings. Nickel-based dual functional materials often do not react with the captured CO 2 and merely desorb the CO 2 rather than form a useful product. However, promoters have not been well explored for these types of materials to improve the catalytic properties, which would be beneficial to improve nickel-based materials. Herein, we report the addition of ytterbium on a nickel-based dual functional material and the improvements to the production of methane from captured CO 2 with the incorporated ytterbium promoter. The ytterbium promoter improves the selectivity of the catalysts for the hydrogenation of captured CO 2 to methane and increases the ability for the material to capture CO 2 due to additional basic sites being formed on the surface of alumina. As a result, the 12%Ni/4%Yb/6%Na 2 O/Al 2 O 3 catalyst was utilized to capture carbon dioxide and then convert the captured CO 2 to methane over five cycles, where both the amount captured and the amount converted remained stable, indicating the stability of the material over long-term use.

Catalysts↗
DOE OSTI2026

Ultrafast Reactive Laser Sintering of Highly Conductive Garnet-Type LLZTO Solid Electrolytes

Rapid and scalable fabrication of garnet-type solid electrolytes remains a major challenge for the practical deployment of lithium metal batteries. Here, we report reactive laser sintering (RLS) as an ultrafast and potentially scalable strategy for fabricating garnet-type Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) solid electrolytes. RLS of LLZTO enables simultaneous reaction and densification, achieving ∼95% relative density while minimizing lithium loss and suppressing secondary phase formation. Compared to conventional furnace sintering, RLS promotes enhanced grain growth and improved densification, leading to improved ionic conductivity (0.36 ± 0.08 mS cm −1 ) while maintaining comparable activation energies for Li + transport. Structural characterization by X-ray diffraction (XRD), Raman spectroscopy, and solid-state 6 Li/ 7 Li NMR confirms the formation of cubic garnet LLZTO with homogeneous microscale elemental distribution. In addition, nanoindentation measurements demonstrate that RLS preserves the mechanical properties of the garnet framework despite ultrafast localized thermal processing. By integrating simultaneous reaction and densification with tunable microstructural control, reactive laser sintering provides a promising manufacturing pathway for high-performance garnet solid electrolytes toward next-generation solid-state batteries.

CO2 laser↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2023

Mechanistic Insights into Cell-Free Gene Expression through an Integrated -Omics Analysis of Extract Processing Methods

Cell-free systems derived from crude cell extracts have developed into tools for gene expression, with applications in prototyping, biosensing, and protein production. Key to the development of these systems is optimization of cell extract preparation methods. However, the applied nature of these optimizations often limits investigation into the complex nature of the extracts themselves, which contain thousands of proteins and reaction networks with hundreds of metabolites. In this report we sought to uncover the black box of proteins and metabolites in Escherichia coli cell-free reactions based on different extract preparation methods. We assess changes in transcription and translation activity from σ70 promoters in extracts prepared with acetate or glutamate buffer and the common post-lysis processing steps of a runoff incubation and dialysis. We then utilize proteomic and metabolomic analyses to uncover potential mechanisms behind these changes in gene expression, highlighting the impact of cold shock-like proteins and the role of buffer composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Direct Conversion of Methane to Ethylene and Acetylene over an Iron-Based Metal–Organic Framework

Conversion of methane (CH 4 ) to ethylene (C 2 H 4 ) and/or acetylene (C 2 H 2 ) enables routes to a wide range of products directly from natural gas. However, high reaction temperatures and pressures are often required to activate and convert CH 4 controllably, and separating C 2+ products from unreacted CH 4 can be challenging. Here, we report the direct conversion of CH 4 to C 2 H 4 and C 2 H 2 driven by non-thermal plasma under ambient (25 °C and 1 atm) and flow conditions over a metal–organic framework material, MFM-300(Fe). The selectivity for the formation of C 2 H 4 and C 2 H 2 reaches 96% with a high time yield of 334 μmol g cat –1 h –1 . At a conversion of 10%, the selectivity to C 2+ hydrocarbons and time yield exceed 98% and 2056 μmol g cat –1 h –1 , respectively, representing a new benchmark for conversion of CH 4 . In situ neutron powder diffraction, inelastic neutron scattering and solid-state nuclear magnetic resonance, electron paramagnetic resonance (EPR), and diffuse reflectance infrared Fourier transform spectroscopies, coupled with modeling studies, reveal the crucial role of Fe–O(H)–Fe sites in activating CH 4 and stabilizing reaction intermediates via the formation of an Fe–O(CH 3 )–Fe adduct. In addition, a cascade fixed-bed system has been developed to achieve online separation of C 2 H 4 and C 2 H 2 from unreacted CH 4 for direct use. Integrating the processes of CH 4 activation, conversion, and product separation within one system opens a new avenue for natural gas utility, bridging the gap between fundamental studies and practical applications in this area.

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

Emergence of Diverse Failure Patterns in Weathering‐Induced Landslides: Insights From Particle Finite Element Simulations

Weathering is a fundamental driver of landslide evolution over geological timescales. Despite its ubiquity and importance, quantifying how weathering drives the progressive destabilization of rock slopes remains challenging. In this work, we develop a unified computational framework based on the particle finite element method to investigate the evolution of weathering‐induced landslides, from long‐term weathering to short‐term slope failure and runout dynamics. The framework integrates key processes, including weathering front propagation, time‐dependent strength degradation, rupture surface development, and post‐failure runout dynamics. Through numerical simulation experiments, we elucidate how interactions among weathering characteristics (type, intensity, and rate law), bedrock strength, fracture distribution, and slope geometry govern the failure modes and kinematics of weathering‐induced landslides. Simulations show that matrix‐dominated weathering leads to shallow translational failures, whereas fracture‐dominated weathering produces deep‐seated rotational and compound landslides. Pre‐existing fractures and slope morphology also strongly influence the movement of destabilized landmasses, affecting the failure pattern (e.g., kinematic mode and rupture surface geometry) and post‐failure behavior (e.g., runout velocity). We further demonstrate that the failure time and volume of weathered slopes are governed by the competition between gravitational driving forces and cohesive resisting forces during progressive destabilization. These findings provide new insights into the fundamental mechanisms that drive the emergence of diverse failure patterns of weathering‐induced landslides with important implications for landslide hazard assessment.

Wang, Liang [Eidgenoessische Technische Hochschule↗
DOE OSTI2020

Fully stretchable active-matrix organic light-emitting electrochemical cell array

Intrinsically and fully stretchable active-matrix-driven displays are an important element to skin electronics that can be applied to many emerging fields, such as wearable electronics, consumer electronics and biomedical devices. Here, we show for the first time a fully stretchable active-matrix-driven organic light-emitting electrochemical cell array. Briefly, it is comprised of a stretchable light-emitting electrochemical cell array driven by a solution-processed, vertically integrated stretchable organic thin-film transistor active-matrix, which is enabled by the development of chemically-orthogonal and intrinsically stretchable dielectric materials. Our resulting active-matrix-driven organic light-emitting electrochemical cell array can be readily bent, twisted and stretched without affecting its device performance. When mounted on skin, the array can tolerate to repeated cycles at 30% strain. This work demonstrates the feasibility of skin-applicable displays and lays the foundation for further materials development.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The surprisingly inexpensive cost of state-driven emission control strategies

Traditionally, analysis of the costs of cutting greenhouse gas emissions has assumed that governments would implement idealized, optimal policies such as uniform economy-wide carbon taxes. Yet actual policies in the real world, especially in large federal governments, are often highly heterogeneous and vary in political support and administrative capabilities within a country. While the benefits of heterogeneous action have been discussed widely for experimentation and leadership, little is known about its costs. Focusing on the United States, we represent plausible variation (by more than a factor of 3) in the stringency of state-led climate policy in a process-based integrated assessment model (GCAM-USA). For a wide array of national decarbonization targets, we find that the nationwide cost from heterogeneous subnational policies is only one-tenth higher than nationally uniform policies. Such results hinge on two critical technologies (advanced biofuels and electricity) for which inter-state trade ameliorates the economic efficiencies that might arise with heterogeneous action.

Peng, Wei↗
DOE OSTI2017

MCM-41 support for ultrasmall γ-Fe 2 O 3 nanoparticles for H 2 S removal

In this paper, MCM-41 is proposed to build mesostructured Fe 2 O 3 -based sorbents as an alternative to other silica or alumina supports for mid-temperature H 2 S removal. MCM-41 was synthesized as micrometric (MCM41_M) and nanometric (MCM41_N) particles and impregnated through an efficient two-solvent (hexane–water) procedure to obtain the corresponding γ-Fe 2 O 3 @MCM-41 composites. The active phase is homogeneously dispersed within the 2 nm channels in the form of ultrasmall maghemite nanoparticles assuring a high active phase reactivity. The final micrometric (Fe_MCM41_M) and nanometric (Fe_MCM41_N) composites were tested as sorbents for hydrogen sulphide removal at 300 °C and the results were compared with a reference sorbent (commercial unsupported ZnO) and an analogous silica-based sorbent (Fe_SBA15). MCM-41 based sorbents, having the highest surface areas, showed superior performances that were retained after the first sulphidation cycle. Specifically, the micrometric sorbent (Fe_MCM41_M) showed a higher SRC value than the nanometric one (Fe_MCM41_N), due to the low stability of the nanosized particles over time caused by their high reactivity. Finally and furthermore, the low regeneration temperature (300–350 °C), besides the high removal capacity, renders MCM41-based systems an alternative class of regenerable sorbents for thermally efficient cleaning up processes in Integrated Gasification Combined Cycles (IGCC) systems.

36 MATERIALS SCIENCE↗